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ain-blockchain | ai network blockchain ai network https ainetwork ai whitepaper https c9ede755 23ca 410d 8a9d e5b895cd95bb filesusr com ugd 4f6eb2 482a2386addb4c3283ee6e26f8ad42e6 pdf documentation https docs ainetwork ai official javascript implementation of ai network blockchain install environment last update at 6th of dec 2021 os macos 10 15 macos latest ubuntu 20 04 ubuntu latest node version v16 x v14 x tracker tracker server is required by new peers who wish to join the ain network each peer is sent the ipaddress of 2 other nodes in the network these nodes then gossip information through the network of all transactions and blocks note tracker server must be started first before starting any blockchain node instances running without docker local clone this repository and install yarn packages git clone https github com ainblockchain ain blockchain git cd ain blockchain tracker server yarn install cd run tracker server node tracker server index js you can override default port numbering system by setting port and p2p port environment variables on google coud platform gcp deploy code in common with node server set of shards to 0 if you only want to run a parent chain or set it to the specific number of shard chains you want to run in addition to the parent chain gcloud init for genesis deploy bash deploy blockchain genesis gcp sh dev staging spring summer of shards setup for incremental deploy bash deploy blockchain incremental gcp sh dev staging spring summer of shards begin parent node index end parent node index setup set up ubuntu machine if it s on a new vm bash setup blockchain ubuntu sh copy files to a sharable folder install yarn packages source setup tracker gcp sh start tracker server job cd ain blockchain bash start tracker genesis gcp sh running with docker build docker image cd tracker server docker build t ainblockchain tracker server pull docker image docker pull ainblockchain tracker server run with docker image docker run network host d ainblockchain tracker server latest client api for development and debugging tracker health check get http ip address 5000 node status check get http ip address 5000 peer nodes node operates a single peer node instance of the ain blockchain a single blockchain node instance processes incoming transaction requests and maintains a local copy of the entire blockchain the blockchain node first queries the tracker server for ip addresses of other peers and then syncs its local blockchain to the network consensus blockchain if a node is included in the whitelist and has staked appropriate amount of ain it will then take part in the consensus protocol running without docker local clone this repository and install yarn packages git clone https github com ainblockchain ain blockchain git cd ain blockchain yarn install run blockchain nodes account injection option keystore debug false stake 100000 console log true enable gas fee workaround true node client index js account injection option keystore debug false stake 100000 console log true enable gas fee workaround true node client index js account injection option keystore debug false stake 100000 console log true enable gas fee workaround true node client index js you can override default port numbering system by setting port and p2p port environment variables before starting node jobs remove existing blockchain files and logs if necessary rm rf path to data dir logs the default blockchain data directory is ain blockchain data e g chain data will be at ain blockchain data chains you can use a different directory by specifying the blockchain data dir environment variable the default minimum size of the validator whitelist is 3 change min num validators parameter in the blockchain configs base genesis json to change this value you may also need to modify the genesis whitelist and genesis validators accordingly the genesis configs directory used is blockchain configs base by default and it can be altered using blockchain configs dir env variable for example afan shard cluster can use the following command line blockchain configs dir blockchain configs afan shard min num validators 1 debug false stake 100000 console log true enable gas fee workaround true node client index js on google cloud platform gcp deploy code in common with tracker server set of shards to 0 if you only want to run a parent chain or set it to the specific number of shard chains you want to run in addition to the parent chain gcloud init for genesis deploy bash deploy blockchain genesis gcp sh dev staging spring summer of shards setup for incremental deploy bash deploy blockchain incremental gcp sh dev staging spring summer of shards begin parent node index end parent node index setup set up ubuntu machine if it s on a new vm bash setup blockchain ubuntu sh copy files to a sharable folder install yarn packages source setup node gcp sh start node server job set shard index to 0 if you re running a root chain node bash start node genesis gcp sh dev spring summer shard index server index running with docker pull docker image from docker hub https hub docker com repository docker ainblockchain ain blockchain docker pull ainblockchain ain blockchain latest docker pull ainblockchain ain blockchain package version or build docker image yourself docker build t ain blockchain run with docker image example docker run e account injection option private key e sync mode peer e stake 10000 e season dev network host d ainblockchain ain blockchain latest docker run e account injection option keystore e sync mode peer e stake 10000 e season mainnet network host d ainblockchain ain blockchain latest you can use some environment variables and these have the following options e season mainnet summer spring sandbox staging exp dev e account injection option private key keystore mnemonic e sync mode fast full peer e stake your target stake e hosting env local comcom gcp aws you can mount a volume when you meet some wants 1 want to preserve blockchain data in the docker container when changing the docker image due to an update 2 want to save your keystore file in the docker container when injecting your account into the blockchain automatically v your volume home ain blockchain data after the node is executed you should inject your account into the node node inject node account js node endpoint url private key node inject node account js node endpoint url keystore node inject node account js node endpoint url mnemonic if you want to inject your account automatically add one of these environment variables before running the node e account injection option private key e private key your private key e account injection option keystore e keystore file path home ain blockchain data keystore e password your password e account injection option mnemonic e mnemonic your mnemonic enter docker container and inspect blockchain files docker exec it container id bin bash cd blockchain blockchains 8080 enter docker container and inspect log files docker exec it container id bin bash cat logger logs 8080 log file how to run tests how to run unit test and integration test all around yarn run test unit yarn run test integration some individual tests already definded in the package json yarn run test chain util yarn run test state util yarn run test block pool yarn run test db yarn run test node yarn run test blockchain yarn run test dapp yarn run test sharding the load test is also supported yarn run loadtest client api for development and debugging node health check get http ip address 8080 fetch latest blocks in the blockchain up to 20 blocks get http ip address 8080 blocks fetch specific list of blocks from the blockchain get http ip address 8080 blocks from 1 to 100 fetch transactions in the transaction pool get http ip address 8080 tx pool fetch transaction status in the transaction tracker get http ip address 8080 tx tracker fetch nonce status in the committed nonce tracker get http ip address 8080 committed nonce tracker fetch nonce status in the pending nonce tracker get http ip address 8080 pending nonce tracker fetch value get http ip address 8080 get value ref db path to fetch fetch rule get http ip address 8080 get rule ref db path to fetch fetch function get http ip address 8080 get function ref db path to fetch fetch owner get http ip address 8080 get owner ref db path to fetch match rule with database value location get http ip address 8080 match rule ref db path to match match function with database value location get http ip address 8080 match function ref db path to match match owner with database rule function owner location get http ip address 8080 match owner ref db path to match evaluate rule post http ip address 8080 eval rule with json body ref db path to eval value some value address 0xabcd z timestamp 1234567890 evaluate owner post http ip address 8080 eval owner with json body ref db path to eval permission write rule address 0xabcd z perform multiple get operations post http ip address 8080 get with json body op list type get value ref db path to fetch type get rule ref db path to fetch2 set value post http ip address 8080 set value with json body ref db path to set value some value increase value post http ip address 8080 inc value with json body ref db path to increase value 10 decrease value post http ip address 8080 dec value with json body ref db path to decrease value 10 set rule post http ip address 8080 set rule with json body ref db path to set value some rule config set function post http ip address 8080 set function with json body ref db path to set value some function config set owner post http ip address 8080 set owner with json body ref db path to set value some owner config perform multiple set operations post http ip address 8080 set with json body op list type set value ref db path to set value some value type set rule ref db path to set2 value some rule perform multiple transactions post http ip address 8080 batch with json body tx list operation type set value ref db path to set value testme operation type set rule ref db path to set2 value some rule utility scripts four node server with a tracker server can be started all at once using start local blockchain sh like bash start local blockchain sh and can be stopped all at once using stop local blockchain sh like bash stop local blockchain sh versions please check the latest versions below api version release https github com ainblockchain ain blockchain releases data protocol version readme md p2p readme md consensus protocol version readme md consensus readme md contribution please read the contributing md contributing md for details on our code of conduct and the process for submitting pull requests to us license this project is licensed under the mit license see the license license file for details | blockchain |
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wee-os | thanks for checking out the best readme template if you have a suggestion that would make this better please fork the repo and create a pull request or simply open an issue with the tag enhancement thanks again now go create something amazing d to avoid retyping too much info do a search and replace for the following puranjaymohan wee os twitter handle puranjay12 gmail com weeos a tiny rtos for arm cortex m3 m4 based microcontrollers project shields i m using markdown reference style links for readability reference links are enclosed in brackets instead of parentheses see the bottom of this document for the declaration of the reference variables for contributors url forks url etc this is an optional concise syntax you may use https www markdownguide org basic syntax reference style links contributors contributors shield contributors url forks forks shield forks url stargazers stars shield stars url issues issues shield issues url mit license license shield license url linkedin linkedin shield linkedin url build status travis ci shield travis ci url lines of code lines of code shield lines of code url github release latest by date release shield release url project logo br p align center a href https github com puranjaymohan wee os img src images logo png alt logo width 350 height 245 a p align center a tiny rtos for arm cortex m3 m4 based microcontrollers br a href https github com puranjaymohan wee os blob master apidoc md strong explore the docs strong a br br a href https github com puranjaymohan wee os view demo a a href https github com puranjaymohan wee os issues report bug a a href https github com puranjaymohan wee os issues request feature a p p table of contents details open open summary h2 style display inline block table of contents h2 summary ol li a href about the project about the project a ul li a href supported hardware supported hardware a li li a href built with built with a li ul li li a href getting started getting started a ul li a href prerequisites prerequisites a li li a href building weeos building weeos a li ul li li a href usage usage a ul li a href source code source code a li li a href using it in your project using it in your project a li ul li li a href roadmap roadmap a li li a href contributing contributing a li li a href license license a li li a href contact contact a li li a href acknowledgements acknowledgements a li ol details about the project about the project weeos is a tiny rtos for arm cortex m3 m4 processors it currently supports two scheduling algoriths and four hardware devices it includes a pre emptive scheduler that supports round robin and weighted round robin scheduling algorithms weeos if fully configurable the scheduling algorithm and the hardware device can be set in the makefile supported hardware st nucleo l476rg https www st com en evaluation tools nucleo l476rg html ti ek tm4c123gxl https www ti com tool ek tm4c123gxl qemu lm3s811evb https wiki qemu org documentation platforms arm nxp mk64f12 https www nxp com products processors and microcontrollers arm microcontrollers general purpose mcus k series cortex m4 k6x ethernet kinetis k64 120 mhz 256 kb sram microcontrollers mcus based on arm cortex m4 core k64 120 built with gnu arm embedded toolchain https developer arm com tools and software open source software developer tools gnu toolchain gnu rm downloads cmsis https www arm com why arm technologies cmsis openocd http openocd org gnu make https www gnu org software make getting started getting started to get a local copy up and running follow these simple steps prerequisites install the prerequisites here is how to do it on arch linux gnu arm embedded toolchain sh sudo pacman s arm none eabi gcc openocd sh sudo pacman s openocd packages for building this is different for every distro it includes things like gnu make sh sudo pacman s base devel building weeos 1 clone the repo sh git clone https github com puranjaymohan wee os git 2 get inside the wee os directory sh cd wee os 3 generate the config mk file sh chmod x configure sh configure sh 4 edit config mk to choose your hardware and scheduling algorithm sh weeos configuration file please edit this file to configure weeos name of the generated elf file project main device for which the project has to be made available devices stm32l476rg lm3s811 mk64f12 tm4c123gh6pm device stm32l476rg scheduling algorithm used in the app available scheduling algorithms round robin weighted round robin schd alg round robin 5 run make to build the final elf file sh make 6 run openocd in another terminal for your hardware sh openocd f usr share openocd scripts board st nucleo l476rg cfg 7 run gdb and upload the elf file to the target sh arm none eabi gdb build main elf x scripts gdb commands usage examples usage source code the repository is divided into 3 main sub directories src device and scripts the src directory includes two directories app and kernel the app directory has the main c file and all other supporting files required for the user s application the kernel directory has all the weeos files the device directory has hardware specific filed like linker scripts etc the scripts directory has some useful scripts sh device lm3s811 mk64f12 stm32l476rg tm4c123gh6pm images logo png license makefile readme md scripts checkpatch pl gdb commands src app kernel using it in your project to use weeos in your project just setup weeos using the steps above add your code to src app you can add c h or s files in the src app folder and they will be compiled assembled and linked automatically while running make to create add kill tasks or use any other weeos feature include the include weeos h in your source file to understand the usage of the weeos api have a look at api documentation api documentation https github com puranjaymohan wee os blob master apidoc md roadmap roadmap see the open issues https github com puranjaymohan wee os issues for a list of proposed features and known issues contributing contributing contributions are what make the open source community such an amazing place to be learn inspire and create any contributions you make are greatly appreciated 1 fork the project 2 create your feature branch git checkout b feature amazingfeature 3 commit your changes git commit m add some amazingfeature 4 push to the branch git push origin feature amazingfeature 5 open a pull request license license distributed under the mit license see license for more information contact contact puranjay mohan puranjay12 gmail com abhay chirania abhaychirania2411 gmail com project link https github com puranjaymohan wee os https github com puranjaymohan wee os acknowledgements acknowledgements operating systems lectures iitd http www cse iitd ernet in os lectures build your own realtime os rtos from ground up on arm 1 https www udemy com course rtos building from ground up on arm processors markdown links images https www markdownguide org basic syntax reference style links contributors shield https img shields io github contributors puranjaymohan wee os svg style for the badge contributors url https github com puranjaymohan wee os graphs contributors forks shield https img shields io github forks puranjaymohan wee os svg style for the badge forks url https github com puranjaymohan wee os network members stars shield https img shields io github stars puranjaymohan wee os svg style for the badge stars url https github com puranjaymohan wee os stargazers issues shield https img shields io github issues puranjaymohan wee os svg style for the badge issues url https github com puranjaymohan wee os issues license shield https img shields io github license puranjaymohan wee os svg style for the badge license url https github com puranjaymohan wee os blob master license linkedin shield https img shields io badge linkedin black svg style for the badge logo linkedin colorb 555 linkedin url https linkedin com in puranjaymohan travis ci shield https img shields io travis com puranjaymohan wee os style for the badge travis ci url https travis ci com puranjaymohan wee os lines of code shield https img shields io tokei lines github puranjaymohan wee os style for the badge lines of code url https github com puranjaymohan wee os release shield https img shields io github v release puranjaymohan wee os style for the badge release url https github com puranjaymohan wee os releases | hacktoberfest operating-system arm embedded-systems kernel | os |
Baichuan-13B | markdownlint disable first line h1 markdownlint disable html div align center h1 baichuan 13b h1 div p align center a href https huggingface co baichuan inc baichuan 13b base target blank baichuan 13b base a a href https huggingface co baichuan inc baichuan 13b chat target blank baichuan 13b chat a a href https modelscope cn organization baichuan inc target blank modelscope a a href https github com baichuan inc baichuan 13b blob main media wechat jpeg raw true target blank wechat a p div align center license https img shields io github license modelscope modelscope svg https github com baichuan inc baichuan 13b blob main license h4 align center p b b a href https github com baichuan inc baichuan 13b blob main readme en md english a p h4 div 2023 09 06 baichuan 2 https github com baichuan inc baichuan2 7b 13b 2023 08 01 baichuan 13b chat https huggingface co baichuan inc baichuan 13b chat benchmark benchmark baichuan 13b baichuan 7b https github com baichuan inc baichuan 7b 130 benchmark baichuan 13b base https huggingface co baichuan inc baichuan 13b base baichuan 13b chat https huggingface co baichuan inc baichuan 13b chat baichuan 13b 1 baichuan 13b baichuan 7b https github com baichuan inc baichuan 7b 130 1 4 tokens llama 13b 40 13b alibi 4096 2 baichuan 13b chat 3 int8 int4 nvidia 3090 4 baichuan 13b benchmark benchmark 5 shot c eval https cevalbenchmark com index html home model 5 shot stem social sciences humanities others average baichuan 7b 38 2 52 0 46 2 39 3 42 8 chinese alpaca plus 13b 35 2 45 6 40 0 38 2 38 8 vicuna 13b 30 5 38 2 32 5 32 5 32 8 chinese llama plus 13b 30 3 38 0 32 9 29 1 32 1 ziya llama 13b pretrain 27 6 34 4 32 0 28 6 30 0 llama 13b 27 0 33 6 27 7 27 6 28 5 moss moon 003 base 16b 27 0 29 1 27 2 26 9 27 4 baichuan 13b base 45 9 63 5 57 2 49 3 52 4 baichuan 13b chat 43 7 64 6 56 2 49 2 51 5 mmlu https arxiv org abs 2009 03300 model 5 shot stem social sciences humanities others average vicuna 13b 40 4 60 5 49 5 58 4 52 0 llama 13b 36 1 53 0 44 0 52 8 46 3 chinese alpaca plus 13b 36 9 48 9 40 5 50 5 43 9 ziya llama 13b pretrain 35 6 47 6 40 1 49 4 42 9 baichuan 7b 35 6 48 9 38 4 48 1 42 3 chinese llama plus 13b 33 1 42 8 37 0 44 6 39 2 moss moon 003 base 16b 22 4 22 8 24 2 24 4 23 6 baichuan 13b base 41 6 60 9 47 4 58 5 51 6 baichuan 13b chat 40 9 60 9 48 8 59 0 52 1 mmlu https github com hendrycks test cmmlu https github com haonan li cmmlu model 5 shot stem humanities social sciences others china specific average baichuan 7b 34 4 47 5 47 6 46 6 44 3 44 0 vicuna 13b 31 8 36 2 37 6 39 5 34 3 36 3 chinese alpaca plus 13b 29 8 33 4 33 2 37 9 32 1 33 4 chinese llama plus 13b 28 1 33 1 35 4 35 1 33 5 33 0 ziya llama 13b pretrain 29 0 30 7 33 8 34 4 31 9 32 1 llama 13b 29 2 30 8 31 6 33 0 30 5 31 2 moss moon 003 base 16b 27 2 30 4 28 8 32 6 28 7 29 6 baichuan 13b base 41 7 61 1 59 8 59 0 56 4 55 3 baichuan 13b chat 42 8 62 6 59 7 59 0 56 1 55 8 cmmlu https github com haonan li cmmlu tokens baichuan 7b 4 096 32 32 64 000 7 000 559 616 1 2 rope https arxiv org abs 2104 09864 4 096 baichuan 13b 5 120 40 40 64 000 13 264 901 120 1 4 alibi https arxiv org abs 2108 12409 4 096 hugging face baichuan 13b base https huggingface co baichuan inc baichuan 13b base baichuan 13b chat https huggingface co baichuan inc baichuan 13b chat baichuan 13b chat hugging face shell pip install r requirements txt python python import torch from transformers import automodelforcausallm autotokenizer from transformers generation utils import generationconfig tokenizer autotokenizer from pretrained baichuan inc baichuan 13b chat use fast false trust remote code true model automodelforcausallm from pretrained baichuan inc baichuan 13b chat device map auto torch dtype torch float16 trust remote code true model generation config generationconfig from pretrained baichuan inc baichuan 13b chat messages messages append role user content response model chat tokenizer messages print response k2 8611 device map auto export cuda visible devices 0 1 0 1 shell python cli demo py p align center img src media cn cli png width 70 p demo streamlit web shell streamlit run web demo py p align center img src media cn web gif width 70 p baichuan 13b chat details summary b b summary baichun 13b chat 1 2 3 4 5 6 7 details details summary b b summary baichun 13b chat baichun 13b chat 1 2019 7355 4 6 2023 8742 2 1 2019 1 020 10 2023 1 500 2 70 20 10 1 2 3 1 2 3 4 details details summary b b summary baichun 13b chat k2 8611 baichun 13b chat reward shaping baichun 13b chat reward shaping agent 1 2 3 4 details details summary b b summary baichun 13b chat 1 2 3 details baichuan 13b alibi rotary embedding llama 13b tokens s 31 6 model tokens s llama 13b 19 4 baichuan 13b 25 4 gpu a100 sxm4 80g pytorch 2 0 0 cu117 transformers 4 29 1 batch size 1 2048 fp16 baichuan 13b base baichuan 13b int8 int4 cpu from pretrained device map auto gpu int8 python model automodelforcausallm from pretrained baichuan inc baichuan 13b chat torch dtype torch float16 trust remote code true model model quantize 8 cuda int4 python model automodelforcausallm from pretrained baichuan inc baichuan 13b chat torch dtype torch float16 trust remote code true model model quantize 4 cuda quantize int8 chat baichuan 13b chat int8 https huggingface co baichuan inc baichuan 13b chat int8 python model automodelforcausallm from pretrained baichuan inc baichuan 13b chat int8 torch dtype torch float16 trust remote code true cuda precision gpu mem gb bf16 fp16 26 0 int8 15 8 int4 9 7 benchmark model 5 shot c eval mmlu cmmlu baichuan 13b base 52 4 51 6 55 3 baichuan 13b base int8 51 2 49 9 54 5 baichuan 13b base int4 47 6 46 0 51 0 cpu baichuan 13b cpu cpu python model automodelforcausallm from pretrained baichuan inc baichuan 13b chat torch dtype torch float32 trust remote code true cpu 60gb baichuan 13b base baichuan 13b chat baichuan 13b llama efficient tuning https github com hiyouga llama efficient tuning lora llama efficient tuning https github com hiyouga llama efficient tuning getting started data json dataset json instruction what are the three primary colors input output the three primary colors are red blue and yellow json sample instruction input instruction input n output 8 nvidia a100 80 gb deepspeed shell deepspeed num gpus 8 src train bash py stage sft model name or path baichuan inc baichuan 13b base do train dataset alpaca gpt4 en alpaca gpt4 zh finetuning type full output dir path to your sft checkpoint overwrite cache per device train batch size 4 per device eval batch size 4 gradient accumulation steps 8 preprocessing num workers 16 lr scheduler type cosine logging steps 10 save steps 100 eval steps 100 learning rate 5e 5 max grad norm 0 5 num train epochs 2 0 dev ratio 0 01 evaluation strategy steps load best model at end plot loss fp16 deepspeed deepspeed json deep speed json json train micro batch size per gpu auto zero allow untested optimizer true fp16 enabled auto loss scale 0 initial scale power 16 loss scale window 1000 hysteresis 2 min loss scale 1 zero optimization stage 2 allgather partitions true allgather bucket size 5e8 overlap comm false reduce scatter true reduce bucket size 5e8 contiguous gradients true lora nvidia a100 80g lora shell cuda visible devices 0 python src train bash py stage sft model name or path baichuan inc baichuan 13b base do train dataset alpaca gpt4 en alpaca gpt4 zh finetuning type lora lora rank 8 lora target w pack output dir path to your sft checkpoint overwrite cache per device train batch size 4 per device eval batch size 4 gradient accumulation steps 8 preprocessing num workers 16 lr scheduler type cosine logging steps 10 save steps 100 eval steps 100 learning rate 5e 5 max grad norm 0 5 num train epochs 2 0 dev ratio 0 01 evaluation strategy steps load best model at end plot loss fp16 llama efficient tuning baichuan 13b ios android baichuan 13b baichuan 13b baichuan 13b apache 2 0 https github com baichuan inc baichuan 13b blob main license baichuan 13b baichuan 13b https huggingface co baichuan inc baichuan 13b chat resolve main baichuan 13b 20 e6 a8 a1 e5 9e 8b e7 a4 be e5 8c ba e8 ae b8 e5 8f af e5 8d 8f e8 ae ae pdf baichuan 13b baichuan 13b opensource baichuan inc com | artificial-intelligence chatgpt chinese gpt-4 huggingface large-language-models natural-language-processing benchmark ceval mmlu | ai |
Visual-Adversarial-Examples-Jailbreak-Large-Language-Models | h1 align center style text align center font weight bold font size 2 0em letter spacing 2 0px visual adversarial examples jailbreak br aligned large language models h1 p align center style text align center font size 1 25em a href https unispac github io target blank style text decoration none xiangyu qi sup 1 sup a nbsp nbsp a href https hackyhuang github io target blank style text decoration none kaixuan huang sup 1 sup a nbsp nbsp a href https scholar google com citations user rfc3l6yaaaaj hl en target blank style text decoration none ashwinee panda sup 1 sup a br a href https www peterhenderson co target blank style text decoration none peter henderson sup 2 sup a nbsp nbsp a href https mwang princeton edu target blank style text decoration none mengdi wang sup 1 sup a nbsp nbsp a href https www princeton edu pmittal target blank style text decoration none prateek mittal sup 1 sup a nbsp nbsp br sup sup equal contribution br princeton university sup 1 sup nbsp nbsp nbsp nbsp stanford university sup 2 sup br p p align center b em arxiv preprint 2023 em br b p p align center style text align center font size 2 5 em b a href https arxiv org abs 2306 13213 target blank style text decoration none arxiv a nbsp b p warning this repository contains prompts model behaviors and training data that are offensive in nature br br assets attack png overview a single visual adversarial example can jailbreak minigpt 4 note 1 for each instruction below we ve sampled 100 random outputs calculating the refusal and obedience ratios via manual inspection a representative redacted output is showcased for each 2 we use 16 255 in the following demo assets human race png assets gender png assets race png minigpt 4 can refuse harmful instructions with a non trivial probability see the green boxes but we find that the aligned behaviors can falter significantly when prompted with a visual adversarial input see the red boxes in the above example we optimize the adversarial example x on a small manually curated corpus comprised of derogatory content against a certain gender 1 an ethnic race 1 and the human race to directly maximize the model s probability of generating such content though the scope of the corpus is very narrow surprisingly a single such adversarial example can enable the model to heed a wide range of harmful instructions and produce harmful content far beyond merely imitating the derogatory corpus see the following examples used in the optimization assets religious 1 png assets religious 2 png assets crime png intriguingly x also facilitates the generation of offensive content against other social groups religious group 1 religious group 2 and even instructions for murder which were not explicitly optimized for in folder adversarial images we provide our sample adversarial images under different distortion constraints the effectiveness of our adversarial examples can be verified by using the minigpt 4 interface running in the huggingface space https huggingface co spaces vision cair minigpt4 https huggingface co spaces vision cair minigpt4 br br step by step instructions for reimplementing our experiments on minigpt 4 note a single a100 80g gpu is sufficient to launch the following experiments br installation we take minigpt 4 13b as the sandbox to showcase our attacks the following installation instructions are adapted from the minigpt 4 repository https github com vision cair minigpt 4 1 set up the environment bash git clone https github com unispac visual adversarial examples jailbreak large language models git cd visual adversarial examples jailbreak large language models conda env create f environment yml conda activate minigpt4 2 prepare the pretrained weights for minigpt 4 as we directly inherit the minigpt 4 code base the guide from the minigpt 4 repository https github com vision cair minigpt 4 tree main can also be directly used to get all the weights get vicuna minigpt 4 13b is built on the v0 version of vicuna 13b https lmsys org blog 2023 03 30 vicuna please refer to this guide https github com vision cair minigpt 4 blob main preparevicuna md from the minigpt 4 repository to get the weights of vicuna then set the path to the vicuna weight in the model config file here https github com unispac visual adversarial examples jailbreak large language models blob main minigpt4 configs models minigpt4 yaml l16 at line 16 get minigpt 4 the 13b version checkpoint download from here https drive google com file d 1a4zlvaidbr 36pasffmgpvh5p7ckmpze view usp share link then set the path to the pretrained checkpoint in the evaluation config file in eval configs minigpt4 eval yaml https github com unispac visual adversarial examples jailbreak large language models blob main eval configs minigpt4 eval yaml l11 at line 11 br generate visual adversarial examples generate a visual adversarial example within a distortion constraint of epsilon 16 255 similar to the example in our overview demo the final adversarial examples will be saved to save dir bad prompt bmp and we also save intermediate checkpoints every 100 iterations the argument can be adjusted e g eps 32 64 128 to evaluate the effectiveness of the attacks under different distortion budgets bash python minigpt visual attack py cfg path eval configs minigpt4 eval yaml gpu id 0 n iters 5000 constrained eps 16 alpha 1 save dir visual constrained eps 16 when there is no need for visual stealthiness one can use the following command to run unconstrained attacks the adversarial image can take any values within the legitimate range of pixel values bash python minigpt visual attack py cfg path eval configs minigpt4 eval yaml gpu id 0 n iters 5000 alpha 1 save dir visual unconstrained br evaluation in folder adversarial images we provide off the shelf adversarial images that we generated under different distortion constraints to verify the effectiveness of our adversarial examples play with the web based interface of minigpt 4 huggingface space https huggingface co spaces vision cair minigpt4 launch a local demo bash python demo py cfg path eval configs minigpt4 eval yaml gpu id 0 testing on a diverse set of 40 manually curated harmful instructions warning this will involve materials that are offensive in nature bash python minigpt test manual prompts visual llm py cfg path eval configs minigpt4 eval yaml gpu id 0 image path adversarial images prompt unconstrained bmp the argument image path can be customized to the path of any input image testing on the realtoxicityprompts dataset download the realtoxicityprompts dataset https allenai org data real toxicity prompts and copy soft link the dataset file to rtp prompts jsonl the inference py will read the dataset filter out prompts with challenging true and ask the model to generate the continuation for each prompt bash python minigpt inference py cfg path eval configs minigpt4 eval yaml gpu id 0 image file adversarial images prompt unconstrained bmp output file result jsonl the argument image file can be customized to the path of any input image output file indicates where to save the generations request and place your perspective api https perspectiveapi com key in perspective api key the get metric py will calculate the toxic scores using both perspective api and detoxify https github com unitaryai detoxify bash python get metric py input result jsonl output result eval jsonl br generate textual adversarial examples we also provide codes for optimizing adversarial text tokens w r t the same attack targets as our visual attacks a running example bash python minigpt textual attack py cfg path eval configs minigpt4 eval yaml gpu id 0 n iters 5000 n candidates 50 save dir textual unconstrained br br attacks on other models we also implement our attacks on two other open sourced vlms including instructblip https github com salesforce lavis tree main projects instructblip and llava https github com haotian liu llava to launch experiments on these models we suggest users to create a separate conda environment for each model and install dependencies following the instructions of the original repositories of these models instruct blip set up the environment and prepare model checkpoints following the instructions of the original repository at https github com salesforce lavis tree main projects instructblip choose the 13b version model built upon vicuna13b i e blip2 vicuna instruct vicuna13b download the version 1 1 model of vicuna 13b to the directory ckpts vicuna 13b v1 1 attack bash python u instructblip visual attack py n iters 5000 constrained save dir results blip constrained 16 eps 16 alpha 1 python u instructblip visual attack py n iters 5000 constrained save dir results blip constrained 32 eps 32 alpha 1 python u instructblip visual attack py n iters 5000 constrained save dir results blip constrained 64 eps 64 alpha 1 python u instructblip visual attack py n iters 5000 save dir results blip unconstrained alpha 1 testing on the realtoxicityprompts dataset make inference on the dataset bash python instructblip inference py image file path to the adversarial example output file result jsonl the get metric py will calculate the toxic scores using both perspective api and detoxify https github com unitaryai detoxify bash python get metric py input result jsonl output result eval jsonl then you can run cal metrics py to summarize the evaluation results from the two evaluation bash python cal metrics py input result eval jsonl llava llama 2 set up the environment following the instructions of the original repository at https github com haotian liu llava after get the model save it to the path ckpts llava llama 2 13b chat freeze attack bash python u llava llama v2 visual attack py n iters 5000 constrained save dir results llava llama v2 constrained 16 eps 16 alpha 1 python u llava llama v2 visual attack py n iters 5000 constrained save dir results llava llama v2 constrained 32 eps 32 alpha 1 python u llava llama v2 visual attack py n iters 5000 constrained save dir results llava llama v2 constrained 64 eps 64 alpha 1 python u llava llama v2 visual attack py n iters 5000 save dir results llava llama v2 unconstrained alpha 1 testing on the realtoxicityprompts dataset make inference on the dataset bash python u llava llama v2 inference py image file path to the adversarial example output file result jsonl the get metric py will calculate the toxic scores using both perspective api and detoxify https github com unitaryai detoxify bash python get metric py input result jsonl output result eval jsonl then you can run cal metrics py to summarize the evaluation results from the two evaluation bash python cal metrics py input result eval jsonl br br citation if you find this useful in your research please consider citing misc qi2023visual title visual adversarial examples jailbreak aligned large language models author xiangyu qi and kaixuan huang and ashwinee panda and peter henderson and mengdi wang and prateek mittal year 2023 eprint 2306 13213 archiveprefix arxiv primaryclass cs cr | ai |
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OBDintheCloud | obdinthecloud android application for connecting to dg technologies gryphon and downloading stored log files application will also upload data downloaded onto android device to google cloud storage platform implemented functionality downloading log files from the gryphon viewing log files downloaded from the gryphon limited capacity uploading downloaded and parsed files to google cloud storage additional features for the future add the ability to download data from the gryphon in realtime add the ability to upload data to the cloud in near realtime add the ability to download through wifi and upload through hspa simultaneously | cloud |
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Azubi | azubi cloud engineering | cloud |
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EuromessageJavaV2 | euromessagejavav2 mobile e comm development app on magento for integrations of euromessage and visilabs therefore we are able to send fcm firebase cloud messaging based push notifications | front_end |
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ioBroker.iot | logo admin iot png iobroker iot adapter number of installations http iobroker live badges iot installed svg number of installations http iobroker live badges iot stable svg npm version http img shields io npm v iobroker iot svg https www npmjs com package iobroker iot test and release https github com iobroker iobroker iot workflows test 20and 20release badge svg translation status https weblate iobroker net widgets adapters iot svg badge svg https weblate iobroker net engage adapters utm source widget downloads https img shields io npm dm iobroker iot svg https www npmjs com package iobroker iot this adapter is only for communication with amazon alexa google home and nightscout it is not for remote access to your iobroker instance use iobroker cloud adapter for that this adapter uses sentry libraries to automatically report exceptions and code errors to the developers for more details and for information how to disable the error reporting see sentry plugin documentation https github com iobroker plugin sentry plugin sentry sentry reporting is used starting with js controller 3 0 settings to use the iot adapter you should first register on the iobroker cloud https iobroker pro https iobroker pro reference to google api type settings https developers google com actions smarthome guides intro img intro png language if you select default language the smart names of devices and of enumerations will not be translated if some language specified all known names will be translated into this language it is done to switch fast between many languages for demonstration purposes place function in names first change the order of function and roles in self generated names if false room function e g living room dimmer if true function room e g dimmer living room concatenate words with you can define the word which will be placed between function and room e g in and from dimmer living room will be dimmer in living room but it is not suggested doing so because recognition engine must analyze one more word and it can lead to misunderstandings off level for switches some groups consist of mixed devices dimmers and switches it is allowed to control them with on and off commands and with percents if command is set to 30 and the off level is 30 so the switches will be turned on by command set to 25 all switches will be turned off additionally if the command is off so the adapter will remember the current dimmer level if the actual value is over or equal to the 30 later when the new on command will come the adapter will switch the dimmer not to 100 but to the level in memory example assume that off level is 30 virtual device light has two physical devices switch and dimmer command set the light to 40 the adapter will remember this value for dimmer will set it for dimmer and will turn the switch on command turn the light off the adapter will set the dimmer to 0 and will turn off the switch command turn on the light dimmer 40 switch on command set the light to 20 dimmer 20 switch off the value for dimmer will not be remembered because it is bellow off level command turn on the light dimmer 40 switch on by on you can select the behavior of on command will come for the number state the specific value can be selected or the last non zero value will be used write response to for every command the text response will be generated you can define here the object id where this text must be written to e g sayit 0 tts text colors the channel needs 3 5 states with the following roles level color saturation required for detection of the channel level color hue level dimmer switch optional level color temperature optional alexa set the device name to color alexa turn the light fuchsia alexa set the bedroom light to red alexa change the kitchen to the color chocolate lock to have the possibility to lock the locks the state must have the role switch lock and have native lock value to determine the lock state if you need a separate value to control the lock you can use native control value alexa is lock name locked unlocked alexa lock the lock name how names will be generated the adapter tries to generate virtual devices for smart home control e g amazon alexa or google home there are two important enumerations for that rooms and functions rooms are like living room bathroom sleeping room functions are like light blind heating the following conditions must be met to get the state in the automatically generated list the state must be in some function enumeration the state must have role state switch or level e g level dimmer if not directly included in functions it can be that the channel is in the functions but state itself not the state must be writable common write true the state dimmer must have common type as number the state heating must have common unit as c f or k and common type as number if the state is only in functions and not in any room the name of state will be used the state names will be generated from function and room e g all lights in the living room will be collected in the virtual device living room light the user cannot change this name because it is generated automatically but if the enumeration name changes this name will be changed too e g function light changed to lights so the living room light will be changed to living room lights all the rules will be ignored if the state has common smartname in this case just the smart name will be used if common smartname is false the state or enumeration will not be included in the list generation the configuration dialog lets the comfortable remove and add the single states to virtual groups or as single device configuration img configuration png if the group has only one state it can be renamed as for this the state s smartname will be used if the group has more than one state the group must be renamed via the enumeration s names to create own groups the user can install scenes adapter or create script in javascript adapter replaces you can specify strings that could be automatically replaced in the device names e g if you set replaces to state level so all state and level will be deleted from names be careful with spaces if you set state level so state and level will be replaced and not level helper states smart lastobjectid this state will be set if only one device was controlled by home skill alexa google home smart lastfunction function name if exists for which last command was executed smart lastroom room name if exists for which last command was executed smart lastcommand last executed command command can be true on false off number x decrease at x x increase at x smart lastresponse textual response on command it can be sent to some text2speech sayit engine toggle mode alexa v3 supports toggle mode it means that if you say alexa turn on the light and the light is already on it will be turned off ifttt instructions doc ifttt md google home if you see the following error message in the log ghome invalid url pro key status auto update is disabled you can set states but receive states only manually so you must generate the url key anew url key img url key png services there is a possibility to send messages to cloud adapter if you call post https service iobroker in v1 iotservice service custom name key xxx user user email und value as payload curl data mystring https service iobroker in v1 iotservice service custom name key xxx user user email or get https service iobroker in v1 iotservice service custom name key xxx user user email data mystring if you set in the settings the field teh white list for services the name custom test and call with custom test as the service name the state cloud 0 services custom test will be set to mystring you may write in the white list and all services will be allowed here you can find instructions on how to use it with tasker doc tasker md ifttt service is allowed only if an ifttt key is set reserved names are ifttt text2command simpleapi swagger these must be used without the custom prefix you can ask by message the valid url for service too sendto iot 0 getserviceendpoint servicename custom myservice result console log json stringify result output result url https service iobroker in v1 iotservice key xxx user uuu service custom myservice stateid iot 0 services myservice warning service name is not in white list text2command you may write text2command in white list you can send post request to https service iobroker in v1 iotservice service text2command key user app key user user email to write data into text2command x text variable you can use get method too https service iobroker in v1 iotservice service text2command key user app key user user email data my command x can be defined in settings by the use text2command instance option custom skill the answers for the custom skill can be processed in two ways text2command javascript text2command if text2command instance is defined in the configuration dialog so the question will be sent to the instance text2command must be configured that the expected phrase will be parsed and the answer will be given back javascript there is a possibility to process the question directly with the script it is activated by default if no text2command instance is selected if text2command instance is defined so this instance must provide the answer and the answer from script will be ignored the adapter will provide the details in two states with different detail level smart lastcommand contains the received text including info about the type of query intent example askdevice status rasenm her smart lastcommandobj contains an json string that can be parsed to an object containing the following information words contain the received words in an array intent contains the type of query possible values currently are v1 skill askdevice controldevice actionstart actionend askwhen askwhere askwho v2 skill queryintent when the full said text was captured controldevice for fallback with only partial text deviceid contains a deviceid identifying the device the request was sent to delivered by amazon will be empty string if not provided deviceroom contains a mapped room identifier you can configure in iot admin ui for collected deviceids sessionid contains a sessionid of the skill session should be the same if multiple commands were spoken delivered by amazon will be empty string if not provided userid contains a userid from the device owner or maybe later the user that was interacting with the skill delivered by amazon will be empty string if not provided username contains a mapped username you can configure in iot admin ui for collected userids more details on how the words are detected and what type of queries the alexa custom skill differentiates please check https forum iobroker net viewtopic php f 37 t 17452 return result via smart lastresponse state the response needs to be sent within 200ms in the state smart lastresponse and can be a simple text string or a json object if it is a text string then this text will be sent as a response to the skill if the text is a json object then the following keys can be used responsetext needs to contain the text to return to amazon shouldendsession is a boolean and controls if the session is closed after the response was spoken or stays open to accept another voice input sessionid needs to contain the sessionid the response is meant for ideally provide it to allow concurrent sessions if not provided the first session that expects a response is assumed return result via the message to iot instance the iot instance also accepts a message with the name alexacustomresponse containing the key response with an object that can contain the keys responsetext and shouldendsession and sessionid as described above there will be no response from the iot instance to the message example of a script that uses texts important that ack true on id iot 0 smart lastcommand ack true change any obj you have 200ms to prepare the answer and to write it into iot x smart lastresponse setstate iot 0 smart lastresponse received phrase is obj state val important that ack false default example of a script that uses json objects important that ack true on id iot 0 smart lastcommandobj ack true change any obj you have 200ms to prepare the answer and to write it into iot x smart lastresponse const request json parse obj state val const response responsetext received phrase is request words join bye shouldendsession true sessionid request sessionid return response via state setstate iot 0 smart lastresponse json stringify response important that ack false default or alternatively return as message sendto iot 0 alexacustomresponse response private cloud if you use private skill action for communication with alexa google home so you have the possibility to use iot instance to process the requests from it e g for yandex alice const object from alisa service object from alisa service or empty object object from alisa service alisa path v1 0 user devices called url path could be any text but it must be there sendto iot 0 private type alisa request object from alisa service response send this response back to alisa service console log json stringify response the following types are supported alexa acting with amazon alexa or amazon custom skill ghome acting with google actions via google home alisa acting with yandex ifttt acting like ifttt actually not required but for tests purposes yandex instructions doc alisa md send messages to app from version 1 15 x you can send messages to iobroker visu application android and ios for that you need to write the following states setstate iot 0 app expire 60 optional time in seconds setstate iot 0 app priority normal optional priority high or normal setstate iot 0 app title iobroker optional default iobroker setstate iot 0 app message message text important that ack false default or just one state only is message is mandatory all other are optional setstate iot 0 app message json stringify message message text title iobroker expire 60 priority normal important that ack false default todo smartnames must have higher priority as groups devices should be grouped by smart name placeholder for the next version at the beginning of the line work in progress changelog 2 0 11 2023 06 20 bluefox added support for the state toggling alexa 3 bluefox done small improvements for alexa 3 2 0 9 2023 06 15 bluefox working on support for amazon alexa v3 2 0 2 2023 06 05 bluefox added support for amazon alexa v3 bluefox removed support for sugar blood indication 1 14 6 2023 05 12 bluefox corrected translations 1 14 5 2023 03 01 bluefox corrected names of enums in gui 1 14 3 2023 01 10 kirovilya fixed processing for lights with ct and rgb in alisa 1 14 2 2022 12 23 bluefox updated gui packages 1 14 1 2022 12 22 bluefox downgraded the axios version to 0 27 2 1 14 0 2022 12 13 bluefox added netatmo support 1 13 0 2022 12 08 apollon77 added support vor custom skill v2 1 12 5 2022 11 09 bluefox small changes on configuration gui 1 12 4 2022 11 03 bluefox added ukrainian language bluefox corrected blockly for unknown languages 1 12 2 2022 10 01 apollon77 fixed crash case 1 12 1 2022 09 27 bluefox corrected error in gui with empty password 1 12 0 2022 09 27 apollon77 do not control saturation with a percentage request via alexa bluefox migrated gui to v5 1 11 9 2022 07 22 apollon77 fix temperature controlling for thermostats via alexa 1 11 8 2022 06 24 apollon77 update dependencies to allow better automatic rebuild 1 11 7 2022 06 13 bluefox tried to correct url key creation for google home 1 11 5 2022 06 03 kirovilya alisa update for binary sensor motion and contact 1 11 4 2022 03 29 apollon77 fix crash cases reported by sentry 1 11 3 2022 03 23 bluefox added the generation of url key for services 1 11 2 2022 03 20 apollon77 fix crash case reported by sentry iobroker iot 3p 1 11 1 2022 03 18 apollon77 optimize logging when many devices are used 1 11 0 2022 03 17 apollon77 also support stored when a rgb state is turned on off apollon77 fixed control percent value to respect min max correctly bluefox support for response messages longer than 128k zip 1 10 0 2022 03 09 apollon77 respect min max when calculating the value for byon with values 1 9 7 2022 02 20 apollon77 fix crash case reported by sentry iobroker iot 3c 1 9 6 2022 02 19 apollon77 make sure to not remember the off value when using stored values for on apollon77 fix crash case reported by sentry iobroker iot 3a 1 9 5 2022 02 08 bluefox fixed google home error with color control 1 9 4 2022 02 08 bluefox fixed error with the certificates fetching 1 9 3 2022 02 03 bluefox removed deprecated package request bluefox refactoring and better error handling 1 9 2 2022 01 26 bluefox added experimental support for remote access 1 8 25 2021 11 18 bluefox corrected the enabling of the category 1 8 24 2021 09 19 bluefox respect the min max limits by controlling 1 8 23 2021 09 18 bluefox fixed the response for the heating control 1 8 22 2021 05 16 bluefox make it admin4 compatible 1 8 21 2021 05 16 bluefox fixed the encryption of the password warning if you see the message in the log that password is invalid please enter the password in configuration dialog one more time and save 1 8 20 2021 05 16 foxriver76 we now write data received from custom services with the acknowledge flag 1 8 19 2021 05 14 bluefox only added one debug output 1 8 16 2021 03 13 bluefox fixed the blind functionality in alisa 1 8 15 2021 03 12 bluefox implemented the sensor functionality in alisa 1 8 14 2021 03 12 bluefox allowed the control of the blinds in alisa 1 8 13 2021 02 04 apollon77 add missing object smart lastobjectid 1 8 12 2021 02 02 bluefox fixed the dimmer issue with alisa 1 8 11 2021 01 20 morluktom alexa corrected the request for percentage values 1 8 10 2021 01 20 bluefox added the reconnection strategy if dns address cannot be resolved 1 8 9 2020 12 27 bluefox updated configuration gui to the latest state 1 8 8 2020 12 14 bluefox corrected the google home error 1 8 6 2020 12 13 bluefox try to fix google home error 1 8 5 2020 11 23 bluefox corrected the configuration table for google home 1 8 4 2020 11 18 bluefox corrected the configuration table for google home 1 8 3 2020 11 16 bluefox trying to fix the set to false at start for google home 1 8 2 2020 11 15 bluefox added the debug outputs for google home 1 8 1 2020 11 13 bluefox the deletion of google home devices was corrected 1 8 0 2020 11 12 bluefox the google home table was rewritten 1 7 15 2020 11 05 morluktom corrected the request for temperature 1 7 14 2020 11 05 bluefox updated the select id dialog 1 7 12 2020 09 25 bluefox updated the select id dialog 1 7 9 2020 09 17 bluefox updated gui for config 1 7 7 2020 09 02 bluefox added information about changed linking process 1 7 6 2020 08 25 bluefox some colors were changed in the dark mode 1 7 5 2020 08 21 apollon77 crash prevented sentry iobroker iot w bluefox values for modes will be converted to number in alisa 1 7 3 2020 08 16 bluefox added vacuum cleaner to alisa 1 7 1 2020 08 16 bluefox added blinds lock and thermostat to alisa 1 6 4 2020 08 06 apollon77 crash prevented sentry iobroker iot v 1 6 3 2020 08 04 bluefox added french letters to allowed symbols 1 6 1 2020 07 10 bluefox used new selectid dialog in gui 1 5 3 2020 05 28 bluefox small change for nightscout 1 5 2 2020 05 21 bluefox changed requirements for password bluefox do not try to load the sharp if the blood sugar not enabled 1 4 18 2020 05 11 apollon77 make sure that invalid configured states or values without a timestamp do not crash adapter sentry iobroker iot 8 apollon77 make sure publishes after the disconnect to not break adapter sentry iobroker iot a 1 4 17 2020 05 11 bluefox better error output is implemented 1 4 14 2020 05 01 bluefox fixed the problem if admin is not on 8081 port 1 4 12 2020 04 30 apollon77 error case handled where system config objects does not exist sentry iobroker iot 5 1 4 11 2020 04 26 bluefox fixed iobroker iot react f 1 4 10 2020 04 24 bluefox fixed crashes reported by sentry 1 4 7 2020 04 23 fixed iot crash when timeouts in communications to google happens sentry iobroker iot 2 fixed iot crash when google answers without customdata sentry iobroker iot 1 1 4 6 2020 04 18 apollon77 add the sentry error reporting to react frontend 1 4 4 2020 04 14 apollon77 remove js controller 3 0 warnings and replace adapter objects access apollon77 add linux dependencies for canvas library apollon77 add sentry configuration 1 4 2 2020 04 08 ta2k fix updatestate for google home 1 4 1 2020 04 04 bluefox the blood glucose request supported now 1 3 4 2020 02 26 ta2k fixed deconz issues in google home 1 3 3 2020 02 12 apollon77 fix alisa error with invalid smartname attributes 1 3 2 2020 02 10 apollon77 usage with all kinds of admin ports and reverse proxies optimized 1 3 1 2020 02 09 apollon77 dependency updates apollon77 make compatible with admin 4 0 because of updated socket io 1 2 1 2020 01 18 bluefox fixed problem if the port of admin is not 8081 1 2 0 2020 01 04 ta2k google home handling and visualization improved 1 1 10 2020 01 03 bluefox now is allowed to select the temperature values as alexa states bluefox allowed the setting type immediately after insertion of new state 1 1 9 2019 11 27 bluefox fixed sometimes the configuration could not be loaded 1 1 8 2019 09 12 bluefox optimization of google home communication was done 1 1 7 2019 09 11 bluefox the sending rate to google home is limited now 1 1 6 2019 09 11 ta2k room fix for google home and linkeddevices 1 1 4 2019 09 10 bluefox decreased keepalive value to fix issue with disconnect 1 1 3 2019 09 09 ta2k google home problem fixed with linkeddevices 1 1 0 2019 09 06 bluefox added support of aliases 1 0 8 2019 09 03 ta2k improved support for google home ta2k added auto detection for rgb rgbsingle hue ct mediadevice switch info socket light dimmer thermostat windowtilt blinds slider ta2k added support for manually adding states as devices ta2k fix update state after sync ta2k added typical google home devices and traits actions ta2k fix only process update message when alexa is checked in the options 1 0 4 2019 08 01 bluefox fixed password encoding please enter password anew 1 0 3 2019 07 30 bluefox fixed language issues for google home and yandex alice 1 0 1 2019 07 26 bluefox support of private skills actions was added 1 0 0 2019 07 14 ta2k google home list was added 0 5 0 2019 06 29 bluefox tried to add yandex alisa 0 4 3 2019 04 14 apollon77 change enable disable of amazon alexa and of google home from configuration to be really active if selected 0 4 2 2019 03 10 bluefox allowed the enablement and disable of amazon alexa and of google home from configuration 0 4 1 2019 02 19 bluefox add version check to google home 0 3 1 2019 01 13 bluefox blockly was fixed 0 3 0 2018 12 30 bluefox detection of google devices was fixed 0 2 2 2018 12 21 bluefox generation of new url key was added 0 2 0 2018 12 18 bluefox change the name of adapter 0 1 8 2018 10 21 bluefox added extended diagnostics 0 1 7 2018 10 14 bluefox the configuration dialog was corrected bluefox the possibility to create the answer with script for the custom skill was implemented 0 1 4 2018 09 26 bluefox initial commit license the mit license mit copyright c 2018 2023 bluefox dogafox gmail com permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | iobroker amazon-alexa google-home ifttt | server |
taln-archives | taln archives taln archives est une archive num rique francophone des articles de recherche en traitement automatique de la langue elle contient actuellement les actes des conf rences recital et taln de 1997 2015 une version html est disponible ici http www atala org taln archives un fichier xml contenant les m ta donn es a t cr pour chaque dition des conf rences ce dernier contient m ta donn es de la conf rence titre de la conf rence acronyme ville pays dates de d but et de fin de la conf rence noms des pr sidents du comit de programme formats des articles publi s e g court long url du site web de la conf rence m ta donn es pour chaque article identifiant unique e g taln 2008 long 001 noms des auteurs emails affiliations titre r sum et mots cl s fran ais et anglais si disponible format de l article num ros des pages nom de la session dans le programme les fichiers bibtex de tous les articles ont t g n r s automatiquement partir du fichier de m ta donn es avec la commande cd tools generate bibtex files sh les fichiers au format texte des articles ont t extraits avec l outil pdftotext http poppler freedesktop org au format texte ou ocris s avec l outil tesseract ocr http code google com p tesseract ocr cd tools extract text from pdfs sh les m ta donn es des fichiers pdfs ont t modifi s avec l outil pdftk http www pdflabs com tools pdftk the pdf toolkit avec la commande cd tools update pdf metadata sh une version web de l archive peut tre cr e avec la commande cd tools python generate html py si vous utilisez cet ensemble de donn es veuillez citer l article florian boudin taln archives une archive num rique francophone des articles de recherche en traitement automatique de la langue traitement automatique des langues naturelles taln 2013 mises jour 19 06 2015 ajout des actes de taln recital 2015 et des ateliers restructuration du d pot avec l ajout des r pertoires conferences et ateliers 18 11 2014 ajout des titres en anglais pour taln 2014 recital 2014 et taln 2011 09 07 2014 ajout des actes de taln recital 2014 28 06 2014 corrections meta donn s 06 05 2015 bug fixes ajout des pr noms noms dans les fichiers de m ta donn es modification des bibtex maintenant en utf 8 transfert des informations sur les meilleurs papiers et les taux de s lection 07 04 2014 ajout des actes de taln 1997 et 1998 02 04 2014 ajout des actes de taln recital 1999 et 2000 28 03 2014 ajout des actes de taln recital 2006 06 02 2014 suppression des fichiers parscit html txt et conversion des articles avec pdftotext nettoyage des header footer 03 02 2014 ajout de l extraction des citations avec parscit 02 02 2014 ajout des actes de recital 2001 31 01 2014 ajout des actes des conf rences taln recital 2002 et taln 2001 modifications des scripts 29 01 2014 modification du script de conversion pdf txt et ajout des fichiers txt html et ocr 27 01 2014 ajout des actes de taln recital 2003 correction de probl mes de case des noms d auteurs correction de probl mes de fichiers corrompus recital 2008 long 010 correction de probl mes de fichiers prot g s taln 2010 long 037 modification globale des m ta donn es des fichiers pdfs l aide de pdftk 24 01 2014 ajout des actes de recital 2004 23 01 2014 ajout des actes de taln 2004 et modification des scripts pour la g n ration du site web 21 01 2014 ajout de m ta donn es pour taln et recital 2005 r sum mots cl s et modification des pdfs 15 01 2014 corrections de m ta donn es 08 01 2014 ajout des actes des conf rences taln 2005 et recital 2005 ajout des noms des sessions dans taln 2009 26 07 2013 ajout des fichiers textes extraits partir du contenu des articles au format pdf 18 07 2013 ajout des fichiers de g n ration de bibtex et du site web 25 06 2013 ajout des actes des conf rences taln 2013 et recital 2013 remerciements jos moreno thierry hamon patrick paroubek gil francopoulo amir hazem anne vilnat c drick fairon pierre zweigenbaum | ai |
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smart-contract-best-practices | img width 200 alt get in touch with consensys diligence src https user images githubusercontent com 2865694 56826101 91dcf380 685b 11e9 937c af49c2510aa0 png https consensys net diligence br sup https consensys net diligence mailto diligence consensys net https consensys net diligence tools sup br br smart contract security best practices visit the documentation site https consensys github io smart contract best practices read the docs in chinese https github com consensys smart contract best practices blob master readme zh md read the docs in vietnamese https github com consensys smart contract best practices blob master readme vi md contributions are welcome feel free to submit a pull request with anything from small fixes to full new sections if you are writing new content please reference the contributing docs about index md page for guidance on style see the issues https github com consensys smart contract best practices issues for topics that need to be covered or updated if you have an idea you d like to discuss please chat with us in gitter https gitter im consensys smart contract best practices building the documentation site git clone git github com consensys smart contract best practices git cd smart contract best practices pip install r requirements txt mkdocs build to run the server and restart on failure until mkdocs serve do done you can also use the mkdocs serve command to view the site on localhost and live reload whenever you save changes redeploying the documentation site mkdocs gh deploy | smart-contracts ethereum solidity security blockchain documentation | blockchain |
pytorch-pooling | pooling this is a collection of different pooling methods used in image classification segmentation detection features multi gpu support easy and useful training log file easy to test different pooling method on classification task requirements python3 6 pytorch1 6 0 cuda10 1 tensorboard 2 3 0 installation clone git clone https github com rentainhe pytorch pooling git make data directory for cifar100 bash cd pytorch pooling mkdir data usage 1 enter directory bash cd pytorch pooling 2 dataset only support cifar100 now will support imagenet later using cifar100 dataset from torchvision since it s more convinient 3 run tensorboard install tensorboard bash pip install tensorboard run tensorboard tensorboard logdir runs port 6006 host localhost 4 training our base backbone is vgg16 with batch normalization bash python3 train py run train name test pooling max run train test visual to set the mode to be executed name str to set the name of this training pooling str e g pooling max to set the pooling method in vgg16 to be max pool2d gpu str e g gpu 1 to set the specified gpu for training the supported pooling args are max pooling average pooling mixed pooling lp pooling lip pooling soft pooling 5 add a new pooling method you should add a new pooling method pool py in pooling pooling method and update the init py file 6 addition lip pooling the backbone in original paper is resnet but i use vggnet in this repo so there might be something wrong with the accuracy results the result i can get from this repo i train every model with the same hyperparam and i don t use any tricks in this repo dataset backbone pooling acc epoch lr 0 1 epoch lr 0 02 epoch lr 0 004 epoch lr 0 0008 total epoch cifar100 vgg16 bn max 70 89 60 60 40 40 200 cifar100 vgg16 bn avg 70 56 60 60 40 40 200 cifar100 vgg16 bn mixed 71 19 60 60 40 40 200 cifar100 vgg16 bn lp p 2 70 65 60 60 40 40 200 cifar100 vgg16 bn lp p 3 70 67 60 60 40 40 200 cifar100 vgg16 bn lip 71 23 60 60 40 40 200 cifar100 vgg16 bn softpool 71 39 60 60 40 40 200 implementated pooling mixed pooling mixed pooling for convolutional neural networks https rd springer com chapter 10 1007 978 3 319 11740 9 34 lp pooling convolutional neural networks applied to house numbers digit classification https arxiv org abs 1204 3968 lip pooling lip local importance based pooling https arxiv org abs 1908 04156 soft pooling refining activation downsampling with softpool https arxiv org abs 2101 00440 | ai |
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Embedded | course exercise of embedded system develop envirnment and tools linux kernel 2 6 qt 4 8 boa | os |
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mws-restaurant-stage-3 | local development api server usage get restaurants curl http localhost 1337 restaurants get restaurants by id curl http localhost 1337 restaurants 3 architecture local server node js sails js contributors brandy lee camacho technical project manager mailto brandy camacho udacity com david harris web services lead mailto david harris udacity com omar albeik frontend engineer mailto omaralbeik gmail com getting started development local api server location of server server server depends on node js lts version v6 11 2 https nodejs org en download npm https www npmjs com get npm and sails js http sailsjs com please make sure you have these installed before proceeding forward great you are ready to proceed forward awesome let s start with running commands in your terminal known as command line interface cli install project dependancies install project dependancies npm i install sails js globally install sails global npm i sails g start the server start server node server you should now have access to your api server environment debug environment development debug port 1337 endpoints get endpoints get all restaurants http localhost 1337 restaurants get favorite restaurants http localhost 1337 restaurants is favorite true get a restaurant by id http localhost 1337 restaurants restaurant id get all restaurant reviews http localhost 1337 reviews get a restaurant review by id http localhost 1337 reviews review id get all reviews for a restaurant http localhost 1337 reviews restaurant id restaurant id post endpoints create a new restaurant review http localhost 1337 reviews parameters restaurant id restaurant id name reviewer name rating rating comments comment text put endpoints favorite a restaurant http localhost 1337 restaurants restaurant id is favorite true unfavorite a restaurant http localhost 1337 restaurants restaurant id is favorite false update a restaurant review http localhost 1337 reviews review id parameters name reviewer name rating rating comments comment text delete endpoints delete a restaurant review http localhost 1337 reviews review id if you find a bug in the source code or a mistake in the documentation you can help us by submitting an issue to our waffle dashboard https waffle io udacity mwnd issues even better you can submit a pull request with a fix archival note this repository is deprecated therefore we are going to archive it however learners will be able to fork it to their personal github account but cannot submit prs to this repository if you have any issues or suggestions to make feel free to utilize the https knowledge udacity com forum to seek help on content specific issues submit a support ticket along with the link to your forked repository if learners are blocked for other reasons here are the links for the retail consumers https udacity zendesk com hc en us requests new and enterprise learners https udacityenterprise zendesk com hc en us requests new ticket form id 360000279131 | front_end |
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bitcoin-bubble-index | bitcoin bubble index what s this this project provides a visualization analysis tool for price bubble of bitcoin including basic price information 60 days accumulative increase hot keywords index and bubble index we accumulated the original data 2010 07 17 2020 03 09 and put them into original data folder and we visualize our analysis result using echarts 1 datasets we provide the following dataset price txt the bitcoin price in usd per day sentaddr txt number of unique active addresses per day transaction txt number of transactions in btc blockchain per day difficulty txt average mining difficulty per day gtrend txt google trends to bitcoin tweets txt tweets per day bitcoin you can get the lastest data from bitinfocharts com 2 how to use open index html in your browser directly and you will see the following page img src https github com aksnzhy bitcoin bubble index blob master index png width 800 in original data folder you can run the command python process data py to get the analysis result which will be stored in data json 1 https github com apache incubator echarts 2 https bitinfocharts com comparison bitcoin transactions html | bitcoin blockchain | blockchain |
bnlp | bengali natural language processing bnlp pypi version https img shields io pypi v bnlp toolkit https pypi org project bnlp toolkit downloads https static pepy tech badge bnlp toolkit https pepy tech project bnlp toolkit bnlp is a natural language processing toolkit for bengali language this tool will help you to tokenize bengali text embedding bengali words embedding bengali document bengali pos tagging bengali name entity recognition bangla text cleaning for bengali nlp purposes features tokenization basic tokenizer docs readme md basic tokenizer nltk tokenizer docs readme md nltk tokenization sentencepiece tokenizer docs readme md bengali sentencepiece tokenization embeddings word2vec embedding docs readme md bengali word2vec fasttext embedding docs readme md bengali fasttext glove embedding docs readme md bengali glove word vectors doc2vec document embedding docs readme md document embedding part of speech tagging crf based pos tagging docs readme md bengali crf pos tagging named entity recognition crf based ner docs readme md bengali crf ner text cleaning docs readme md text cleaning corpus docs readme md bengali corpus class letters vowels punctuations stopwords installation pip installer pip install bnlp toolkit or upgrade pip install u bnlp toolkit python 3 6 3 7 3 8 3 9 3 10 os linux windows mac build from source git clone https github com sagorbrur bnlp git cd bnlp python setup py install sample usage py from bnlp import basictokenizer tokenizer basictokenizer raw text tokens tokenizer raw text print tokens output documentation full documentation are available here https github com sagorbrur bnlp tree master docs if you are using previous version of bnlp check the documentation archive https github com sagorbrur bnlp tree master docs archive contributor guide check contributing md https github com sagorbrur bnlp blob master contributing md page for details thanks to semantics lab https www facebook com lab semantics all the developers who are contributing to enrich bengali nlp | bengali-nlp bengali-tokenization bengali-word-embedding nltk-tokenizer bengali-language bengal-pos-tagging bn-glove bengali bengali-nlp-library bengali-word2vec bengali-fasttext bangla bangla-nlp bangla-pos-tagging bangla-word2vec bengali-ner named-entity-recognition ner nlp | ai |
stat-nlp-nyu | stat nlp nyu statistical natural language processing at nyu | ai |
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fuelprices | fuelprices home work data engineering python file to parse json file into sqlite3 database | python3 parsing sqlite3 anaconda json | server |
cvlib | downloads https static pepy tech personalized badge cvlib period total units international system left color grey right color blue left text pip 20installs https pepy tech project cvlib pypi https img shields io pypi v cvlib svg color blue https pypi org project cvlib cvlib a simple high level easy to use open source computer vision library for python installation installing dependencies provided the below python packages are installed cvlib is completely pip installable opencv tensorflow if you don t have them already installed you can install through pip pip install opencv python tensorflow optional or you can compile them from source if you want to enable optimizations for your specific hardware for better performance if you are working with gpu you can install tensorflow gpu package through pip make sure you have the necessary nvidia drivers installed preoperly cuda toolkit cudnn etc if you are not sure just go with the cpu only tensorflow package you can also compile opencv from source to enable cuda optimizations for nvidia gpu installing cvlib pip install cvlib to upgrade to the newest version pip install upgrade cvlib optional if you want to build cvlib from source clone this repository and run the below commands git clone https github com arunponnusamy cvlib git cd cvlib pip install note compatability with python 2 x is not officially tested face detection detecting faces in an image is as simple as just calling the function detect face it will return the bounding box corners and corresponding confidence for all the faces detected example python import cvlib as cv faces confidences cv detect face image seriously that s all it takes to do face detection with cvlib underneath it is using opencv s dnn module with a pre trained caffemodel to detect faces to enable gpu python faces confidences cv detect face image enable gpu true checkout face detection py in examples directory for the complete code sample output examples images face detection output jpg gender detection once face is detected it can be passed on to detect gender function to recognize gender it will return the labels man woman and associated probabilities example python label confidence cv detect gender face underneath cvlib is using an alexnet like model trained on adience dataset https talhassner github io home projects adience adience data html agegender by gil levi and tal hassner for their cvpr 2015 https talhassner github io home publication 2015 cvpr paper to enable gpu python label confidence cv detect gender face enable gpu true checkout gender detection py in examples directory for the complete code sample output examples images gender detection output jpg object detection detecting common objects in the scene is enabled through a single function call detect common objects it will return the bounding box co ordinates corrensponding labels and confidence scores for the detected objects in the image example python import cvlib as cv from cvlib object detection import draw bbox bbox label conf cv detect common objects img output image draw bbox img bbox label conf underneath it uses yolov4 https github com alexeyab darknet model trained on coco dataset http cocodataset org capable of detecting 80 common objects https github com arunponnusamy object detection opencv blob master yolov3 txt in context to enable gpu python bbox label conf cv detect common objects img enable gpu true checkout object detection py in examples directory for the complete code real time object detection yolov4 is actually a heavy model to run on cpu if you are working with real time webcam video feed and doesn t have gpu try using tiny yolo which is a smaller version of the original yolo model it s significantly fast but less accurate python bbox label conf cv detect common objects img confidence 0 25 model yolov4 tiny check out the example examples object detection webcam py to learn more other supported models yolov3 yolov3 tiny custom trained yolo weights to run inference with custom trained yolov3 v4 weights try the following python from cvlib object detection import yolo yolo yolo weights config labels bbox label conf yolo detect objects img yolo draw bbox img bbox label conf to enable gpu python bbox label conf yolo detect objects img enable gpu true checkout the example examples yolo custom weights inference py to learn more sample output examples images object detection output jpg utils video to frames get frames method can be helpful when you want to grab all the frames from a video just pass the path to the video it will return all the frames in a list each frame in the list is a numpy array python import cvlib as cv frames cv get frames downloads demo mp4 optionally you can pass in a directory path to save all the frames to disk python frames cv get frames downloads demo mp4 downloads demo frames creating gif animate method lets you create gif from a list of images just pass a list of images or path to a directory containing images and output gif name as arguments to the method it will create a gif out of the images and save it to disk for you python cv animate frames documents frames gif sponsor developing and maintaining open source projects takes a lot of time and effort if you are getting value out of this project consider supporting my work by simply buying me a coffee https buymeacoffee com arunponnusamy one time or every month license cvlib is released under mit license help for bugs and feature requests feel free to file a github issue https github com arunponnusamy cvlib issues make sure to check whether the issue has been filed already for usage related how to questions please create a new question on stackoverflow https stackoverflow com questions tagged cvlib with the tag cvlib community join the official discord server https discord gg chhqjzgwfh or github discussions https github com arunponnusamy cvlib discussions to talk about all things cvlib citation if you find cvlib helpful in your work please cite the following bibtex misc ar2018cvlib author arun ponnusamy title cvlib high level computer vision library for python howpublished url https github com arunponnusamy cvlib year 2018 | computer-vision image-processing machine-learning deep-learning python | ai |
Database-Lab-Project | database lab project this is about a mandatory database lab under my bachelor program of software engineering at university duisburg essen block 1 modeling the database for the projectfunder crowdfunding platform block 2 sql with db2 block 3 implementation of crowdfunding projectfunder platform for more details please see the aufgaben pdf | server |
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vision-hw0 | cse 455 homework 0 welcome friends for the first assignment we ll just get to know the codebase a little bit and practice manipulating images transforming things breaking stuff it should be fun image basics we have a pretty basic datastructure to store images in our library the image struct stores the image metadata like width height and number of channels it also contains the image data stored as a floating point array you can check it out in src image h it looks like this typedef struct int h w c float data image we have also provided some functions for loading and saving images use the function image im load image image jpg to load a new image to save an image use save image im output which will save the image as output jpg if you want to make a new image with dimensions width x height x channels you can call image im make image w h c you should also use free image im when you are done with an image so it goes away you can check out how all this is implemented in src load image c you probably shouldn t change anything in this file we use the stb image libary for the actual loading and saving of jpgs because that is like really complicated i think i ve never tried anywho you ll be modifying the file src process image c we ve also included a python compatability library uwimg py includes the code to access your c library from python tryit py has some example code you can run we will build the library using make simply run the command make after you make any changes to the code then you can quickly test your changes by running uwimg test you can also try running the example python code to generate some images python tryit py 1 getting and setting pixels the most basic operation we want to do is change the pixels in an image as we talked about in class we represent an image as a 3 dimensional tensor we have spatial information as well as multiple channels which combine together to form a color image rgb format figs rgb png the convention is that the coordinate system starts at the top left of the image like so image coordinate system figs coords png in our data array we store the image in chw format the first pixel in data is at channel 0 row 0 column 0 the next pixel is channel 0 row 0 column 1 then channel 0 row 0 column 2 etc your first task is to fill out these two functions in src process image c float get pixel image im int x int y int c void set pixel image im int x int y int c float v get pixel should return the pixel value at column x row y and channel c set pixel should set the pixel to the value v you will need to do bounds checking to make sure the coordinates are valid for the image set pixel should simply return without doing anything if you pass in invalid coordinates for get pixel we will perform padding to the image there are a number of possible padding strategies image padding strategies figs pad png we will use the clamp padding strategy this means that if the programmer asks for a pixel at column 3 use column 0 or if they ask for column 300 and the image is only 256x256 you will use column 255 because of zero based indexing we can test out our pixel setting code on the dog image by removing all of the red channel see line 3 8 in tryit py 1 getting and setting pixels im load image data dog jpg for row in range im h for col in range im w set pixel im row col 0 0 save image im figs dog no red then try running it check out our very not red dog figs dog no red jpg 2 copying images sometimes you have an image and you want to copy it to do this we should make a new image of the same size and then fill in the data array in the new image you could do this by getting and setting pixels by looping over the whole array and just copying the floats pop quiz if the image is 256x256x3 how many total pixels are there or by using the built in memory copying function memcpy fill in the function image copy image image im in src process image c with your code 3 grayscale image now let s start messing with some images people like making images grayscale it makes them look old or something let s do it remember how humans don t see all colors equally here s the chart to remind you eye sensitivity to different wavelengths figs sensitivity png this actually makes a huge difference in practice here s a colorbar we may want to convert color bar figs colorbar png if we convert it using an equally weighted mean k r g b 3 we get a conversion that doesn t match our perceptions of the given colors averaging grayscale figs avggray jpg instead we are going to use a weighted sum now there are a few ways to do this if we wanted the most accurate conversion it would take a fair amount of work srgb uses gamma compression 1 so we would first want to convert the color to linear rgb and then calculate relative luminance https en wikipedia org wiki relative luminance but we don t care about being toooo accurate so we ll just do the quick and easy version instead video engineers use a calculation called luma 2 to find an approximation of perceptual intensity when encoding video signal we ll use that to convert our image to grayscale it operates directly on the gamma compressed srgb values that we already have we simply perform a weighted sum y 0 299 r 0 587 g 114 b using this conversion technique we get a pretty good grayscale image now we can run tryit py to output graybar jpg see lines 10 13 3 grayscale image im load image data colorbar png graybar rgb to grayscale im save image graybar graybar grayscale colorbars figs gray png implement this conversion for the function rgb to grayscale return a new image that is the same size but only one channel containing the calculated luma values 4 shifting the image colors now let s write a function to add a constant factor to a channel in an image we can use this across every channel in the image to make the image brighter or darker we could also use it to say shift an image to be more or less of a given color fill in the code for void shift image image im int c float v it should add v to every pixel in channel c in the image now we can try shifting all the channels in an image by 4 or 40 see lines 15 20 in tryit py 4 shift image im load image data dog jpg shift image im 0 4 shift image im 1 4 shift image im 2 4 save image im overflow but wait when we look at the resulting image overflow jpg we see something bad has happened the light areas of the image went past 1 and when we saved the image back to disk it overflowed and made weird patterns overflow figs overflow jpg 5 clamping the image values our image pixel values have to be bounded generally images are stored as byte arrays where each red green or blue value is an unsigned byte between 0 and 255 0 represents none of that color light and 255 represents that primary color light turned up as much as possible we represent our images using floating point values between 0 and 1 however we still have to convert between our floating point representation and the byte arrays that are stored on disk in the example above our pixel values got above 1 so when we converted them back to byte arrays and saved them to disk they overflowed the byte data type and went back to very small values that s why the very bright areas of the image looped around and became dark we want to make sure the pixel values in the image stay between 0 and 1 implement clamping on the image so that any value below zero gets set to zero and any value above 1 gets set to one fill in void clamp image image im to modify the image in place then when we clamp the shifted image and save it we see much better results see lines 22 24 in tryit py 5 clamp image clamp image im save image im fixed and the resulting image fixed jpg figs fixed jpg 6 rgb to hue saturation value so far we ve been focussing on rgb and grayscale images but there are other colorspaces out there too we may want to play around with like hue saturation and value hsv https en wikipedia org wiki hsl and hsv we will be translating the cubical colorspace of srgb to the cylinder of hue saturation and value rgb hsv conversion figs convert png hue https en wikipedia org wiki hue can be thought of as the base color of a pixel saturation https en wikipedia org wiki colorfulness saturation is the intensity of the color compared to white the least saturated color the value https en wikipedia org wiki lightness is the perception of brightness of a pixel compared to black you can try out this demo http math hws edu graphicsbook demos c2 rgb hsv html to get a better feel for the differences between these two colorspaces for a geometric interpretation of what this transformation rgb to hsv geometry figs rgbtohsv png now to be sure there are lots of issues http poynton ca notes colour and gamma colorfaq html rtftoc36 with this colorspace but it s still fun to play around with and relatively easy to implement the easiest component to calculate is the value it s just the largest of the 3 rgb components v max r g b next we can calculate saturation this is a measure of how much color is in the pixel compared to neutral white gray neutral colors have the same amount of each three color components so to calculate saturation we see how far the color is from being even across each component first we find the minimum value m min r g b then we see how far apart the min and max are c v m and the saturation will be the ratio between the difference and how large the max is s c v except if r g and b are all 0 because then v would be 0 and we don t want to divide by that so just set the saturation 0 if that s the case finally to calculate hue we want to calculate how far around the color hexagon our target color is color hex figs hex png we start counting at red each step to a point on the hexagon counts as 1 unit distance the distance between points is given by the relative ratios of the secondary colors we can use the following formula from wikipedia https en wikipedia org wiki hsl and hsv hue and chroma img src figs eq svg width 256 there is no correct hue if c 0 because all of the channels are equal so the color is a shade of gray right in the center of the cylinder however for now let s just set h 0 if c 0 because then your implementation will match mine notice that we are going to have h 0 1 and it should circle around if it gets too large or goes negative thus we check to see if it is negative and add one if it is this is slightly different than other methods where h is between 0 and 6 or 0 and 360 we will store the h s and v components in the same image so simply replace the r channel with h the g channel with s etc 7 hsv to rgb ok now do it all backwards in hsv to rgb finally when your done we can mess with some images in tryit py we convert an image to hsv increase the saturation then convert it back lines 26 32 6 7 colorspace and saturation im load image data dog jpg rgb to hsv im shift image im 1 2 clamp image im hsv to rgb im save image im dog saturated saturated dog picture figs dog saturated jpg hey that s exciting play around with it a little bit see what you can make note that with the above method we do get some artifacts because we are trying to increase the saturation in areas that have very little color instead of shifting the saturation you could scale the saturation by some value to get smoother results 8 a small amount of extra credit implement void scale image image im int c float v to scale a channel by a certain amount this will give us better saturation results note you will have to add the necessary lines to the header and python library it should be very similar to what s already there for shift image now if we scale saturation by 2 instead of just shifting it all up we get much better results im load image data dog jpg rgb to hsv im scale image im 1 2 clamp image im hsv to rgb im save image im dog scale saturated dog saturated smoother figs dog scale saturated jpg 9 super duper extra credit implement rgb to hue chroma lightness https en wikipedia org wiki cieluv cylindrical representation 28cielch 29 a perceptually more accurate version of hue saturation value note this will involve gamma decompression converting to ciexyz converting to cieluv converting to hcl and the reverse transformations the upside is a similar colorspace to hsv but with better perceptual properties turn it in you only need to turn in one file your process image c use the dropbox link on the class website 1 https en wikipedia org wiki srgb the srgb transfer function gamma 2 https en wikipedia org wiki luma video | ai |
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Django-3-Web-Development-Cookbook-Fourth-Edition | django 3 web development cookbook django 3 web development cookbook published by packt download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781838987428 https packt link free ebook 9781838987428 a p | front_end |
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4180_team_project_2D_mapping_bot | 2d mapping robot image https user images githubusercontent com 59176907 166605907 93b266ab 1fa6 4b01 ad0f eecf105977df png this is a final team based project for georgia tech s ece 4180 embedded system design course team members juntao wang jwang3046 gatech edu sicheng zou sicheng zou gatech edu nick witten nick witten gatech edu overview the goal of the project is to create a robot that autonomously roams a room and plots the 2d coordinates of walls and obstacles while expanding its knowledge in a frontier based fashion design description the shadow bot chassis is used as the frame of the robot 2 dc motors with a dual h bridge driver module are used to maneuver the robot and 3 lidar sensors are used to measure distances while the robot is moving the whole system is based on the mbed lpc1768 running mbed rtos in addition a raspberry pi is used to send control commands and for data manipulation in python the robot is able to flash code wirelessly using the ssh protocol on the raspberry pi and serial communication between the raspberry pi and mbed hardware systems image https user images githubusercontent com 59176907 166589332 684a14d0 5920 4d25 ae13 304ae478fbdf png software systems flow chart mapper https user images githubusercontent com 64867842 166290131 8ed56b9a 3980 4f2d 981d 875d3332afb2 jpg control system diagram automonus mode flow chart https user images githubusercontent com 64867842 166316527 de09a802 df6c 48b6 8c22 c4eb831cb8b8 jpg autonomous mode state diagram software setup the virtual com port is used to control the mapper through the raspberry pi make sure the pi has internet connection use ssh to obtain a terminal clone this repository and use gcc arm none eabi compiler to build the mbed source code a usb micro type b to a usb mini type b cable should be used to connect the raspberry pi to the mbed to flash the binary file copy it to media pi mbed the two python scripts are to be run on the pi and a spare pc not connected to the pi make sure the project paths and pi ip address is updated in the python scripts usage to control the robot open the serial port to the mbed from the pi this can be done with the following commands pip install pyserial python m serial tools miniterm dev ttyacm0 9600 the following commands can be issued over the serial port key action arrow up speed up arrow down slow down backup arrow left turn counterclockwise 90 degrees arrow right turn clockwise 90 degrees r reset state and map a toggle autonomous mode enter print mapper state the two python scripts can be used to enable plotting pi saver py should be run on the pi and pc plotter py should be run on a pc commands can still be issued while running the pi saver py script but they should typed into the script stdin and enter must be issued after every keypress so they can be passed through to the mbed part list mbed lpc1768 https www sparkfun com products 9564 shadow bot chassis https www sparkfun com products 13301 sparkfun motor driver dual tb6612fng with headers https www sparkfun com products 14450 hobby gearmotor 140 rpm pair https www sparkfun com products 13302 2x wheels https www sparkfun com products 13259 3x vl53l0x tof sensor https www adafruit com product 3317 2x wheel encoder kit https www sparkfun com products 12629 5v battery pack https www sparkfun com products 9835 usb portable charger https us anker com products a1215 dc barrel jack adapter https www sparkfun com products 10811 raspberry pi zero w https www sparkfun com products 14277 connections motor drivers and dual motors mbed lpc1768 motor driver motor left motor right vout vcc gnd gnd vm to power a01 red a02 black b01 red b02 black p21 pwma p22 pmwb p6 ai1 p7 bi1 p5 ai2 p8 bi2 lidar sensors mbed lpc1768 lidar right lidar left lidar center vout vin vin vin gnd gnd gnd gnd p28 sda sda sda p27 scl scl scl p24 shdn p25 shdn p26 shdn encoders mbed lpc1768 encoder left encoder right vout red red gnd black black p11 white p12 white source code click here https github com ericjuntao 4180 team project 2d mapping bot tree main src photos image https user images githubusercontent com 103451305 166268167 431d8e7a 00c7 478a a439 cd10e1d252b3 jpeg robot front view image https user images githubusercontent com 103451305 166268255 cac2833a e02a 4056 81dd a8b20729ecb8 jpeg robot top view image https user images githubusercontent com 103451305 166268281 64000073 e4c9 41c7 bfc8 bb1114eec288 jpeg robot motor and encoder videos 5d426e33601fc5a9d46645a86f8db33 https user images githubusercontent com 64867842 166330112 a8ef231e 3e2b 4b0d a220 d8afc62608e4 png https www youtube com watch v h9ovxw3yx7e ab channel zousicheng autonomous mapping demo 0ea3cb7534f60492225665e88f2c3e7 https user images githubusercontent com 64867842 166330598 6b51ec51 b8f4 447f ba78 00ca33d45f72 png https www youtube com watch v 4t7in5bh2gq ab channel zousicheng remote control mapping demo | os |
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LiteOS | liteos port on mi v soft processors this repository contains liteos example projects running on mi v soft processors rv32 refer to the readme md in each project folder for more details | os |
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CS460 | students of cs460 if you found this repository because you are looking for solutions to 460 labs then you succeeded and you re welcome to them however cs460 requires knowledge of what the code is actually doing to pass the interviews and if you are found lacking in such the professor will know and it won t look good for you knowing that proceed with caution copyright copyright 2010 2015 k c wang kwang eecs wsu edu this program is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation either version 3 of the license or at your option any later version this program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details you should have received a copy of the gnu general public license along with this program if not see http www gnu org licenses | os |
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Clustering_on_Hyperspectral_Image | clustering on hyperspectral image project for clustering master course of data science and information technologies dsit description of project this project is devoted to hyperspectral images hsi processing the goal is compare the performance of cost function optimization clustering algorithms on the one hand and the hierarchical algorithms on the other hand in finding homogeneous regions in the salinas hsi for more information regarding the project please check the project custering algorithms pdf for more details regarding the algorithms that were utilized and the corresponding results please check the clustering project pdf code for the implementation of the clustering algorithms is based on https github com pikrakis introduction to pattern recognition a matlab approach | clustering hierarchical-clustering cost-function hyperspectral-images matlab salinas-valley pca bsas | server |
NLP | nlp code examples for the natural language processing course i teach at the university of texas at dallas videos available on my youtube channel https www youtube com playlist list plfe6ica dewk oyj4vlz5jbqvltc7jjoc notes explaining the code can be found on amazon in book form nlp book https www amazon com exploring nlp python building understanding dp b08p8qkdzk if the jupyter notebooks won t load on your machine copy and paste the link into jupyter s notebook viewer https nbviewer jupyter org | ai |
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NVTabular | nvtabular https github com nvidia nvtabular pypi https img shields io pypi v nvtabular color orange label version https pypi python org pypi nvtabular license https img shields io github license nvidia merlin nvtabular https github com nvidia merlin nvtabular blob stable license documentation https img shields io badge documentation blue svg https nvidia merlin github io nvtabular stable introduction html nvtabular is a feature engineering and preprocessing library for tabular data that is designed to easily manipulate terabyte scale datasets and train deep learning dl based recommender systems it provides high level abstraction to simplify code and accelerates computation on the gpu using the rapids dask cudf https github com rapidsai cudf tree main python dask cudf library nvtabular is a component of nvidia merlin https developer nvidia com nvidia merlin an open source framework for building and deploying recommender systems and works with the other merlin components including merlin models https github com nvidia merlin models hugectr https github com nvidia hugectr and merlin systems https github com nvidia merlin systems to provide end to end acceleration of recommender systems on the gpu extending beyond model training with nvidia s triton inference server https github com nvidia tensorrt inference server the feature engineering and preprocessing steps performed on the data during training can be automatically applied to incoming data during inference img src https developer nvidia com blog wp content uploads 2020 07 recommender system training pipeline 1 png benefits when training dl recommender systems data scientists and machine learning ml engineers have been faced with the following challenges huge datasets commercial recommenders are trained on huge datasets that may be several terabytes in scale complex data feature engineering and preprocessing pipelines datasets need to be preprocessed and transformed so that they can be used with dl models and frameworks in addition feature engineering creates an extensive set of new features from existing ones requiring multiple iterations to arrive at an optimal solution input bottleneck data loading if not well optimized can be the slowest part of the training process leading to under utilization of high throughput computing devices such as gpus extensive repeated experimentation the entire data engineering training and evaluation process can be repetitious and time consuming requiring significant computational resources nvtabular alleviates these challenges and helps data scientists and ml engineers process datasets that exceed gpu and cpu memory without having to worry about scale focus on what to do with the data and not how to do it by using abstraction at the operation level prepare datasets quickly and easily for experimentation so that more models can be trained deploy models into production by providing faster dataset transformation learn more in the nvtabular core features documentation https nvidia merlin github io nvtabular stable core features html performance when running nvtabular on the criteo 1tb click logs dataset using a single v100 32gb gpu feature engineering and preprocessing was able to be completed in 13 minutes furthermore when running nvtabular on a dgx 1 cluster with eight v100 gpus feature engineering and preprocessing was able to be completed within three minutes combined with hugectr http www github com nvidia hugectr the dataset can be processed and a full model can be trained in only six minutes the performance of the criteo drlm workflow also demonstrates the effectiveness of the nvtabular library the original etl script provided in numpy took over five days to complete combined with cpu training the total iteration time is over one week by optimizing the etl code in spark and running on a dgx 1 equivalent cluster the time to complete feature engineering and preprocessing was reduced to three hours meanwhile training was completed in one hour installation nvtabular requires python version 3 7 additionally gpu support requires cuda version 11 0 nvidia pascal gpu or later compute capability 6 0 nvidia driver 450 80 02 linux or wsl installing nvtabular using conda nvtabular can be installed with anaconda from the nvidia channel by running the following command conda install c nvidia c rapidsai c numba c conda forge nvtabular python 3 7 cudatoolkit 11 2 installing nvtabular using pip nvtabular can be installed with pip by running the following command pip install nvtabular installing nvtabular with pip causes nvtabular to run on the cpu only and might require installing additional dependencies manually when you run nvtabular in one of our docker containers the dependencies are already installed installing nvtabular with docker nvtabular docker containers are available in the nvidia merlin container repository https catalog ngc nvidia com filters orderby scoredesc query merlin the following table summarizes the key information about the containers container name container location functionality merlin hugectr https catalog ngc nvidia com orgs nvidia teams merlin containers merlin hugectr nvtabular hugectr and triton inference merlin tensorflow https catalog ngc nvidia com orgs nvidia teams merlin containers merlin tensorflow nvtabular tensorflow and triton inference merlin pytorch https catalog ngc nvidia com orgs nvidia teams merlin containers merlin pytorch nvtabular pytorch and triton inference to use these docker containers you ll first need to install the nvidia container toolkit https github com nvidia nvidia docker to provide gpu support for docker you can use the ngc links referenced in the table above to obtain more information about how to launch and run these containers to obtain more information about the software and model versions that nvtabular supports per container see support matrix https github com nvidia nvtabular blob stable docs source resources support matrix rst notebook examples and tutorials we provide a collection of examples https github com nvidia merlin nvtabular tree stable examples to demonstrate feature engineering with nvtabular as jupyter notebooks introduction to nvtabular s high level api advanced workflows with nvtabular nvtabular on cpu scaling nvtabular to multi gpu systems in addition nvtabular is used in many of our examples in other merlin libraries end to end examples with merlin https github com nvidia merlin merlin tree stable examples training examples with merlin models https github com nvidia merlin models tree stable examples training examples with transformer4rec https github com nvidia merlin transformers4rec tree stable examples feedback and support if you d like to contribute to the library directly see the contributing md https github com nvidia nvtabular blob stable contributing md we re particularly interested in contributions or feature requests for our feature engineering and preprocessing operations to further advance our merlin roadmap we encourage you to share all the details regarding your recommender system pipeline in this survey https developer nvidia com merlin devzone survey if you re interested in learning more about how nvtabular works see our nvtabular documentation https nvidia merlin github io nvtabular stable introduction html we also have api documentation https nvidia merlin github io nvtabular stable api index html that outlines the specifics of the available calls within the library | deep-learning feature-engineering feature-selection gpu machine-learning nvidia preprocessing recommendation-system recommender-system | os |
Paper-Notes | cv dl papers reading list irregular updating recommanded resources in computer vision and deep learning including advanced paper and issue solutions in experiments mark read status as heavy check mark contents cv dl papers reading list irregular updating cv dl papers reading listirregular updating contents contents image recognition image recognition backbone network backbone network light weight network light weight network image segmentation image segmentation semantic segmentation semantic segmentation real time segmentation real time segmentation instance segmentaion instance segmentaion panoptic segmentation panoptic segmentation losses losses object detection object detection anchor free anchor free image restoration image restoration super resolution super resolution image caption image caption generative adversarial networks generative adversarial networks attention mechanism attention mechanism natural language processing related natural language processing related medical image analysis medical image analysis other applications other applications pose estimation pose estimation training tricks training tricks data augmentation data augmentation normalization normalization initialization initialization automl automl dataset and contest dataset and contest dataset dataset contest contest image recognition backbone network x alexnet imagenet classification with deep convolutional neural networks 2012 neurips 2012 paper http www image net org challenges lsvrc 2012 supervision pdf pytorch https github com pytorch vision blob master torchvision models alexnet py x zfnet visualizing and understanding convolutional networks 2013 11 eccv 2014 paper https arxiv org abs 1311 2901 tensorflow https github com infocusp tf cnnvis x nin network in network 2013 12 iclr 2014 paper https arxiv org abs 1312 4400 tensorflow tflearn https github com tflearn tflearn blob master examples images network in network py x sppnet spatial pyramid pooling in deep convolutional networks for visual recognition 2014 6 eccv 2014 paper https arxiv org abs 1406 4729 x vgg very deep convolutional networks for large scale image recognition 2014 09 paper https arxiv org abs 1409 1556 slide http www robots ox ac uk karen pdf ilsvrc 2014 pdf pytorch https github com pytorch vision blob master torchvision models vgg py x inception v1 going deeper with convolutions 2014 09 paper https arxiv org abs 1409 4842 pytorch https github com kuangliu pytorch cifar blob master models googlenet py x inception v2 batch normalization accelerating deep network training by reducing internal covariate shift 2015 02 paper https arxiv org abs 1502 03167 x inception v3 rethinking the inception architecture for computer vision 2015 12 cvpr 2016 paper https arxiv org abs 1512 00567 pytorch https github com pytorch vision blob master torchvision models inception py x resnet deep residual learning for image recognition 2015 12 cvpr 2016 paper https arxiv org abs 1512 03385 pytorch https github com pytorch vision blob master torchvision models resnet py x resnet v2 identity mappings in deep residual networks 2016 03 eccv 2016 paper https arxiv org abs 1603 05027 torch https github com kaiminghe resnet 1k layers mxnet https github com tornadomeet resnet x inception v4 inception v4 inception resnet and the impact of residual connections on learning 2016 02 paper https arxiv org abs 1602 07261 tensorflow https github com tensorflow models blob master research slim nets inception v4 py pytorch https github com cadene pretrained models pytorch blob master pretrainedmodels models inceptionv4 py x wide resnet wide residual networks 2016 05 paper https arxiv org abs 1605 07146 pytorch https github com szagoruyko wide residual networks x densenet densely connected convolutional networks 2016 08 cvpr 2017 paper https arxiv org abs 1608 06993 pytorch https github com pytorch vision blob master torchvision models densenet py x xception xception deep learning with depthwise separable convolutions 2016 10 paper https arxiv org abs 1610 02357 pytorch https github com cadene pretrained models pytorch x resnext aggregated residual transformations for deep neural networks 2016 11 paper https arxiv org abs 1611 05431 pytorch https github com hsuxu resnext polynet polynet a pursuit of structural diversity in very deep networks 2016 11 paper https arxiv org abs 1611 05725 pytorch https github com cadene pretrained models pytorch blob master pretrainedmodels models polynet py x drn dilated residual networks 2017 05 cvpr 2017 paper https arxiv org abs 1705 09914 pytorch https github com fyu drn blob master classify py x dpn dual path networks 2017 07 paper https arxiv org abs 1707 01629 pytorch https github com rwightman pytorch dpn pretrained mxnet https github com cypw dpns x nasnet learning transferable architectures for scalable image recognition 2017 07 paper https arxiv org abs 1707 07012 tensorflow https github com tensorflow models tree master research slim nets nasnet x senet squeeze and excitation networks 2017 09 paper https arxiv org abs 1709 01507 caffe https github com hujie frank senet pytorch https github com moskomule senet pytorch x capsules dynamic routing between capsules 2017 10 paper https arxiv org abs 1710 09829 pytorch https github com gram ai capsule networks x non local non local neural networks 2017 11 paper https arxiv org abs 1711 07971 pytorch https github com alexhex7 non local pytorch pnasnet progressive neural architecture search 2017 12 paper https arxiv org abs 1712 00559 pytorch https github com chenxi116 pnasnet pytorch tensorflow https github com tensorflow models blob 696b69a498b43f8e6a1ecb24bb82f7b9db87c570 research slim nets nasnet pnasnet py light weight network x squeeze net squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size 2016 02 paper https arxiv org abs 1602 07360 pytorch https github com pytorch vision blob master torchvision models squeezenet py x mobilenets mobilenets efficient convolutional neural networks for mobile vision applications 2017 04 paper https arxiv org abs 1704 04861 tensorflow https github com tensorflow models blob master research slim nets mobilenet v1 py pytorch https github com marvis pytorch mobilenet x shufflenet v1 shufflenet an extremely efficient convolutional neural network for mobile devices 2017 07 paper https arxiv org abs 1707 01083 pytorch https github com jaxony shufflenet igcv1 interleaved group convolutions for deep neural networks 20017 07 iccv 2017 paper https arxiv org abs 1707 02725 mxnet https github com hellozting interleavedgroupconvolutions mobilenet v2 mobilenetv2 inverted residuals and linear bottlenecks 2018 01 cvpr 2018 paper https arxiv org abs 1801 04381 pytorch https github com tonylins pytorch mobilenet v2 x enas efficient neural architecture search via parameter sharing 2018 02 icml 2018 paper https arxiv org abs 1802 03268 tensorflow https github com melodyguan enas pytorch https github com carpedm20 enas pytorch x squeezenext squeezenext hardware aware neural network design 2018 03 cvpr 2018 paper https arxiv org abs 1803 10615 caffe https github com amirgholami squeezenext pytorch https github com luuuyi squeezenext pytorch igcv2 igcv2 interleaved structured sparse convolutional neural networks 2018 04 cvpr 2018 paper https arxiv org abs 1804 06202 mxnet https github com homles11 igcv3 igcv3 igcv3 interleaved low rank group convolutions for efficient deep neural networks 2018 06 bmvc 2018 paper https arxiv org abs 1806 00178 mxnet https github com homles11 igcv3 x shufflenet v2 shufflenet v2 practical guidelines for efficient cnn architecture design 2018 07 cvpr 2018 paper https arxiv org abs 1807 11164 pytorch https github com miaow1988 shufflenet v2 pytorch caffe pytorch https github com ericsun99 shufflenet v2 pytorch espnetv2 espnetv2 a light weight power efficient and general purpose convolutional neural network 2018 11 paper https arxiv org abs 1811 11431 pytorch https github com sacmehta espnetv2 image segmentation image segmentation figs image segmentation png semantic segmentation x fcn1 fully convolutional networks for semantic segmentation 2014 11 cvpr 2015 paper1 http www arxiv org abs 1411 4038 paper1 http www arxiv org abs 1605 06211 pytorch https github com wkentaro pytorch fcn x deeplab v1 semantic image segmentation with deep convolutional nets and fully connected crfs 2014 12 iclr 2015 paper https arxiv org abs 1412 7062 caffe https github com cdmh deeplab public x crf rnn conditional random fields as recurrent neural networks 2015 02 iccv 2015 paper https arxiv org abs 1502 03240 pytorch https github com torrvision crfasrnn x u net u net convolutional networks for biomedical image segmentation 2015 05 miccai 2015 paper https arxiv org abs 1505 04597 pytorch https github com milesial pytorch unet pytorch hsu https github com hsuxu carvana pytorch unet parsenet parsenet looking wider to see better 2015 06 iclr 2016 paper https arxiv org abs 1506 04579 caffe https github com debidatta caffe parsenet dag dag recurrent neural networks for scene labeling 2015 09 cvpr 2016 paper https arxiv org abs 1509 00552 pytorch https github com sallymmx dag rnn dpn semantic image segmentation via deep parsing network 2015 09 iccv 2015 paper https arxiv org abs 1509 02634 project https liuziwei7 github io projects dpn html x segnet segnet a deep convolutional encoder decoder architecture for image segmentation 2015 11 pami 2016 paper https arxiv org abs 1511 00561 caffe https github com alexgkendall caffe segnet tensoflow https github com tkuanlun350 tensorflow segnet pytorch https github com meetshah1995 pytorch semseg blob master ptsemseg models segnet py attention to scale attention to scale scale aware semantic image segmentation liang chieh 2015 11 cvpr 2016 paper http arxiv org abs 1511 03339 caffe https bitbucket org aquariusjay deeplab public ver2 project http lijiancheng0614 github io 2016 11 03 2016 11 03 attention to scale x dilated conv multi scale context aggregation by dilated convolutions 2015 11 iclr 2016 paper https arxiv org abs 1511 07122 caffe https github com fyu dilation sec seed expand and constrain three principles for weakly supervised image segmentation 2016 03 eccv 2016 paper http arxiv org abs 1603 06098 caffe https github com kolesman sec lrr laplacian pyramid reconstruction and refinement for semantic segmentation 2016 05 eccv 2016 paper https arxiv org abs 1605 02264 matlab https github com golnazghiasi lrr x deeplab v2 deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs 2016 06 tpami 2018 paper https arxiv org abs 1606 00915 caffe https bitbucket org aquariusjay deeplab public ver2 dpn mrf deep learning markov random field for semantic segmentation 2016 06 tpami 2017 paper2 https arxiv org abs 1606 07230 refinenet refinenet multi path refinement networks for high resolution semantic segmentation 2016 11 cvpr 2017 paper https arxiv org abs 1611 06612 matconvnet https github com guosheng refinenet pytorch https github com thomasjpfan pytorch refinenet ifcn improving fully convolution network for semantic segmentation 2016 11 paper https arxiv org abs 1611 08986 frrn full resolution residual networks for semantic segmentation in street scenes 2016 11 cvpr 2017 paper https arxiv org abs 1611 08323 theano https github com tobypde frrn x tiramisu the one hundred layers tiramisu fully convolutional densenets for semantic segmentation 2016 11 cvprw 2017 paper https arxiv org abs 1611 09326 theano https github com simjeg fc densenet pytorch https github com bfortuner pytorch tiramisu x pspnet pyramid scene parsing network 2016 12 cvpr 2017 paper https arxiv org abs 1612 01105 caffe https github com hszhao pspnet pytorch1 https github com meetshah1995 pytorch semseg blob master ptsemseg models pspnet py pytorch2 https github com lextal pspnet pytorch x duc understanding convolution for semantic segmentation 2017 02 wacv 2018 paper https arxiv org abs 1702 08502 mxnet https github com tusimple tusimple duc pixelnet pixelnet representation of the pixels by the pixels and for the pixels 2017 02 paper https arxiv org abs 1702 06506 caffe https github com aayushbansal pixelnet project http www cs cmu edu aayushb pixelnet x gcn large kernel matters improve semantic segmentation by global convolutional network 2017 03 paper https arxiv org abs 1703 02719 pytorch1 https github com medal iitb lung segmentation pytorch2 https github com sconsul global convolutional network pixeltcn pixel deconvolutional networks 2017 05 paper https arxiv org abs 1705 06820 tensorflow https github com hongyanggao pixeltcn x lovaszsoftmax the lov sz softmax loss a tractable surrogate for the optimization of the intersection over union measure in neural networks 2017 05 cvpr 2018 paper https arxiv org abs 1705 08790 pytorch tensorflow https github com bermanmaxim lovaszsoftmax drn dilated residual networks 2017 05 cvpr 2017 paper https arxiv org abs 1705 09914 pytorch https github com fyu drn blob master classify py g frnet gated feedback refinement network for dense image labeling cvpr 2017 paper https www cs umanitoba ca ywang papers cvpr17 pdf caffe https github com mrochan gfrnet tversky loss tversky loss function for image segmentation using 3d fully convolutional deep networks 2017 06 paper https arxiv org abs 1706 05721 x generalised dice generalised dice overlap as a deep learning loss function for highly unbalanced segmentations 2017 07 paper https arxiv org abs 1707 03237 segmentation aware segmentation aware convolutional networks using local attention masks 2017 08 iccv 2017 paper https arxiv org abs 1708 04607 caffe https github com aharley segaware sdn stacked deconvolutional network for semantic segmentation 2017 08 paper https arxiv org abs 1708 04943 seg everything learning to segment every thing 2017 11 cvpr 2018 paper https arxiv org abs 1711 10370 caffe2 https github com ronghanghu seg every thing x deeplab v3 encoder decoder with atrous separable convolution for semantic image segmentation 2018 02 paper https arxiv org abs 1802 02611 tensorflow https github com rishizek tensorflow deeplab v3 plus tensorflow official https github com tensorflow models tree master research deeplab pytorch https github com jfzhang95 pytorch deeplab xception r2u net recurrent residual convolutional neural network based on u net r2u net for medical image segmentation 2018 02 paper https arxiv org abs 1802 06955 pytorch https github com leejunhyun image segmentation adaptsegnet learning to adapt structured output space for semantic segmentation 2018 02 cvpr 2018 paper https arxiv org abs 1802 10349 pytorch https github com wasidennis adaptsegnet x attention u net attention u net learning where to look for the pancreas 2018 04 paper https arxiv org abs 1804 03999 pytorch https github com ozan oktay attention gated networks vortex pooling vortex pooling improving context representation in semantic segmentation 2018 04 paper https arxiv org paper 1804 06242 dfn learning a discriminative feature network for semantic segmentation 2018 04 cvpr 2018 paper https arxiv org abs 1804 09337 tensorflow https github com whitesockcat discriminative feature network pag pixel wise attentional gating for parsimonious pixel labeling 2018 05 wacv 2019 paper https arxiv org abs 1805 01556 x fpanet pyramid attention network for semantic segmentation 2018 05 paper https arxiv org paper 1805 10180 pytorch https github com javeywang pyramid attention networks pytorch x probabilistic u net a probabilistic u net for segmentation of ambiguous images 2018 06 neurips 2018 tensorflow https github com simonkohl probabilistic unet g frnet gated feedback refinement network for coarse to fine dense semantic image labeling 2018 06 paper https arxiv org abs 1806 11266 x ocnet ocnet object context network for scene parsing 2018 09 paper https arxiv org abs 1809 00916 pytorch https github com pkurainbow ocnet pytorch x danet dual attention network for scene segmentation 2018 09 aaai 2019 paper https arxiv org abs 1809 02983 pytorch https github com junfu1115 danet dpc searching for efficient multi scale architectures for dense image prediction 2018 09 paper https arxiv org abs 1809 04184 tensorflow https github com tensorflow models tree master research deeplab laddernet laddernet multi path networks based on u net for medical image segmentation paper https arxiv org abs 1810 07810 pytorch https github com juntang zhuang laddernet pixel augmentation pixel level data augmentation for semantic image segmentation using generative adversarial networks 2018 11 paper https arxiv org abs 1811 00174 espnetv2 espnetv2 a light weight power efficient and general purpose convolutional neural network 2018 11 paper https arxiv org abs 1811 11431 pytorch https github com sacmehta espnetv2 ccnet ccnet criss cross attention for semantic segmentation 2018 11 paper https arxiv org abs 1811 11721 pytorch https github com speedinghzl ccnet x denseaspp for semantic segmentation in street scenes cvpr 2018 paper http openaccess thecvf com content cvpr 2018 papers yang denseaspp for semantic cvpr 2018 paper pdf x drn dense relation network learning consistent and context aware representation for semantic image segmentation 2018 icip 2018 paper https ieeexplore ieee org document 8451830 mxnet1 https github com zhuangyqin drn mxnet2 https github com tonysy drn mxnet psanet psanet point wise spatial attention network for scene parsing 2018 eccv 2018 paper https hszhao github io papers eccv18 psanet pdf project https hszhao github io projects psanet caffe https github com hszhao psanet x dupsampling decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation 2019 03 cvpr 2019 paper https arxiv org abs 1903 02120 pytorch https github com linzhuochen dupsampling real time segmentation x enet enet a deep neural network architecture for real time semantic segmentation 2016 06 paper https arxiv org abs 1606 02147 caffe https github com timosaemann enet x icnet icnet for real time semantic segmentation on high resolution images 2017 04 eccv 2018 paper https arxiv org abs 1704 08545 caffe https github com hszhao icnet pytorch https github com meetshah1995 pytorch semseg blob master ptsemseg models icnet py x espnet espnet efficient spatial pyramid of dilated convolutions for semantic segmentation 2018 03 eccv 2018 paper https arxiv org abs 1803 06815 pytorch https github com sacmehta espnet x lw refinenet light weight refinenet for real time semantic segmentation 2018 10 bmvc 2018 paper https arxiv org abs 1810 03272 pytorch https github com drsleep light weight refinenet pytorch https github com deepmotionairesearch denseaspp instance segmentaion x masklab masklab instance segmentation by refining object detection with semantic and direction features 2017 12 cvpr 2018 paper https arxiv org abs 1712 04837 project https vitalab github io deep learning 2018 07 26 masklab html x panet path aggregation network for instance segmentation 2018 03 cvpr 2018 paper https arxiv org abs 1803 01534 pytorch https github com shuliu1993 panet prms weakly supervised instance segmentation using class peak response 2018 04 cvpr 2018 paper https arxiv org abs 1804 00880 pytorch https github com zhouyanzhao prm tensormask tensormask a foundation for dense object segmentation 2019 03 paper https arxiv org abs 1903 12174 panoptic segmentation x ps panoptic segmentation 2018 01 paper https arxiv org abs 1801 00868 semi supervised ps weakly and semi supervised panoptic segmentation 2018 08 eccv 2018 paper https arxiv org abs 1808 03575 matlab https github com qizhuli weakly supervised panoptic segmentation jsis net panoptic segmentation with a joint semantic and instance segmentation network 2018 09 paper https arxiv org abs 1809 02110 tascnet learning to fuse things and stuff 2018 12 paper https arxiv org abs 1812 01192 x interactive full image segmentation 2018 12 paper https arxiv org abs 1812 01888 x aunet attention guided unified network for panoptic segmentation 2018 12 paper https arxiv org abs 1812 03904 ps fpn panoptic feature pyramid networks 2019 01 paper https arxiv org abs 1901 02446 upsnet upsnet a unified panoptic segmentation network 2019 01 paper https arxiv org abs 1901 03784 losses lovaszsoftmax the lov sz softmax loss a tractable surrogate for the optimization of the intersection over union measure in neural networks 2017 05 cvpr 2018 paper https arxiv org abs 1705 08790 pytorch tensorflow https github com bermanmaxim lovaszsoftmax max pooling loss loss max pooling for semantic image segmentation 2017 04 cvpr 2017 paper https arxiv org abs 1704 02966 pytorch https github com belbes mpl pytorch x tversky loss tversky loss function for image segmentation using 3d fully convolutional deep networks 2017 06 paper https arxiv org abs 1706 05721 generalised dice generalised dice overlap as a deep learning loss function for highly unbalanced segmentations 2017 07 paper https arxiv org abs 1707 03237 neuroiou neuroiou learning a surrogate loss for semantic segmentation 2018 bmvc 2018 paper regularized losses on regularized losses for weakly supervised cnn segmentation 2018 03 eccv 2018 paper https arxiv org abs 1803 09569 pytorch caffe https github com meng tang rloss object detection object detection figs object detection png x rcnn rich feature hierarchies for accurate object detection and semantic segmentation 2013 11 cvpr 2014 paper https arxiv org abs 1311 2524 matlab https github com rbgirshick rcnn x fast r cnn fast r cnn 2015 04 iccv 2015 paper https arxiv org abs 1504 08083 caffe https github com rbgirshick fast rcnn x yolo you only look once unified real time object detection 2015 06 cvpr 2016 paper https arxiv org abs 1506 02640 project https pjreddie com darknet yolo darknet https github com pjreddie darknet pytorch https github com xiongzihua pytorch yolo v1 x ssd ssd single shot multibox detector 2015 12 eccv 2016 paper https arxiv org abs 1512 02325 caffe https arxiv org abs 1512 02325 pytorch https github com amdegroot ssd pytorch x ohem training region based object detectors with online hard example mining 2016 01 cvpr 2016 paper https arxiv org abs 1604 03540 caffe https github com abhi2610 ohem x r fcn r fcn object detection via region based fully convolutional networks 2016 05 neurips 2016 paper https arxiv org abs 1605 06409 caffe https github com daijifeng001 r fcn dag adversarial examples for semantic segmentation and object detection 2017 03 iccv 2017 paper https arxiv org abs 1703 08603 caffe https github com cihangxie dag x focal loss focal loss for dense object detection 2017 07 paper https arxiv org abs 1708 02002 dsod dsod learning deeply supervised object detectors from scratch 2017 07 iccv 2017 paper https arxiv org abs 1708 01241 caffe with ssd https github com szq0214 dsod pytorch https github com uoip ssd variants mxnet https github com eureka7mt mxnet dsod x rfbnet receptive field block net for accurate and fastobject detection 2017 11 eccv 2018 paper https arxiv org abs 1711 07767 pytorch https github com ruinmessi rfbnet cascade r cnn cascade r cnn delving into high quality object detection 2017 12 cvpr 2018 paper https arxiv org abs 1712 00726 caffe https github com zhaoweicai detectron cascade rcnn note zhihu https zhuanlan zhihu com p 41825737 x survey deep learning for generic object detection a survey 2018 09 ijcv 2018 paper https arxiv org abs 1809 02165 training from scratch rethinking imagenet pre training 2018 11 paper https arxiv org abs 1811 08883 x ghm detection gradient harmonized single stage detector 2018 11 aaai 2019 paper https arxiv org abs 1811 05181 pytorch https github com libuyu ghm detection bof bag of freebies for training object detection neural networks 2019 02 paper https arxiv org abs 1902 04103 mxnet https github com dmlc gluon cv generalized iou generalized intersection over union a metric and a loss for bounding box regression 2019 02 cvpr 2019 paper https arxiv org abs 1902 09630 anchor free x densebox densebox unifying landmark localization with end to end object detection 2015 09 paper https arxiv org abs 1509 04874 caffe https github com yangyi02 densebox pytorch https github com captaineven densebox x cornernet cornernet detecting objects as paired keypoints 2018 08 eccv 2018 paper https arxiv org abs 1808 01244 pytorch https github com princeton vl cornernet extremenet bottom up object detection by grouping extreme and center points 2019 01 cvpr 2018 paper https arxiv org abs 1901 08043 pytorch https github com xingyizhou extremenet x centernet object as points 2019 04 paper https arxiv org pdf 1904 07850 pdf pytorch https github com xingyizhou centernet x centernet centernet keypoint triplets for object detection 2019 04 cvpr2019 paper https arxiv org abs 1904 08189 pytorch https github com duankaiwen centernet image restoration super resolution x deep learning for image super resolution a survey paper https arxiv org abs 1902 06068 x edsr enhanced deep residual networks for single image super resolution 2017 7 cvpr 2017 paper https arxiv org abs 1707 02921 pytorch https github com thstkdgus35 edsr pytorch residual dense network for image super resolution 2018 02 cvpr 2018 paper https arxiv org abs 1802 08797 torch https github com yulunzhang rdn x wide activation for efficient and accurate image super resolution 2018 08 paper https arxiv org abs 1808 08718 pytorch https github com jiahuiyu wdsr ntire2018 image caption x show attend and tell show attend and tell neural image caption generation with visual attention 2015 paper https arxiv org abs 1502 03044 tensorflow https github com yunjey show attend and tell pytorch https github com sgrvinod a pytorch tutorial to image captioning x image captioning with semantic attention 2016 paper https arxiv org abs 1603 03925 torch https github com chapternewscu image captioning with semantic attention x bottom up and top down attention for image captioning and visual question answering 2017 paper https arxiv org abs 1707 07998 caffe https github com peteanderson80 bottom up attention x convolutional image captioning 2017 paper https arxiv org abs 1711 09151 pytorch https github com aditya12agd5 convcap cnn cnn convolutional decoders for image captioning 2018 paper https arxiv org abs 1805 09019 generative adversarial networks x dcgan unsupervised representation learning with deep convolutional generative adversarial networks 2015 11 iclr 2016 paper https arxiv org abs 1511 06434 pytorch https github com pytorch examples tree master dcgan tensorflow https github com carpedm20 dcgan tensorflow torch https github com soumith dcgan torch munit multimodal unsupervised image to image translation 2018 04 eccv 2018 paper https arxiv org abs 1804 04732 pytorch https github com nvlabs munit sagan self attention generative adversarial networks 2018 05 paper https arxiv org abs 1805 08318 pytorch https github com heykeetae self attention gan dirt diverse image to image translation via disentangled representations 2018 08 paper https arxiv org abs 1808 00948 pytorch https github com hsinyinglee drit font color gray notes maybe suitable for unpaired mr ct synthesis for human body font x vid2vid video to video synthesis 2018 08 paper https arxiv org abs 1808 06601 pytorch https github com nvidia vid2vid x biggan large scale gan training for high fidelity natural image synthesis 2018 09 paper https arxiv org abs 1809 11096 pytorch https github com aaronleong biggan pytorch stylegan a style based generator architecture for generative adversarial networks 2018 12 paper https arxiv org abs 1812 04948 attention mechanism x show attend and tell neural image caption generation with visual attention 2015 02 paper https arxiv org abs 1502 03044 tensorflow https github com yunjey show attend and tell pytorch https github com sgrvinod a pytorch tutorial to image captioning x image captioning with semantic attention 2016 03 paper https arxiv org abs 1603 03925 torch https github com chapternewscu image captioning with semantic attention x attention is all you need 2017 06 paper https arxiv org abs 1706 03762 pytorch https github com jadore801120 attention is all you need pytorch tensorflow https github com kyubyong transformer x bottom up and top down attention for image captioning and visual question answering 2017 07 paper https arxiv org abs 1707 07998 caffe https github com peteanderson80 bottom up attention x attention u net learning where to look for the pancreas 2018 04 paper https arxiv org abs 1804 03999 pytorch https github com ozan oktay attention gated networks x self attention generative adversarial networks 2018 05 paper https arxiv org abs 1805 08318 pytorch https github com heykeetae self attention gan notes gan learning visual question answering by bootstrapping hard attention 2018 08 paper https arxiv org abs 1808 00300 pytorch https github com gnouhp pytorch adahan note hard attention pervasive attention 2d convolutional neural networks for sequence to sequence prediction 2018 08 paper https arxiv org abs 1808 03867 pytorch https github com elbayadm attn2d natural language processing related x pervasive attention pervasive attention 2d convolutional neural networks for sequence to sequence prediction 2018 08 conll 2018 paper https arxiv org abs 1808 03867 pytorch https github com elbayadm attn2d medical image analysis x rician normalization normalization of t2w mri prostate images using rician a priori medical imaging 2016 computer aided diagnosis paper http adsabs harvard edu abs 2016spie 9785e 29l x u net u net convolutional networks for biomedical image segmentation 2015 05 paper https arxiv org abs 1505 04597 pytorch https github com milesial pytorch unet pytorch hsu https github com hsuxu carvana pytorch unet x v net v net fully convolutional neural networks for volumetric medical image segmentation 2016 06 paper https arxiv org abs 1606 04797 pytorch https github com mattmacy vnet pytorch pytorch hsu https github com hsuxu magic vnet x xmasnet prostate cancer diagnosis using deep learning with 3d multiparametric mri 2017 03 medical imaging computer aided diagnosis 2017 paper https arxiv org abs 1703 04078 x tversky loss tversky loss function for image segmentation using 3d fully convolutional deep networks 2017 06 paper https arxiv org abs 1706 05721 tdn automated detection of clinically significant prostate cancer in mp mri images based on an end to end deep neural network 2018 07 tmi 2018 paper https ieeexplore ieee org iel7 42 8353174 08245842 pdf x anatomynet anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy 2018 08 paper https arxiv org abs 1808 05238 x improving data augmentation for medical image segmentation 2018 12 midl 2018 paper https openreview net forum id rkbbchjig x computer aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric mri jmri 2018 paper https www ncbi nlm nih gov pubmed 29659067 ensemble a new ensemble learning framework for 3d biomedical image segmentation 2018 12 aaai 2019 paper https arxiv org abs 1812 03945 code https github com haozheng94 ensemble multiresunet multiresunet rethinking the u net architecture for multimodal biomedical image segmentation 2019 02 cvpr 2019 paper https arxiv org abs 1902 04049 tensorflow https github com nibtehaz multiresunet data augmentation using learned transforms for one shot medical image segmentation 2019 02 cvpr 2019 paper https arxiv org abs 1902 09383 tensorflow https github com xamyzhao brainstorm other applications pose estimation deeppose deeppose human pose estimation via deep neural networks 2013 11 cvpr 2014 paper https arxiv org abs 1312 4659 chainer https github com mitmul deeppose x hourglass stacked hourglass networks for human pose estimation 2016 03 eccv 2016 paper https arxiv org abs 1603 06937 torch https github com anewell pose hg train simple baselines simple baselines for human pose estimation and tracking 2018 04 eccv 2018 paper https arxiv org abs 1804 06208 pytorch https github com microsoft human pose estimation pytorch x mspn rethinking on multi stage networks for human pose estimation 2019 01 paper https arxiv org abs 1901 00148 training tricks x bag of tricks bag of tricks for image classification with convolutional neural networks 2018 12 paper https arxiv org abs 1812 01187 data augmentation x pairing samples data augmentation by pairing samples for images classification 2018 01 paper https arxiv org abs 1801 02929 autoaugment autoaugment learning augmentation policies from data 2018 05 paper https arxiv org abs 1805 09501 x albumentations albumentations fast and flexible image augmentations 2018 09 paper https arxiv org abs 1809 06839 pixel augmentation pixel level data augmentation for semantic image segmentation using generative adversarial networks 2018 11 paper https arxiv org abs 1811 00174 data augmentation using learned transforms for one shot medical image segmentation 2019 02 cvpr 2019 paper https arxiv org abs 1902 09383 tensorflow https github com xamyzhao brainstorm normalization x batch normalization batch normalization accelerating deep network training by reducing internal covariate shift 2015 02 paper https arxiv org abs 1502 03167 x layer normalization layer normalization 2016 07 paper https arxiv org abs 1607 06450 x instance normalization instance normalization the missing ingredient for fast stylization 2016 07 paper https arxiv org abs 1607 08022 x group normalization group normalization 2018 03 paper https arxiv org abs 1803 08494 tensorflow https github com shaohua0116 group normalization tensorflow x how does batch normalization help optimization 2018 05 neurips 2018 paper https arxiv org abs 1805 11604 switchable normalization differentiable learning to normalize via switchable normalization 2018 06 paper https arxiv org abs 1806 10779 pytorch https github com switchablenorms switchable normalization x synchronized batchnorm pytorch https github com vacancy synchronized batchnorm pytorch mxnet https github com zhanghang1989 mxnet gluon syncbn initialization x delving deep into rectifiers surpassing human level performance on imagenet classification 2015 02 paper https arxiv org abs 1502 01852 x understanding the difficulty of training deep feedforward neural networks paper http proceedings mlr press v9 glorot10a html x training techniques an overview of gradient descent optimization algorithms url http ruder io optimizing gradient descent index html x zeroinit residual learning without normalization via better initialization 2018 09 iclr2019 paper https openreview net forum id h1gsz30ckx automl learning to optimize 2016 06 paper https arxiv org abs 1606 01885 neural architecture search with reinforcement learning 2016 11 paper https arxiv org abs 1611 01578 metaqnn designing neural network architectures using reinforcement learning 2016 11 iclr 2017 paper https arxiv org abs 1611 02167 large scale evolution large scale evolution of image classifiers 2017 03 icml 2017 paper https arxiv org abs 1703 01041 genetic cnn genetic cnn paper https arxiv org abs 1703 01513 eas efficient architecture search by network transformation 2017 07 aaai 2018 paper https arxiv org abs 1707 04873 tensorflow https github com han cai eas x nasnet learning transferable architectures for scalable image recognition 2017 07 paper https arxiv org abs 1707 07012 tensorflow https github com tensorflow models tree master research slim nets nasnet hierarchical representations for efficient architecture search 2017 11 iclr 2018 paper https arxiv org abs 1711 00436 amoebanet a regularized evolution for image classifier architecture search 2018 02 paper https arxiv org abs 1802 01548 x enas efficient neural architecture search via parameter sharing 2018 02 icml 2018 paper https arxiv org abs 1802 03268 tensorflow https github com melodyguan enas pytorch https github com carpedm20 enas pytorch dataset and contest dataset pascal voc 2012 http host robots ox ac uk pascal voc the main goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes i e not pre segmented objects it is fundamentally a supervised learning learning problem in that a training set of labelled images is provided cityscapes https www cityscapes dataset com large scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities with high quality pixel level annotations of 5 000 frames in addition to a larger set of 20 000 weakly annotated frames nyuv2 https cs nyu edu silberman datasets nyu depth v2 html the nyu depth v2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the rgb and depth cameras from the microsoft kinect pascal context https cs stanford edu roozbeh pascal context this dataset is a set of additional annotations for pascal voc 2010 it goes beyond the original pascal semantic segmentation task by providing annotations for the whole scene the statistics section has a full list of 400 labels sunrgbd http rgbd cs princeton edu dataset is captured by four different sensors and contains 10 335 rgb d images at a similar scale as pascal voc the whole dataset is densely annotated and includes 146 617 2d polygons and 64 595 3d bounding boxes with accurate object orientations as well as a 3d room layout and scene category for each image ade20k http groups csail mit edu vision datasets ade20k ade20k dataset contains more than 20k scene centric images exhaustively annotated with objects and object parts specifically the benchmark is divided into 20k images for training 2k images for validation and another batch of held out images for testing there are totally 150 semantic categories included for evaluation which include stuffs like sky road grass and discrete objects like person car bed note that there are non uniform distribution of objects occuring in the images mimicking a more natural object occurrence in daily scene grand challenges in biomedical image analysis https grand challenge org grand challenges in biomedical image analysis contest ai challenger https challenger ai | research papers deep-learning computer-vision machine-learning semantic-segmentation image-recognition object-detection image-classification image-caption gan generative-adversarial-network medical-image-computing pytorch tensorflow papanoptic-segmentation instance-segmentation | ai |
LLM-Client | a name readme top a codegpt llm client a user friendly java http client providing access to the large language model llm apis and services contributions welcome contributions welcome svg contributions welcome maven maven shield maven url installation to use the package you need to use following maven dependency xml dependency groupid ee carlrobert groupid artifactid llm client artifactid version 0 0 6 version dependency gradle dependency kts dependencies implementation ee carlrobert llm client 0 0 6 usage chat completion sse java openaiclient client new openaiclient builder system getenv openai api key setorganization my organization build eventsource call client getchatcompletion new openaichatcompletionrequest builder list of new openaichatcompletionmessage user prompt setmodel openaichatcompletionmodel gpt 4 settemperature 0 1 build new completioneventlistener override public void onmessage string message system out println message call cancel contributing contributions are what make the open source community such an amazing place to learn inspire and create any contributions you make are greatly appreciated if you have a suggestion that would make this better please fork the repo and create a pull request you can also simply open an issue with the tag enhancement 1 fork the project 2 create your feature branch git checkout b feature amazingfeature 3 commit your changes git commit m add some amazingfeature 4 push to the branch git push origin feature amazingfeature 5 open a pull request license this is an unofficial openai library and is not associated with or endorsed by openai mit carl robert linnupuu portfolio markdown links images https www markdownguide org basic syntax reference style links contributions welcome svg http img shields io badge contributions welcome brightgreen contributions welcome https github com jetbrains ideavim blob master contributing md maven shield https img shields io maven central v ee carlrobert llm client maven url https central sonatype com namespace ee carlrobert portfolio https carlrobert ee | ai |
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job-portal-mobile-application | job portal mobile application welcome to the job portal system this is our university group project under the mobile application development module we have created this mobile application using kotlin language on android studio there is a four main parts as 1 user management 2 post job vacancies 3 apply for jobs 4 payment portal login sign up user management c reate user should register with the app if he she is not registered user can register to the system by filling out the sign up form user should enter a valid email and should not be registered in the system before after entering the password twice the user can register to the system and the user profile gets created then the user can login with the valid credentials r ead the entered details can be shown in the user profile u pdate if the user wants to change the user details except email then user can edit and the user profile gets updated d elete if user wants to delete the profile he she can delete it when the user enter the credentials of that profile and try to login the system shows the email or password is incorrect that means the profile has been deleted screenshot 29 https github com keshala09 job portal mobile application assets 105196447 8206326b 8128 4c1d 87a8 27a8235238bd post job vacancies c reate after user login with the system user can post a job by filling out this form customer should have to fill all the fields otherwise he she cannot continue after confirming the job ad post is successfully created r ead the entered details in the form are shown in the your ads page u pdate the user can update any detail in the job he she posted d elete the user can delete the job posted and once the user deleted it user cannot see the job in the your ads page screenshot 30 https github com keshala09 job portal mobile application assets 105196447 30e498a5 6c07 45c4 a49e fb540aa3dea5 apply for job c reate when the user is logged into the system he she can select a job category then system shows the jobs related to selected category then user can select a job to apply after that user should have to fill out the form and upload the cv all the fields should be filled system doesn t allow to go to the next page with empty fields r ead then use can check the details that he entered u pdate then the user can edit the details if necessary if there are nothing to edit user can apply the job successfully d elete user can delete the uploaded file if the user has uploaded wrong file screenshot 32 https github com keshala09 job portal mobile application assets 105196447 a7d18436 8405 4e36 835b c9861e239dfc payment portal c reate if a user wants to post a job he she needs to do a payment for that user can go to the payment portal and enter the payment method and the number of ads the user wishes to post when the user enters the number of ads system calculates the total amount to be paid then the user should pay the amount and upload a payment proof r ead user can see the details that he entered and the calculated amount to be paid after uploading the payment proof and then by clicking confirm the user can complete the payment process u pdate if the user wants to edit the details entered he she can edit them and again the total amount to be paid will be calculated and updated then again user must upload the payment proof d elete user can delete the uploaded file if the user has uploaded wrong file of payment proof screenshot 33 https github com keshala09 job portal mobile application assets 105196447 b9a4ae13 7c0c 4de1 9116 14da5484de25 thank you | kotlin android-studio xml | front_end |
KalangoRTOS | kalango a always experimental just for fun rtos simple preemptive cooperative realtime multitask kernel made just for fun and aims to be used to learn basics and the internals of multitasking programming on microcontrollers the kernel is engineered to be simple and scalable allowing others to download use learn and scale it into more professional projects it is under development status and all updates will figure here main features real time preemptive scheduler fast and predictable execution time context switching supports up to 32 priority levels round robin policy with same priority threads soft timers counting semaphores binary semaphores task management recursive mutexes message queues scalable user can configure how much kernel objects application need unlimited kernel objects and threads limited by processor memory o 1 tlsf memory allocator leave kernel to manage its memory written in c with less possible assembly paths just on context switching limitations please keep in mind this is an experimental project intended to support popular 32bit microcontrollers no plan to support 8 bit platforms most of the code are written with gcc or clang in mind no c support it was designed to take advantage of exisiting manufacturers microcontroller abstraction libraries such cmsis and nrfx timer callbacks are deffered from isr get the code to get this respository git clone https github com ulipe kalangortos getting started using cmake you can use the cmake build system to integrate the kalango into your existing cmake project to do so just copy this folder into your project folder and in your cmake project search by this directory as below add subdirectory kalangortos after that in your executable target just link kalango library using regular cmake option target link libraries your executable target kalangortos other libraries the kalango top level include will placed in your project and you can invoke all the kalango related functions additionally you need to supply a kalango config h header file inside of b confs b folder there are some samples just copy to your project and rename it to b kalango config h b after that when running your project cmake command just supply the location of this file cmake source directory other arguments dkalango config file path path to kalango config h file getting started stand alone mode on your embedded project add b include b folder to your include search path add b src b desired b arch b folder and b lib b folders to search source path from confs board take a template config and put in your project rename it to kalango config h add to your compiling options include path to kalango config h file include the kalango api h on your application code to use rtos features inside of your main function initialize your target then run the scheduler by calling b kalango corestart b function see example below include kalango api h static taskid task a static taskid task b static void demotask1 void arg uint32 t noof wakeups 0 for kalango sleep 250 noof wakeups static void demotask2 void arg uint32 t noof wakeups 0 for kalango sleep 25 noof wakeups int main void tasksettings settings settings arg null settings function demotask1 settings priority 8 settings stack size 512 task a kalango taskcreate settings settings arg null settings function demotask2 settings priority 4 settings stack size 512 task b kalango taskcreate settings void task a void task b start scheduling kalango corestart return 0 support if you want some help with this work give a star and contact me ryukokki felipe gmail com | os |
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nlp-exercises | nlp exercises this repository contains my answers to exercises from natural language processing with python analyzing text with the natural language toolkit steven bird ewan klein and edward loper o reilly media 2009 the book is available online here http nltk org book it s a fascinating text with plenty of neat examples | ai |
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monorepo | div align center project logo a href https github com connext monorepo img src https github com connext brand blob main connext logo blacktext multicolor png raw true alt logo width 320 height 80 a test sops sops shield sops url deploy testnet deploy testnet shield deploy testnet url build test deploy build test deploy shield build test deploy url discord discord shield discord url twitter twitter shield twitter url br h3 align center about connext h3 h4 align center connext is public infrastructure powering fast trust minimized communication between blockchains h4 p align center useful links br a href https docs connext network strong explore the docs strong a br br a href https bridge connext network view mainnet bridge a br a href https testnet bridge connext network view testnet bridge connext amarok a br br a href https github com connext monorepo issues report bug a br a href https immunefi com bounty connext bug bounty program a br br a href https github com connext monorepo issues request feature a p div table of contents details summary table of contents summary ol li a href about connext about connext a ul li a href built with architecture a li ul li li a href getting started getting started a ul li a href prerequisites prerequisites a li li a href installation installation a li ul li li a href contributing contributing a li li a href license license a li li a href contact contact a li li a href acknowledgments acknowledgments a li ol details about the project connext architecture the connext architecture can be seen as a layered system as follows layer protocol stakeholders application layer crosschain applications xapps sdk liquidity layer routers sequencer messaging execution layer lighthouse sequencer messaging verification layer watcher messaging transport layer ambs about connext connext is a modular stack for trust minimized generalized communication between blockchains read more https docs connext network core concepts understanding connext architecture adapters https github com connext monorepo tree main packages adapters wrappers around external modules these adapters can be shared between different packages cache https github com connext monorepo tree main packages adapters cache is a wrapper around all the redis based caches that are used database https github com connext monorepo tree main packages adapters database is implementation of schema and client for the database subrgaph https github com connext monorepo tree main packages adapters subgraph includes graphclient implementation and reader functions for subgraph txservice https github com connext monorepo tree main packages adapters txservice resiliently attempts to send transactions to chain with retries etc and is used to read and write to rpc providers and has fallback providers if needed fallbacks can be defined as arrays and this way we can provide resiliency in case of failure web3signer https github com connext monorepo tree main packages adapters web3signer is a wrapper around web3signer which is a secure way of signing which does not require to include mnemonics in the app itself agents https github com connext monorepo tree main packages agents core infra hosted services for functionality and ux cartographer https github com connext monorepo tree main packages agents cartographer is our chain indexer which indexes from subgraph and provides an api to query raw and computed data lighthouse https github com connext monorepo tree main packages agents lighthouse is an implementation for execution layer relayer https github com connext monorepo tree main packages agents relayer is an implementation of a relayer in case we can t use gelato router https github com connext monorepo tree main packages router listens for events from messaging service and subgraph and then dispatches transactions to txservice sdk https github com connext monorepo tree main packages agents sdk is a js wrapper around the contract calls themselves and can be used by integrations sequencer https github com connext monorepo tree main packages agents sequencer is the agent module which is in charge of sourcing bids from routers and puts fast liquidity bids onto the chain itself deployments https github com connext monorepo tree main packages deployments contracts https github com connext monorepo tree main packages deployments contracts contracts are the contracts that we deploy and the deployment scripts subgraph https github com connext monorepo tree main packages deployments subgraph is all the subgraph source code to define all the mappings and contains all the configurations to deploy to different graph hosted services or third party graph providers examples https github com connext monorepo tree main packages examples these are not used in production but contains ways to use the sdk that are illustrative of how to integrate connext integration https github com connext monorepo tree main packages integration utilities for integration test utils https github com connext monorepo tree main packages utils collection of helper functions that are shared throughout the different packages p align right a href top back to top a p first time setup use node version 18 x and make sure you are on the latest yarn version yarn set version berry to set up the containers use docker if docker is not already installed on your system you can easily install it by clicking here https www docker com try running yarn to update everything if you have issues try deleting node modules and yarn lock after deleting yarn lock run touch yarn lock since it does not like if there is no lock file dev environment setup environment by initiating the build yarn yarn build all here yarn install deps create symlinks hoist packages yarn build all build all packages to run rabbitmq with the management plugin using docker run the following command docker run it rm name rabbitmq p 5672 5672 p 15672 15672 rabbitmq 3 10 management this command will download the latest rabbitmq image with the management plugin and start a container with the name rabbitmq to run redis execute the following command docker run it rm name redis p 6379 6379 redis this command will download the latest redis image and start a container with the name redis and now you are all ready to interact with monorepo individual commands can be run against workspaces as so example for nxtp utils package yarn workspace connext nxtp utils test you should be able to do everything from the root and not need to go into the individual package dirs for example adding an npm package yarn workspace connext nxtp txservice add ethers run router yarn workspace connext nxtp router dev runs router in hot reload mode running test yarn install deps create symlinks hoist packages yarn build all build all packages or yarn workspace connext smart contracts build build the specific package run test yarn workspace connext smart contracts test runs test adding packages to add a new package that can be shared by the rest of the repo you can use some convenience scripts that we have installed yarn tsp create connext test lib template node lib note the tsp tool is not required it just makes boilerplate generation easier if you want you can copy paste stuff from other packages documentation on the tool is here https github com jtbennett create ts project blob main docs tsp commands md to add the lib to be a dependency of a consuming app i e the router yarn tsp add connext test lib cwd packages router again this can all be done without the tool all it does is add some files and make some config changes note we use node lib as the template for all the packages there are some other included templates like browser lib which didn t work with our bundling we might need to revisit things for bundling reqs publishing packages update the changelog md changelog md run yarn version all x x x where x x x is the full version string of the npm version to deploy i e 0 0 1 use x x x beta n for amarok releases from production branch and x x x alpha n for amarok releases from main branch commit and add a tag matching the version git commit am version git tag am version run git push follow tags the github action will github workflows build docker image and verify yml publish the packages by recognizing the version tag contributing contributing contributions are what makes the open source community such an amazing place to learn inspire and create any contributions you make are greatly appreciated if you have a suggestion that would make this better please fork the repo and create a pull request you can also simply open an issue with the tag enhancement don t forget to give the project a star thanks again 1 fork the project 2 create your feature branch git checkout b feature amazingfeature 3 commit your changes git commit m add some amazingfeature 4 push to the branch git push origin feature amazingfeature 5 open a pull request p align right a href top back to top a p license license distributed under the mit license see license txt for more information p align right a href top back to top a p project link https github com connext monorepo https github com connext monorepo p align right a href top back to top a p markdown links images https www markdownguide org basic syntax reference style links sops shield https github com connext monorepo actions workflows test sops yaml badge svg sops url https github com connext monorepo actions workflows test sops yaml deploy testnet shield https github com connext monorepo actions workflows deploy testnet yaml badge svg deploy testnet url https github com connext monorepo actions workflows deploy testnet yaml build test deploy shield https github com connext monorepo actions workflows build test deploy yml badge svg build test deploy url https github com connext monorepo actions workflows build test deploy yml discord shield https img shields io discord 454734546869551114 logo discord discord url https discord gg m93sqf4 twitter shield https img shields io twitter follow connextnetwork style social twitter url https twitter com connextnetwork | ethereum interoperability crosschain protocol blockchain connext | blockchain |
azure-search-openai-javascript | page type sample languages javascript typescript nodejs products azure azure cognitive services azure openai urlfragment azure search openai javascript chatgpt enterprise data with azure openai and cognitive search table of contents features features getting started getting started azure account requirements azure account requirements azure deployment azure deployment cost estimation cost estimation project setup project setup github codespaces github codespaces vs code remote containers vs code remote containers local environment local environment deploying from scratch deploying from scratch deploying with existing resources deploying with existing resources deploying again deploying again sharing environments sharing environments enabling optional features enabling optional features enabling application insights enabling application insights enabling authentication enabling authentication using the app using the app running locally running locally using a different backend using a different backend productionizing productionizing clean up clean up resources resources note note faq faq troubleshooting troubleshooting open in github codespaces https img shields io static v1 style for the badge label github codespaces message open color brightgreen logo github https github com codespaces new hide repo select true ref main repo 599293758 machine standardlinux32gb devcontainer path devcontainer 2fdevcontainer json location westus2 open in remote containers https img shields io static v1 style for the badge label remote 20 20containers message open color blue logo visualstudiocode https vscode dev redirect url vscode ms vscode remote remote containers cloneinvolume url https github com azure samples azure search openai javascript this sample demonstrates a few approaches for creating chatgpt like experiences over your own data using the retrieval augmented generation pattern it uses azure openai service to access the chatgpt model gpt 35 turbo and azure cognitive search for data indexing and retrieval retrieval augmented generation architecture docs rag architecture png the repo includes sample data so it s ready to try end to end in this sample application we use a fictitious company called contoso real estate and the experience allows its customers to ask support questions about the usage of its products the sample data includes a set of documents that describe its terms of service privacy policy and a support guide the application is made from multiple components including search service the backend service that provides the search and retrieval capabilities indexer service the service that indexes the data and creates the search indexes web app the frontend web application that provides the user interface and orchestrates the interaction between the user and the backend services app architecture docs app architecture drawio png features chat and q a interfaces explores various options to help users evaluate the trustworthiness of responses with citations tracking of source content etc shows possible approaches for data preparation prompt construction and orchestration of interaction between model chatgpt and retriever cognitive search settings directly in the ux to tweak the behavior and experiment with options optional performance tracing and monitoring with application insights chat screen docs chat screenshot png getting started azure account prerequisites important in order to deploy and run this sample you ll need azure account if you re new to azure get an azure account for free https azure microsoft com free cognitive search to get free azure credits to get started azure subscription with access enabled for the azure openai service you can request access with this form https aka ms oaiapply azure account permissions your azure account must have microsoft authorization roleassignments write permissions such as role based access control administrator https learn microsoft com azure role based access control built in roles role based access control administrator preview user access administrator https learn microsoft com azure role based access control built in roles user access administrator or owner https learn microsoft com azure role based access control built in roles owner if you don t have subscription level permissions they must be granted to you with rbac https learn microsoft com azure role based access control built in roles role based access control administrator preview for an existing resource group and deploy to that existing group existing resource group your azure account also needs microsoft resources deployments write permissions at a subscription level azure deployment cost estimation pricing may vary per region and usage exact costs cannot be estimated you may try the azure pricing calculator https azure com e 8ffbe5b1919c4c72aed89b022294df76 for the resources below azure container apps pay as you go tier costs based on vcpu and memory used pricing https azure microsoft com pricing details container apps azure static web apps free tier pricing https azure microsoft com pricing details app service static azure openai standard tier chatgpt and ada models pricing per 1k tokens used and at least 1k tokens are used per question pricing https azure microsoft com en us pricing details cognitive services openai service form recognizer so standard tier using pre built layout pricing per document page sample documents have 261 pages total pricing https azure microsoft com pricing details form recognizer azure cognitive search standard tier 1 replica free level of semantic search pricing per hour pricing https azure microsoft com pricing details search azure blob storage standard tier with zrs zone redundant storage pricing per storage and read operations pricing https azure microsoft com pricing details storage blobs azure monitor pay as you go tier costs based on data ingested pricing https azure microsoft com pricing details monitor to avoid unnecessary costs remember to take down your app if it s no longer in use either by deleting the resource group in the portal or running azd down project setup there are multiple ways to successfully setup this project the easiest way to get started is with github codespacesm that provides preconfigurations to setup all the tools for you read more below github codespaces alternatively you can set up your local environment local environment follwing the instructions below github codespaces you can run this repo virtually by using github codespaces which will open a web based vs code in your browser open in github codespaces https img shields io static v1 style for the badge label github codespaces message open color brightgreen logo github https github com codespaces new hide repo select true ref main repo 599293758 machine standardlinux32gb devcontainer path devcontainer 2fdevcontainer json location westus2 vs code remote containers a similar option to codespaces is vs code remote containers that will open the project in your local vs code instance using the dev containers extension https marketplace visualstudio com items itemname ms vscode remote remote containers open in remote containers https img shields io static v1 style for the badge label remote 20 20containers message open color blue logo visualstudiocode https vscode dev redirect url vscode ms vscode remote remote containers cloneinvolume url https github com azure samples azure search openai javascript local environment azure developer cli https aka ms azure dev install node js 18 https nodejs org en download docker for desktop https www docker com products docker desktop git https git scm com downloads powershell 7 pwsh https github com powershell powershell for windows users only important ensure you can run pwsh exe from a powershell command if this fails you likely need to upgrade powershell then get the project code 1 create a new folder and switch to it in the terminal 1 run azd auth login 1 run azd init t azure search openai javascript note that this command will initialize a git repository and you do not need to clone this repository deploying from scratch execute the following command if you don t have any pre existing azure services and want to start from a fresh deployment 1 run azd up this will provision azure resources and deploy this sample to those resources including building the search index based on the files found in the data folder you will be prompted to select a location for the majority of resources except for the openai and static web app resources by default the openai resource will be deployed to eastus2 you can set a different location with azd env set azure openai resource group location location currently only a short list of locations is accepted that location list is based on the openai model availability table https learn microsoft com azure cognitive services openai concepts models model summary table and region availability and may become outdated as availability changes by default the staic web app resource will be deployed to eastus2 you can set a different location with azd env set azure webapp location location currently only a short list of locations is accepted note that static web app is a global service and the location you choose will only affect the managed functions app which is not used in this sample 1 after the application has been successfully deployed you will see a url printed to the console click that url to interact with the application in your browser it will look like the following output from running azd up docs deployment png note it can take 15 minutes for the application to be fully deployed deploying with existing resources if you already have existing azure resources you can re use those by setting azd environment values existing resource group 1 run azd env set azure resource group name of existing resource group 1 run azd env set azure location location of existing resource group existing openai resource 1 run azd env set azure openai service name of existing openai service 1 run azd env set azure openai resource group name of existing resource group that openai service is provisioned to 1 run azd env set azure openai chatgpt deployment name of existing chatgpt deployment only needed if your chatgpt deployment is not the default chat 1 run azd env set azure openai emb deployment name of existing gpt embedding deployment only needed if your embeddings deployment is not the default embedding existing azure cognitive search resource 1 run azd env set azure search service name of existing azure cognitive search service 1 run azd env set azure search service resource group name of existing resource group with acs service 1 if that resource group is in a different location than the one you ll pick for the azd up step then run azd env set azure search service location location of existing service 1 if the search service s sku is not standard then run azd env set azure search service sku name of sku the free tier won t work as it doesn t support managed identity see other possible values https learn microsoft com azure templates microsoft search searchservices pivots deployment language bicep sku other existing azure resources you can also use existing form recognizer and storage accounts see infra main parameters json for list of environment variables to pass to azd env set to configure those existing resources provision remaining resources now you can run azd up following the steps in deploying from scratch deploying from scratch above that will both provision resources and deploy the code deploying again if you ve only changed the backend frontend code in the app folder then you don t need to re provision the azure resources you can just run azd deploy if you ve changed the infrastructure files infra folder or azure yaml then you ll need to re provision the azure resources you can do that by running azd up sharing environments to give someone else access to a completely deployed and existing environment either you or they can follow these steps 1 install the azure developer cli https aka ms azure dev install 1 run azd init t azure search openai javascript or clone this repository 1 run azd env refresh e environment name they will need the azd environment name subscription id and location to run this command you can find those values in your azure env name env file this will populate their azd environment s env file with all the settings needed to run the app locally 1 set the environment variable azure principal id either in that env file or in the active shell to their azure id which they can get with az ad signed in user show 1 run scripts roles ps1 or scripts roles sh to assign all of the necessary roles to the user if they do not have the necessary permission to create roles in the subscription then you may need to run this script for them once the script runs they should be able to run the app locally enabling optional features enabling application insights to enable application insights and the tracing of each request along with the logging of errors set the azure use application insights variable to true before running azd up 1 run azd env set azure use application insights true 1 run azd up to see the performance data go to the application insights resource in your resource group click on the investigate performance blade and navigate to any http request to see the timing data to inspect the performance of chat requests use the drill into samples button to see end to end traces of all the api calls made for any chat request tracing screenshot docs transaction tracing png to see any exceptions and server errors navigate to the investigate failures blade and use the filtering tools to locate a specific exception you can see python stack traces on the right hand side enabling authentication by default the deployed azure web app will have no authentication or access restrictions enabled meaning anyone with routable network access to the web app can chat with your indexed data you can require authentication to your azure active directory by following the add app authentication https learn microsoft com training modules publish static web app authentication tutorial and set it up against the deployed web app to then limit access to a specific set of users or groups you can follow the steps from restrict your azure ad app to a set of users https learn microsoft com azure active directory develop howto restrict your app to a set of users by changing assignment required option under the enterprise application and then assigning users groups access users not granted explicit access will receive the error message aadsts50105 your administrator has configured the application app name to block users unless they are specifically granted assigned access to the application enabling cors for an alternate frontend by default the deployed search api will only allow requests from the same origin as the deployed web app origin to enable cors https developer mozilla org docs web http cors for a frontend hosted on a different origin run 1 run azd env set allowed origin https your domain com 2 run azd up running locally you can only run locally after having successfully run the azd up command 1 run azd auth login 2 run azd env get values env to get the environment variables for the app 3 run npm start or run the vs code task start app to start the project locally using the app in azure navigate to the azure static web app deployed by azd the url is printed out when azd completes as endpoint or you can find it in the azure portal running locally navigate to http 127 0 0 1 5173 once in the web app try different topics in chat or q a context for chat try follow up questions clarifications ask to simplify or elaborate on answer etc explore citations and sources click on settings to try different options tweak prompts etc using a different backend the search api service implements the azureml chat backend protocol https github com azure azureml run specification blob chat protocol specs chat protocol chat app protocol md it can be swapped with any service that implements the same protocol like the python backend client in this repository https github com azure samples azure search openai demo instead of the node js implementation featured in this repo to do so follow these steps 1 deploy this repository following the steps above 2 get the frontend url if you want to use the deployed web app run azd env get values grep webapp uri to get the url if you want to use the local web app use http localhost 5173 if you want to use the codespaces local web app use https your codespace base url 5173 app github dev 3 open the alternative backend repository your want to use for example https github com azure samples azure search openai demo 4 set the frontend url as an allowed origin with azd env set allowed origin your frontend url 5 follow the steps to deploy the python backend https github com azure samples azure search openai demo deploying from scratch 6 once the python backend is fully deployed get the backend url with azd env get values grep backend uri 7 set the backend url in this repo running azd env set backend uri your backend url 8 depending on whether you want to use the deployed web app or the local web app if you want to use the deployed web app run azd up to redeploy if you want to use the local web app on your machine or in codespaces run sh export the environment variable the syntax may be different depending on your shell or if you re using windows export backend uri your backend url start the app npm start workspace webapp productionizing this sample is designed to be a starting point for your own production application but you should do a thorough review of the security and performance before deploying to production here are some things to consider openai capacity the default tpm tokens per minute is set to 30k that is equivalent to approximately 30 conversations per minute assuming 1k per user message response you can increase the capacity by changing the chatgptdeploymentcapacity and embeddingdeploymentcapacity parameters in infra main bicep to your account s maximum capacity you can also view the quotas tab in azure openai studio https oai azure com to understand how much capacity you have azure storage the default storage account uses the standard lrs sku to improve your resiliency we recommend using standard zrs for production deployments which you can specify using the sku property under the storage module in infra main bicep azure cognitive search the default search service uses the standard sku with the free semantic search option which gives you 1000 free queries a month assuming your app will experience more than 1000 questions you should either change semanticsearch to standard or disable semantic search entirely in the request options if you see errors about search service capacity being exceeded you may find it helpful to increase the number of replicas by changing replicacount in infra core search search services bicep or manually scaling it from the azure portal azure container apps the default container app setup uses 1 vcpu core and 2 gb ram per container with autoscaling enabled the minimum number of replicas is set to 1 and the maximum to 10 you can change vcpu and ram capacity in the template https github com azure samples azure search openai javascript blob main infra main bicep l144 l145 and define your own auto scaling rules based on load for more details read set scaling rules in azure container apps https learn microsoft com en us azure container apps scale app pivots azure resource manager authentication by default the deployed app is publicly accessible we recommend restricting access to authenticated users see enabling authentication enabling authentication above for how to enable authentication networking we recommend deploying inside a virtual network if the app is only for internal enterprise use use a private dns zone also consider using azure api management apim for firewalls and other forms of protection for more details read azure openai landing zone reference architecture https techcommunity microsoft com t5 azure architecture blog azure openai landing zone reference architecture ba p 3882102 loadtesting we recommend running a loadtest for your expected number of users you can use the locust tool https docs locust io with the locustfile py in this sample or set up a loadtest with azure load testing resources revolutionize your enterprise data with chatgpt next gen apps w azure openai and cognitive search https aka ms entgptsearchblog azure cognitive search https learn microsoft com azure search search what is azure search azure openai service https learn microsoft com azure cognitive services openai overview clean up to clean up all the resources created by this sample 1 run azd down 2 when asked if you are sure you want to continue enter y 3 when asked if you want to permanently delete the resources enter y the resource group and all the resources will be deleted note note the documents used in this demo contain information generated using a language model azure openai service the information contained in these documents is only for demonstration purposes and does not reflect the opinions or beliefs of microsoft microsoft makes no representations or warranties of any kind express or implied about the completeness accuracy reliability suitability or availability with respect to the information contained in this document all rights reserved to microsoft faq details a id ingestion why chunk a summary why do we need to break up the documents into chunks when azure cognitive search supports searching large documents summary chunking allows us to limit the amount of information we send to openai due to token limits by breaking up the content it allows us to easily find potential chunks of text that we can inject into openai the method of chunking we use leverages a sliding window of text such that sentences that end one chunk will start the next this allows us to reduce the chance of losing the context of the text details details a id ingestion more pdfs a summary how can we upload additional documents without redeploying everything summary to upload more documents put them in the data folder and run scripts index data sh or scripts index data ps1 details details a id compare samples a summary how does this sample compare to other chat with your data samples summary another popular repository for this use case is here https github com microsoft sample app aoai chatgpt that repository is designed for use by customers using azure openai studio and azure portal for setup it also includes azd support for folks who want to deploy it completely from scratch the primary differences this repository includes multiple rag retrieval augmented generation approaches that chain the results of multiple api calls to azure openai and acs together in different ways the other repository uses only the built in data sources option for the chatcompletions api which uses a rag approach on the specified acs index that should work for most uses but if you needed more flexibility this sample may be a better option this repository is also a bit more experimental in other ways since it s not tied to the azure openai studio like the other repository feature comparison feature azure search openai javascript sample app aoai chatgpt rag approach multiple approaches only via chatcompletion api data sources vector support yes yes data ingestion yes md yes pdf txt md html persistent chat history no browser tab only yes in cosmosdb technology comparison tech azure search openai javascript sample app aoai chatgpt frontend react lit react backend node js fastify python flask vector db azure cognitive search azure cognitive search deployment azure developer cli azd azure portal az azd details details a id switch gpt4 a summary how do you use gpt 4 with this sample summary in infra main bicep change chatgptmodelname to gpt 4 instead of gpt 35 turbo you may also need to adjust the capacity above that line depending on how much tpm your account is allowed details details a id chat ask diff a summary what is the difference between the chat and ask tabs summary the chat tab uses the approach programmed in chat read retrieve read ts https github com azure samples azure search openai javascript blob main packages search src lib approaches chat read retrieve read ts the ask tab uses the approach programmed in ask retrieve then read ts https github com azure samples azure search openai javascript blob main packages search src lib approaches ask retrieve then read ts there is also another one ask approach available using an agent https github com azure samples azure search openai javascript blob main packages search src lib approaches ask read retrieve read ts details details a id azd up explanation a summary what does the azd up command do summary the azd up command comes from the azure developer cli https learn microsoft com en us azure developer azure developer cli overview and takes care of both provisioning the azure resources and deploying code to the selected azure hosts the azd up command uses the azure yaml file combined with the infrastructure as code bicep files in the infra folder the azure yaml file for this project declares several hooks for the prepackage step and postprovision steps the up command first runs the prepackage hook which installs node dependencies and builds the react js based javascript files it then packages all the code both frontend and backend services into a zip file which it will deploy later next it provisions the resources based on main bicep and main parameters json at that point since there is no default value for the openai resource location it asks you to pick a location from a short list of available regions then it will send requests to azure to provision all the required resources with everything provisioned it runs the postprovision hook to process the local data and add it to an azure cognitive search index finally it looks at azure yaml to determine the azure host container apps and static web apps in this case and uploads the zip to azure app service the azd up command is now complete but it may take a few minutes for the app to be fully available and working after the initial deploy related commands are azd provision for just provisioning if infra files change and azd deploy for just deploying updated app code details troubleshooting here are the most common failure scenarios and solutions 1 the subscription azure subscription id doesn t have access to the azure openai service please ensure azure subscription id matches the id specified in the openai access request process https aka ms oai access 1 you re attempting to create resources in regions not enabled for azure openai e g east us 2 instead of east us or where the model you re trying to use isn t enabled see this matrix of model availability https aka ms oai models 1 you ve exceeded a quota most often number of resources per region see this article on quotas and limits https aka ms oai quotas 1 you re getting same resource name not allowed conflicts that s likely because you ve run the sample multiple times and deleted the resources you ve been creating each time but are forgetting to purge them azure keeps resources for 48 hours unless you purge from soft delete see this article on purging resources https learn microsoft com azure cognitive services manage resources tabs azure portal purge a deleted resource 1 after running azd up and visiting the website you see a 404 not found in the browser wait 10 minutes and try again as it might be still starting up then try running azd deploy and wait again if you still encounter errors with the deployed app consult these tips for debugging app service app deployments http blog pamelafox org 2023 06 tips for debugging flask deployments to html and file an issue if the error logs don t help you resolve the issue | azd-templates azure azureopenai javascript langchain-js langchain-typescript openai typechat typescript | ai |
lectures | 001 001 002 002 pca 003 003 water shield superpixels adaboost viola jones 004 004 005 005 network surgery 006 006 transfer learning 007 007 fcn segnet u net faster rcnn yolo ssd mask rcnn 008 008 rnn lstm gru ocr 009 009 010 findface 010 metric learning 011 011 landmarks densepose | computer-vision study deep-learning deep-neural-networks convolutional-neural-networks | ai |
deep-learning-drizzle | balloon tada deep learning drizzle confetti ball balloon books read enough so you start developing intuitions and then trust your intuitions and go for it https www deeplearning ai hodl geoffrey hinton books br prof geoffrey hinton university of toronto heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy 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graphical models arrow heading down https github com kmario23 deep learning drizzle loudspeaker probabilistic graphical models sparkles machine learning fundamentals arrow heading down https github com kmario23 deep learning drizzle cupid machine learning fundamentals cyclone boom natural language processing arrow heading down https github com kmario23 deep learning drizzle hibiscus natural language processing cherry blossom sparkling heart optimization for machine learning arrow heading down https github com kmario23 deep learning drizzle cupid optimization for machine learning cyclone boom automatic speech recognition arrow heading down https github com kmario23 deep learning drizzle speaking head automatic speech recognition speech balloon thought balloon general machine learning arrow heading down https github com kmario23 deep learning drizzle cupid general machine learning cyclone boom modern computer vision arrow heading down https github com kmario23 deep learning drizzle fire modern computer vision camera flash movie camera reinforcement learning arrow heading down https github com kmario23 deep learning drizzle balloon reinforcement learning hotsprings video game boot camps or summer schools arrow heading down https github com kmario23 deep learning drizzle star2 boot camps or summer schools maple leaf bayesian deep learning arrow heading down https github com kmario23 deep learning drizzle game die bayesian deep learning spades gem medical imaging arrow heading down https github com kmario23 deep learning drizzle movie camera medical imaging camera video camera graph neural networks arrow heading down https github com kmario23 deep learning drizzle tada graph neural networks geometric dl confetti ball balloon bird s eye view of artificial intelligence arrow heading down https github com kmario23 deep learning drizzle bird birds eye view of agi eagle heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign tada deep learning deep neural networks confetti ball balloon heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage lecture videos year 1 neural networks for machine learning geoffrey hinton university of toronto lecture slides http www cs toronto edu hinton coursera slides html br csc321 tijmen https www cs toronto edu tijmen csc321 youtube lectures https www youtube com playlist list plorl3ht4jocdu872ghiywf6jwrk snhz9 br uoft mirror https www cs toronto edu hinton coursera lectures html 2012 br 2014 2 neural networks demystified stephen welch welch labs suppl code https github com stephencwelch neural networks demystified youtube lectures https www youtube com playlist list pliahhy2ibx9hdharr6b7xevztgzra1pou 2014 3 deep learning at oxford nando de freitas oxford university oxford ml http www cs ox ac uk teaching courses 2014 2015 ml youtube lectures https www youtube com playlist list ple6wd9fr efw8dtjaupotupcqmov53fu 2015 4 deep learning for perception dhruv batra virginia tech ece 6504 https computing ece vt edu f15ece6504 youtube lectures https www youtube com playlist list pl fzd610i7yasfh2elbirda90kl2ml0f7 2015 5 deep learning ali ghodsi university of waterloo stat 946 https uwaterloo ca data analytics deep learning youtube lectures https www youtube com playlist list plehulrpyt1hyi78uokmpwcgrxgca9nvoe f2015 6 cs231n cnns for visual recognition andrej karpathy stanford university cs231n http cs231n stanford edu 2015 none 2015 7 cs224d deep learning for nlp richard socher stanford university cs224d http cs224d stanford edu youtube lectures https www youtube com playlist list plmimxx8char8dxwb9lrqdpctmewaml96q 2015 8 bay area deep learning many legends stanford none youtube lectures https www youtube com playlist list plraxtmerzgofmuxkacrynd2ftgbzk2thw 2016 9 cs231n cnns for visual recognition andrej karpathy stanford university cs231n http cs231n stanford edu 2016 youtube lectures https www youtube com playlist list plkt2usq6rbvctenovbg1tpcc7oqi31alc br academic torrent https academictorrents com details 46c5af9e2075d9af06f280b55b65cf9b44eb9fe7 2016 10 neural networks hugo larochelle universit de sherbrooke neural networks http info usherbrooke ca hlarochelle neural networks content html youtube lectures https www youtube com playlist list pl6xpj9i5qxyecohn7tqghaj6naprnmubh br academic torrent https academictorrents com details e046bca3bc837053d1609ef33d623ee5c5af7300 2016 11 cs224d deep learning for nlp richard socher stanford university cs224d http cs224d stanford edu youtube lectures https www youtube com playlist list plljy ebtnft4csvwyqschddp58m3zfhig br academic torrent https academictorrents com details dd9b74b50a1292b4b154094b7338ec1d66e8894d 2016 12 cs224n nlp with deep learning richard socher stanford university cs224n http web stanford edu class cs224n youtube lectures https www youtube com playlist list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 2017 13 cs231n cnns for visual recognition justin johnson stanford university cs231n http cs231n stanford edu 2017 youtube lectures https www youtube com playlist list pl3fw7lu3i5jvhm8ljyj zlfqrf3eo8syv br academic torrent https academictorrents com details ed8a16ebb346e14119a03371665306609e485f13 2017 14 topics in deep learning ruslan salakhutdinov cmu 10707 https deeplearning cmu 10707 github io youtube lectures https www youtube com playlist list plpixoj hndsosl buy7 uevqkyfhhapa f2017 15 deep learning crash course leo isikdogan ut austin none youtube lectures https www youtube com playlist list plwkotbjtdolj3rxbl neiprn9v3a9cx07 2017 16 deep learning and its applications fran ois piti trinity college dublin ee4c16 https github com frcs 4c16 2017 youtube lectures https www youtube com playlist list plio1iezl5ib9nkulnr0x5vxn8aaekglwt 2017 17 deep learning andrew ng stanford university cs230 http cs230 stanford edu youtube lectures https www youtube com playlist list ploromvodv4roabxsyghtsbvuz4g yqhob 2018 18 uva deep learning efstratios gavves university of amsterdam uva dlc https uvadlc github io lecture videos https uvadlc github io lectures sep2018 html 2018 19 advanced deep learning and reinforcement learning many legends deepmind none youtube lectures https www youtube com playlist list plqymg7htrazdnjre23vqcgivpfz k2rzs 2018 20 machine learning peter bloem vrije universiteit amsterdam mlvu https mlvu github io youtube lectures https www youtube com playlist list plcof9eqayqgsoro3pfzeyzfz6cszyo0vj 2018 21 deep learning francois fleuret epfl ee 59 https fleuret org ee559 2018 dlc video lectures https fleuret org ee559 2018 dlc materials 2018 22 introduction to deep learning alexander amini harini suresh and others mit 6 s191 http introtodeeplearning com youtube lectures https www youtube com playlist list pltbw6njqru rwp5 7c0oivt26zgjg9ni br 2017 version https www youtube com playlist list plkkunyzb8lmxfutyupa7b4oimn6cjd6rs 2017 2021 23 deep learning for self driving cars lex fridman mit 6 s094 https selfdrivingcars mit edu youtube lectures https www youtube com playlist list plraxtmerzgoeikm4sgnokngvnjby9efdf 2017 2018 24 introduction to deep learning bhiksha raj and many others cmu 11 485 785 http deeplearning cs cmu edu youtube lectures https www youtube com playlist list plp 0k3kfddpwjbj4q8we 0ynqeg0fzrsa s2018 25 introduction to deep learning bhiksha raj and many others cmu 11 485 785 http deeplearning cs cmu edu youtube lectures https www youtube com playlist list plp 0k3kfddpyh44fp0dl0cbyprvtcfgoi recitation inclusive https www youtube com playlist list pllr0 zolbfd6kdbq93g8 guhi j1icefm f2018 26 deep learning specialization andrew ng stanford dl ai https www deeplearning ai deep learning specialization youtube lectures https www youtube com channel uccixc5mjshvytzr1mal5l9w playlists 2017 2018 27 deep learning ali ghodsi university of waterloo stat 946 https uwaterloo ca data analytics teaching deep learning 2017 youtube lectures https www youtube com playlist list plehulrpyt1hxtolyuweyyioxdabdmaosb f2017 28 deep learning mitesh khapra iit madras cs7015 https www cse iitm ac in miteshk cs7015 html youtube lectures https www youtube com playlist list plyqspqzte6m9gcgajvqbc68hk jkgbayt 2018 29 deep learning for ai upc barcelona dlai 2017 https telecombcn dl github io 2017 dlai br dlai 2018 https telecombcn dl github io 2018 dlai youtube lectures https www youtube com playlist list pl 5emc3hqtbagiujkefjctbnxc0wxc vd 2017 2018 30 deep learning alex bronstein and avi mendelson technion cs236605 https vistalab technion github io cs236605 info youtube lectures https www 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a2018 36 theory of deep learning lots of legends canary islands dali 18 http dalimeeting org dali2018 workshoptheorydl html youtube lectures https www youtube com playlist list plecnfjwzkqxtwbnv8gefgqmmpgz9yf4lr 2018 37 introduction to deep learning alex smola uc berkeley stat 157 http courses d2l ai berkeley stat 157 index html youtube lectures https www youtube com playlist list plzso 6 bsqhqhbcogaobuljoxayyqhpfw s2019 38 deep unsupervised learning pieter abbeel uc berkeley cs294 158 https sites google com view berkeley cs294 158 sp19 home youtube lectures https www youtube com channel ucf4sx8kazm ogczjmresu9w videos s2019 39 machine learning peter bloem vrije universiteit amsterdam mlvu https mlvu github io youtube lectures https www youtube com playlist list plcof9eqayqgupldntvqny bthtcme5r93 2019 40 deep learning on computational accelerators alex bronstein and avi mendelson technion cs236605 https vistalab technion github io cs236605 lectures youtube lectures https www youtube 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lectures https www youtube com playlist list ploromvodv4ro1nb9td4iuz3qghgegtqnx f2019 51 analyses of deep learning lots of legends stanford university stats 385 https stats385 github io youtube lectures https stats385 github io lecture videos 2017 2019 52 deep learning foundations and applications debdoot sheet and sudeshna sarkar iit kgp ai61002 http www facweb iitkgp ac in debdoot courses ai61002 spr2020 youtube lectures https www youtube com playlist list pl addfjimo6pzfwjz0rjlke mismrw7mh s2020 53 designing visualizing and understanding deep neural networks john canny uc berkeley cs 182 282a https bcourses berkeley edu courses 1487769 pages cs l w 182 slash 282a designing visualizing and understanding deep neural networks spring 2020 youtube lectures https www youtube com playlist list plkfd6 40kjiwao6eca8kzsefbob0nfvwm s2020 54 deep learning yann lecun and alfredo canziani nyu ds ga 1008 https atcold github io pytorch deep learning youtube lectures https www youtube com playlist 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2020 youtube lectures https www youtube com playlist list plqymg7htrazcdxz44o4p3n5anz3llrvzf 2020 64 multimodal machine learning louis philippe morency others carnegie mellon university 11 777 mmml 20 https cmu multicomp lab github io mmml course fall2020 youtube lectures https www youtube com channel ucqlhijtgyhiwqpnupu5e2gg videos f2020 65 reliable and interpretable artificial intelligence martin vechev eth z rich riai 20 https www sri inf ethz ch teaching riai2020 youtube lectures https www youtube com playlist list plwjm4hhpang6c w7jjnydec kjk9osp0y f2020 66 fundamentals of deep learning david mcallester toyota technological institute chicago ttic 31230 https mcallester github io ttic 31230 fall2020 youtube lectures https www youtube com channel uccivrtrrr3bqdagbti9 hvq videos f2020 67 foundations of deep learning soheil feize university of maryland college park cmsc 828w http www cs umd edu class fall2020 cmsc828w youtube lectures https www youtube com playlist list 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pl1t8fo7arwlcwg04ognijy91pywmkt2lv s2021 72 deep learning designing visualizing and understanding dnns sergey levine uc berkeley cs 182 https cs182sp21 github io youtube lectures https www youtube com playlist list pl iwqose6tfvmkkqhucjpaortijyt8a5a s2021 73 deep learning in the life sciences manolis kellis mit 6 874 https mit6874 github io youtube lectures https www youtube com playlist list plypixjdtica5sxv7ae3 ps9fyx3vudiox s2021 74 introduction to deep learning and generative models sebastian raschka university of wisconsin madison stat 453 http pages stat wisc edu sraschka teaching stat453 ss2021 youtube lectures https www youtube com playlist list pltkmizhvd 2kjtixow0zfhffbajjilh51 s2021 75 deep learning alfredo canziani and yann lecun nyu nyu dlsp21 https atcold github io nyu dlsp21 youtube lectures https www youtube com playlist list pllhtzkzzvu9e6xufg10tktwapkszczubi s2021 76 applied deep learning alexander pacha tu wien none youtube lectures https www youtube com playlist 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pl8fnqmh2k7jzprxqdyufoiyvhim8pyzwd 2022 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign cupid machine learning fundamentals cyclone boom heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage video lectures year 1 linear algebra gilbert strang mit 18 06 sc http ocw mit edu 18 06scf11 youtube lectures https www youtube com playlist list pl221e2bbf13becf6c 2011 2 probability primer jeffrey miller brown university mathematical monk youtube lectures https www youtube com playlist list pl17567a1a3f5db5e4 2011 3 information theory pattern recognition and neural networks david mackay university of cambridge itprnn http www inference org uk mackay itprnn youtube lectures https www youtube com playlist list plrubu5bi5n4afpg32imbdworvaa vcso6 2012 4 linear algebra review zico kolter cmu linalg http www cs cmu edu zkolter course linalg index html youtube lectures https www youtube com playlist list plm4pv4kyyzgzl5ay6dmpyzrnbzq 8v t 2013 5 probability and statistics michel van biezen none youtube lectures https www youtube com playlist list plx2gx ftpvxuwwtzakohbdhplvz0fbyqv 2015 6 linear algebra an in depth introduction pavel grinfeld none part 1 https www youtube com playlist list pllxfthzgmrukxd88idzs14f4nxazudsmv br part 2 https www youtube com playlist list pllxfthzgmrulwjythculb2qweizokwtsu br part 3 https www youtube com playlist list pllxfthzgmruiqyrutsfxcomiqkugoggj5 br part 4 https www youtube com playlist list pllxfthzgmrulzfrncrrj7xdctjgr633mm 2015 2017 7 multivariable calculus grant sanderson khan academy none youtube lectures https www youtube com playlist list plsql0a2vh4hc5feha6rc5c0wbrtx56nf7 2016 8 essence of linear algebra grant sanderson none youtube lectures https www youtube com playlist list plzhqobowtqdpd3mizzm2xvfitgf8he ab 2016 9 essence of calculus grant sanderson none youtube lectures https www youtube com playlist list plzhqobowtqdmsr9k rj53dwvrmyo3t5yr 2017 2018 10 math background for machine learning geoff gordon cmu 10 606 https canvas cmu edu courses 603 assignments syllabus 10 607 https piazza com cmu fall2017 1060610607 home youtube lectures https www youtube com playlist list pl7y 1rk2ccsaqrtwoz95z gmcecvg5mza f2017 11 mathematics for machine learning linear algebra calculus david dye samuel cooper and freddie page ic london mml https www coursera org learn linear algebra machine learning youtube lectures https www youtube com playlist list plmauaus7wsop itndivr0ankutuhezme4 2018 12 multivariable calculus s k gupta and sanjeev kumar iit roorkee mvc https nptel ac in syllabus 111107108 youtube lectures https www youtube com playlist list plq gm0yrywtiqtk374nzhfocqkwmj71vx 2018 13 engineering probability rich radke rensselaer polytechnic institute none youtube lectures https www youtube com playlist list pluh62q4sv7bu1dn2g6ncyimbml7oxh jx 2018 14 matrix methods in data analysis signal processing and machine learning gilbert strang mit 18 065 https ocw mit edu courses mathematics 18 065 matrix methods in data analysis signal processing and machine learning spring 2018 youtube lectures https www youtube com playlist list plul4u3cngp63omnuhxqiucrks2pivhn3k s2018 15 information theory himanshu tyagi iisc bengaluru e2 201 https ece iisc ac in htyagi course e2201 2020 html youtube lectures https www youtube com playlist list plgmdnelgj1cys 8dlmgpiaowvfeda4nuj 2018 20 16 math camp mark walker university of arizona uamathcamp econ 519 http www u arizona edu mwalker mathcamp2019 htm youtube lectures https www youtube com playlist list plcjquuqt zglhwuacpm7 rks2ejnfwco 2019 17 a 2020 vision of linear algebra gilbert strang mit vola https ocw mit edu resources res 18 010 a 2020 vision of linear algebra spring 2020 youtube lectures https www youtube com playlist list plul4u3cngp61iqefiwle21ejcxwmwvvek s2020 18 mathematics for numerical computing and machine learning szymon rusinkiewicz princeton university cos 302 https www cs princeton edu courses archive fall20 cos302 outline html youtube lectures https www youtube com playlist list pl88asuxxl dsjc5pig8bgkc5wsupyw hh f2020 19 essential statistics for neuroscientists philipp berens universit t klinikum t bingen none youtube lectures https www youtube com playlist list pl05ump7r6ij0gw5sliroa1dmysccx4oxt 2020 20 mathematics for machine learning ulrike von luxburg eberhard karls universit t t bingen math4ml https www tml cs uni tuebingen de teaching 2020 maths for ml youtube lectures https www youtube com playlist list pl05ump7r6ij1a6kdey8pve9zocv6slhrs w2020 21 introduction to causal inference brady neal mila montr al causalinf https www bradyneal com causal inference course youtube lectures https www youtube com playlist list ploazktcs0rzb6bb9l508cyj1z u9iwka0 f2020 22 applied linear algebra andrew thangaraj iit madras ee5120 http www ee iitm ac in andrew ee5120 youtube lectures https www youtube com playlist list plyqspqzte6m chzu5rgfamcxonufytopm 2021 23 mathematical tools for data science carlos fernandez granda new york university ds ga 1013 math ga 2824 https cds nyu edu math tools youtube lectures https www youtube com playlist list plbef5mjte6ktu6ylxfzd6lyychhw5pilc 2021 24 mathematics for numerical computing and machine learning ryan adams princeton university cos 302 sml 305 https www cs princeton edu courses archive spring21 cos302 youtube lectures https www youtube com playlist list plco4cuablhfeho42hvivwasovbaih30uc 2021 go to contents arrow 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google com site michaelzibulevsky optimization course youtube lectures https www youtube com playlist list pldfb2eef4ddafe30b 2009 3 optimization for machine learning s v n vishwanathan purdue university none youtube lectures https www youtube com playlist list pl09b0e8afc69be108 2011 4 optimization geoff gordon ryan tibshirani cmu 10 725 https www cs cmu edu ggordon 10725 f12 youtube lectures https www youtube com playlist list pl7y 1rk2ccsdov91mclonv4kexfftb7du 2012 5 convex optimization joydeep dutta iit kanpur cvx nptel https nptel ac in courses 111 104 111104068 youtube lectures https www youtube com playlist list plbmvogvj5njqhfqfisdgalccwvdcm1w4l 2013 6 foundations of optimization joydeep dutta iit kanpur fop nptel https nptel ac in courses 111 104 111104071 youtube lectures https www youtube com playlist list plbmvogvj5njrrbofh3qm3p6 nvyevdgd 2014 7 algorithmic aspects of machine learning ankur moitra mit 18 409 aaml http people csail mit edu moitra 409 html youtube lectures 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data mining nando de freitas university of british columbia cpsc 340 https www cs ubc ca nando 340 2012 index php youtube lectures https www youtube com playlist list ple6wd9fr ecf 5ncbnsqmhqorpichfjf 2012 5 learning from data yaser abu mostafa caltech cs156 http work caltech edu telecourse html youtube lectures https www youtube com playlist list pld63a284b7615313a 2012 6 machine learning rudolph triebel technische universit t m nchen machine learning https vision in tum de teaching ws2013 ml ws13 youtube lectures https www youtube com playlist list pltbdjv 4f eiiongkls9okrbep8qr47wl 2013 7 introduction to machine learning alex smola cmu 10 701 http alex smola org teaching cmu2013 10 701 youtube lectures https www youtube com playlist list plzso 6 bsqhqmmkwwvvywkregu4b4kmu9 2013 8 introduction to machine learning alex smola and geoffrey gordon cmu 10 701x http alex smola org teaching cmu2013 10 701x youtube lectures https www youtube com playlist list plzso 6 bsqhr7npk4k0zqdm2dpdraqz b 2013 9 pattern recognition sukhendu das iit m and c a murthy isi calcutta pr nptel https nptel ac in syllabus 106106046 youtube lectures https www youtube com playlist list plbmvogvj5njqjmlb2cyw9rry0d5s0tqrp 2014 10 an introduction to statistical learning with applications in r trevor hastie and robert tibshirani stanford stat learn https lagunita stanford edu courses humanitiesandscience statlearning winter2015 about br r bloggers https www r bloggers com in depth introduction to machine learning in 15 hours of expert videos youtube lectures https www youtube com playlist list plog0nghtcqbptlzzrha2ocqzqb1d qz5v 2014 11 introduction to machine learning katie malone sebastian thrun udacity ml udacity https www udacity com course ud120 youtube lectures https www youtube com playlist list plawxtw4syapkqxg8tkvdivyv4hflg7sih 2015 12 introduction to machine learning dhruv batra virginia tech ece 5984 https filebox ece vt edu s15ece5984 youtube lectures https www youtube com playlist list pl fzd610i7yduintfy teoxktwg4mhzhu 2015 13 statistical learning classification ali ghodsi university of waterloo stat 441 https uwaterloo ca data analytics statistical learning classification youtube lectures https www youtube com playlist list plehulrpyt1hy 4obwbk4ab0xk97s6imfc 2015 14 machine learning theory shai ben david university of waterloo none youtube lectures https www youtube com playlist list plpw2kenyw usgvmr7ftq3zrjfls5jt4bo 2015 15 introduction to machine learning alex smola cmu 10 701 http alex smola org teaching 10 701 15 youtube lectures https www youtube com playlist list plzso 6 bsqhttv7w9u7grtxbhmh mw3qn s2015 16 statistical machine learning larry wasserman cmu none youtube lectures https www youtube com playlist list pljbui5mgii6bweuzf7he6nowwvgne y8r s2015 17 ml supervised learning michael littman charles isbell pushkar kolhe gatech ml udacity https eu udacity com course machine learning ud262 youtube lectures https www youtube com playlist list plawxtw4syapl0n6 e1gvylp5 mumujoko 2015 18 ml unsupervised learning michael littman charles isbell pushkar kolhe gatech ml udacity https eu udacity com course machine learning unsupervised learning ud741 youtube lectures https www youtube com playlist list plawxtw4syapmahhu lz3mhlsj yh jng7 2015 19 advanced introduction to machine learning barnabas poczos and alex smola 10 715 https www cs cmu edu bapoczos classes ml10715 2015fall youtube lectures https www youtube com playlist list pl4yhk0pt0zhwbzsbkmgzpnpw6sf6ma0ix f2015 20 machine learning pedro domingos uwashington csep 546 https courses cs washington edu courses csep546 16sp youtube lectures https www youtube com playlist list pltpqex 31jxgtdac6 3hxwcp7fq4n8ygr s2016 21 statistical machine learning larry wasserman cmu none youtube lectures https www youtube com playlist list pltb9vqq8wiacbk2xrtyn5t9uupdsnm7ye s2016 22 machine learning with large datasets william cohen cmu 10 605 http curtis ml cmu edu w courses index php machine learning with large datasets 10 605 in fall 2016 youtube lectures https www youtube com playlist list plnfbqxrw5mrhptfkadfwq0vcusi2iwecw f2016 23 math background for machine learning geoffrey gordon cmu 10 600 youtube lectures https www youtube com playlist list pl7y 1rk2ccsa339crwxmwuabrulbvpbcg f2016 24 statistical learning classification ali ghodsi university of waterloo none youtube lectures https www youtube com playlist list plehulrpyt1hzxdemu7k4etcf0ld b5adg 2017 25 machine learning andrew ng stanford university coursera ml https www coursera org learn machine learning youtube lectures https www youtube com playlist list pllsst5z dsk h9vyzkqkynwcitqhlrjln 2017 26 machine learning roni rosenfield cmu 10 601 http www cs cmu edu roni 10601 f17 youtube lectures https www youtube com playlist list pl7k0r4t5c10 g7cwcnhfzoaxlaininchk 2017 27 statistical machine learning ryan tibshirani larry wasserman cmu 10 702 http www stat cmu edu ryantibs statml youtube lectures https www youtube com playlist list pljbui5mgii6b7a0nm74zhtovqttc9dacv s2017 28 machine learning for computer vision fred hamprecht heidelberg university none youtube lectures https www youtube com playlist list plurasnb3n4ksqfyt8vbldsq9po9xtu8ry f2017 29 math background for machine learning geoffrey gordon cmu 10 606 10 607 https canvas cmu edu courses 603 assignments syllabus youtube lectures https www youtube com playlist list pl7y 1rk2ccsaqrtwoz95z gmcecvg5mza f2017 30 data visualization ali ghodsi university of waterloo none youtube lectures https www youtube com playlist list plehulrpyt1hzqoxehtnuytmd0anqvtyak 2017 31 machine learning for physicists florian marquardt uni erlangen n rnberg ml4phy 17 http www thp2 nat uni erlangen de index php 2017 machine learning for physicists by florian marquardt lecture videos https www video uni erlangen de course id 574 2017 32 machine learning for intelligent systems kilian weinberger cornell university cs4780 http www cs cornell edu courses cs4780 2018fa youtube lectures https www youtube com playlist list pll8olhzgyoq7bkvburthesalr7bonzbxs f2018 33 statistical learning theory and applications tomaso poggio lorenzo rosasco sasha rakhlin 9 520 6 860 https cbmm mit edu lh 9 520 youtube lectures https www youtube com playlist list plygkbdfnk iatlo6olw4swmiqgz4f2opy f2018 34 machine learning and data mining mike gelbart university of british columbia cpsc 340 https ubc cs github io cpsc340 youtube lectures https www youtube com playlist list plwmxhcz 53q02zleaxigki1jzffco6m b 2018 35 foundations of machine learning david rosenberg bloomberg foml https bloomberg github io foml home youtube lectures https www youtube com playlist list plnzuxoufsxnvftwtb1hl6mel1v32w0thi 2018 36 introduction to machine learning andreas krause eth z rich introml https las inf ethz ch teaching introml s18 youtube lectures https www youtube com playlist list plzn6ln6whln273tsqyfdrbusa o5nuesv 2018 37 machine learning fundamentals sanjoy dasgupta uc san diego mlf slides https drive google com drive folders 1l1rwv jmihlzipw0ztggn9 snwosa3m9 youtube lectures https www youtube com playlist list pl onphfckvqhuzctvgqic8w2shzkwlm0s 2018 38 machine learning jordan boyd graber university of maryland cmsc 726 http users umiacs umd edu jbg teaching cmsc 726 youtube lectures https www youtube com playlist list plegwunz91wfselyrcz7d1gwavifdazmgo 2015 2018 39 machine learning andrew ng stanford university cs229 http cs229 stanford edu syllabus autumn2018 html youtube lectures https www youtube com playlist list ploromvodv4rmigqp3wxshtmggzqpfvfbu 2018 40 machine intelligence h r tizhoosh uwaterloo syde 522 https kimialab uwaterloo ca kimia index php teaching syde 522 machine intelligence 2 youtube lectures https www youtube com playlist list pl4upcu5bnihwcx93gv6aqnkmvmwx4azot 2019 41 introduction to machine learning pascal poupart university of waterloo cs480 680 https cs uwaterloo ca ppoupart teaching cs480 spring19 youtube lectures https www youtube com playlist list pldaol1zkcqtw uzosvbneeckhsnug m0k s2019 42 advanced machine learning thorsten joachims cornell university cs 6780 https www cs cornell edu courses cs6780 2019sp lecture videos https cornell mediasite com mediasite catalog full f5d1cd3323f746cca80b2468bf97efd421 s2019 43 machine learning for structured data matt gormley carnegie mellon university 10 418 10 618 http www cs cmu edu mgormley courses 10418 schedule html youtube lectures https www youtube com playlist list pl4cxkujbvnvihrkp4bxufvrliwzes iep f2019 44 advanced machine learning joachim buhmann eth z rich ml2 aml https ml2 inf ethz ch courses aml lecture videos https video ethz ch lectures d infk 2019 autumn 252 0535 00l html f2019 45 machine learning for signal processing vipul arora iit kanpur mlsp http home iitk ac in vipular stuff 2019 mlsp html lecture videos https iitk my sharepoint com f g personal vipular iitk ac in enf97nzfsovbiyclc6yhfe4bluv6ca4u8lpqq4vtsdo xg f2019 46 foundations of machine learning animashree anandkumar caltech cms 165 http tensorlab cms caltech edu users anima cms165 2019 html youtube lectures https www youtube com playlist list plvnifwxslhca5guh0o92nemiwiqigvfqp 2019 47 machine learning for physicists florian marquardt uni erlangen n rnberg none lecture videos https www video uni erlangen de course id 778 2019 48 applied machine learning andreas m ller columbia university coms w4995 https www cs columbia edu amueller comsw4995s19 youtube lectures https www youtube com playlist list pl pvmaaanxiqgzqs2oi3owept dpmwtfa 2019 49 fundamentals of machine learning over networks hossein shokri ghadikolaei kth sweden mlons https sites google com view mlons course materials youtube lectures https www youtube com playlist list plwoztd81wfcebfrxdfnurdnt3abdlfg80 2019 50 foundations of machine learning and statistical inference animashree anandkumar caltech cms 165 http tensorlab cms caltech edu users anima cms165 2020 html youtube lectures https www youtube com playlist list plvnifwxslhcdlbyitallyboaepbmf1ahg 2020 51 machine learning rebecca willett and yuxin chen university of chicago stat 37710 cmsc 35400 https voices uchicago edu willett teaching stats37710 cmsc35400 s20 lecture videos https voices uchicago edu willett teaching stats37710 cmsc35400 s20 s2020 52 introduction to machine learning sanjay lall and stephen boyd stanford university ee104 cme107 http ee104 stanford edu youtube lectures https www youtube com playlist list ploromvodv4rn uy7 wms051 q1d6akxmk s2020 53 applied machine learning andreas m ller columbia university coms w4995 https www cs columbia edu amueller comsw4995s20 schedule youtube lectures https www youtube com playlist list pl pvmaaanxirnsw6wicpsvshfycrezmlm s2020 54 statistical machine learning ulrike von luxburg eberhard karls universit t t bingen stat ml https www tml cs uni tuebingen de teaching 2020 statistical learning index php youtube lectures https www youtube com playlist list pl05ump7r6ij2xcvrrzlokx6eohwaga2cc ss2020 55 probabilistic machine learning philipp hennig eberhard karls universit t t bingen prob ml https uni tuebingen de en 180804 youtube lectures https www youtube com playlist list pl05ump7r6ij1thaofy96m5ux3j21a6ynd ss2020 56 machine learning sarath chandar polymtl udem mila inf8953ce http sarathchandar in teaching ml fall2020 youtube lectures https www youtube com playlist list plimtcgowf et0mi ammqq0sijupwyaior f2020 57 machine learning erik bekkers universiteit van amsterdam uva ml https uvaml1 github io youtube lectures https www youtube com playlist list pl8fnqmh2k7jzhtvybkmvrmyxdymmgjj n f2020 58 neural networks for signal processing shayan srinivasa garani indian institute of science nn4sp https labs dese iisc ac in pnsil neural networks and learning systems i fall 2020 youtube lectures https www youtube com playlist list plgmdnelgj1czn1399dv7 u4vbnjflrsua f2020 59 introduction to machine learning dmitry kobak universit t klinikum t bingen none youtube lectures https www youtube com playlist list pl05ump7r6ij35shkldqccjsdntugy4fqt 2020 60 machine learning prml erik j bekkers universiteit van amsterdam uvaml 1 https uvaml1 github io youtube lectures https www youtube com playlist list pl8fnqmh2k7jzhtvybkmvrmyxdymmgjj n 2020 61 machine learning with kernel methods julien mairal and jean philippe vert inria ens paris saclay google ml kernels http members cbio mines paristech fr jvert svn kernelcourse course 2021mva index html youtube lectures https www youtube com playlist list pld93kgj6 edrknj27azmecbrlq1smkp o s2021 62 continual learning vincenzo lomonaco universit di pisa contlearn 21 https course continualai org background details youtube lectures https www youtube com playlist list plm6qxeab xkbfm5rgqp6wcr7jegdg51px 2021 63 causality christina heinze deml eth zurich causal 21 https stat ethz ch lectures ss21 causality php course materials youtube lectures https stat ethz ch lectures ss21 causality php course materials 2021 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign balloon reinforcement learning hotsprings video game heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage video lectures year 1 a short course on reinforcement learning satinder singh umichigan none youtube lectures https www youtube com playlist list plm4pv4kyyzgy4cifq5c36 1jmnlab80ky 2011 2 approximate dynamic programming dimitri p bertsekas mit lecture slides http adpthu2014 weebly com slides materials html youtube lectures https www youtube com playlist list pliclbsfqnfaxomveqphi5er1lgf2 l9i4 2014 3 introduction to reinforcement learning david silver deepmind ucl rl http www0 cs ucl ac uk staff d silver web teaching html youtube lectures https www youtube com playlist list plqymg7htrazdm oyhwgpebj2mfcfzfobq 2015 4 reinforcement learning charles isbell chris pryby gatech michael littman brown rl udacity https eu udacity com course reinforcement learning ud600 youtube lectures https www youtube com playlist list plawxtw4syapniddwo9e2c7ixisu pdsnp 2015 5 reinforcement learning balaraman ravindran iit madras rl iitm https www cse iitm ac in ravi courses reinforcement 20learning html youtube lectures https www youtube com playlist list plndwvhi37uggqivcazcmtggeqhy9w7d9d 2016 6 deep reinforcement learning sergey levine uc berkeley cs 294 http rail eecs berkeley edu deeprlcoursesp17 youtube lectures https www youtube com playlist list plkfd6 40kjiwtmsbcv9ovjb3yao4sfwkx s2017 7 deep reinforcement learning sergey levine uc berkeley cs 294 http rail eecs berkeley edu deeprlcourse fa17 youtube lectures https www youtube com playlist list plkfd6 40kjiznc9cdbvtjaf2oyt8 vae3 f2017 8 deep rl bootcamp many legends uc berkeley deep rl https sites google com view deep rl bootcamp lectures youtube lectures https www youtube com channel uctgm vlxkuylprz ygajhow videos 2017 9 data efficient reinforcement learning lots of legends canary islands derl 17 http dalimeeting org dali2017 data efficient reinforcement learning html youtube lectures https www youtube com playlist list pl twvtpyd1vavdpxukup6w suzqq7e8k8 2017 10 deep reinforcement learning sergey levine uc berkeley cs 294 112 http rail eecs berkeley edu deeprlcourse fa18 youtube lectures https www youtube com playlist list plkfd6 40kjixjmr j5a1mkxk26gh qg37 2018 11 reinforcement learning pascal poupart university of waterloo cs 885 https cs uwaterloo ca ppoupart teaching cs885 spring18 youtube lectures https www youtube com playlist list pldaol1zkcqtxfjnio3tqqn6xmbbl07edc 2018 12 deep reinforcement learning and control katerina fragkiadaki and tom mitchell cmu 10 703 http www andrew cmu edu course 10 703 youtube lectures https www youtube com playlist list plpixoj hndsnfvowrklsuobmnf2j1l5ov 2018 13 reinforcement learning and optimal control dimitri bertsekas arizona state university rloc http web mit edu dimitrib www rlbook html lecture videos http web mit edu dimitrib www rlbook html 2019 14 reinforcement learning emma brunskill stanford university cs 234 http web stanford edu class cs234 index html youtube lectures https www youtube com playlist list ploromvodv4rosopzutgyctapigly2nd8u 2019 15 reinforcement learning day lots of legends microsoft research new york rld 19 https www microsoft com en us research event reinforcement learning day 2019 agenda youtube lectures https www youtube com playlist list pld7hfcn7lxre9nwex3up ricdi6 0mqvc 2019 16 new directions in reinforcement learning and control lots of legends ias princeton university ndrlc 19 https www math ias edu ndrlc youtube lectures https www youtube com playlist list plddzb3twjpz61sgqd6cbwcmtc275nrku3 2019 17 deep reinforcement learning sergey levine uc berkeley cs 285 http rail eecs berkeley edu deeprlcourse fa19 youtube lectures https www youtube com playlist list plkfd6 40kjiwhwjpgazj9vsj9cfmkb79a f2019 18 deep multi task and meta learning chelsea finn stanford university cs 330 https cs330 stanford edu youtube lectures https www youtube com playlist list ploromvodv4rmc6zfymnd7ug3lvvwaity5 f2019 19 rl theory seminars lots of legends earth rl theory sem https sites google com view rltheoryseminars past seminars youtube lectures https www youtube com channel ucfbfutc9rbkk6p b4r9eba videos 2020 20 deep reinforcement learning sergey levine uc berkeley cs 285 http rail eecs berkeley edu deeprlcourse fa20 youtube lectures https www youtube com playlist list pl iwqose6tfuriihcrlt wj9byivpbfgc f2020 21 introduction to reinforcement learning amir massoud farahmand vector institute university of toronto rl intro https amfarahmand github io introrl youtube lectures https www youtube com playlist list plcveixxl2xnbidq51a8ijwprq2ao0ykrq s2021 22 reinforcement learning antonio celani and emanuele panizon international centre for theoretical physics none youtube lectures https www youtube com playlist list plp0hsy2ubep8q2g3mfhgvgvqfemx0qrwm 2021 23 computational sensorimotor learning pulkit agrawal mit csail 6 884 csl https pulkitag github io 6 884 lectures youtube lectures https www youtube com playlist list plwnwxag kbxpmtis2fkwssf7hql2tcc78 s2021 24 reinforcement learning dimitri p bertsekas asu mit rl 21 http web mit edu dimitrib www rlbook html youtube lectures https www youtube com playlist list plmh30bg15sip79jrj mvf12uvb1qptpzn s2021 25 reinforcement learning sarath chandar cole polytechnique de montr al inf8953de https chandar lab github io inf8953de youtube lectures https www youtube com playlist list plimtcgowf es jdf ucm60extcgzg67ua f2021 26 deep reinforcement learning sergey levine uc berkeley cs 285 http rail eecs berkeley edu deeprlcourse youtube lectures https www youtube com playlist list pl iwqose6tfxxkgi1ggyv1b xa0dxe5eh f2021 27 reinforcement learning lecture series lots of legends deepmind uc london rl series https deepmind com learning resources reinforcement learning series 2021 youtube lectures https www youtube com playlist list plqymg7htrazdvh599eitlewsuosjbaodm 2021 28 reinforcement learning dimitri p bertsekas asu mit rl 22 http web mit edu dimitrib www rlbook html youtube lectures https www youtube com playlist list plmh30bg15sioxhxlldoio0bhsiy84ymdj s2022 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy 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heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage lecture videos year 1 probabilistic graphical models many legends mpi is mlss tuebingen http mlss tuebingen mpg de 2013 2013 speakers html youtube lectures https www youtube com playlist list pll0gjjzxhawtriw ynfswmailsa0hjcz3 2013 2 probabilistic modeling and machine learning zoubin ghahramani university of cambridge wust wroclaw https www ii pwr edu pl gonczarek zoubin html youtube lectures https www youtube com playlist list plwuok5j xosdfvagkerx9hqnrvziurbz2 2013 3 probabilistic graphical models eric xing cmu 10 708 http www cs cmu edu epxing class 10708 lecture html youtube lectures https www youtube com playlist list pli3niod p5aoxroztd1p6cclavu9rntc 2014 4 learning with structured data an introduction to probabilistic graphical models christoph lampert ist austria none youtube lectures https www youtube com playlist list pleqohzpnmtfa0wc1jxjovvorjlx8w0rgf 2016 5 probabilistic graphical models nicholas zabaras university of notre dame pgm https www zabaras com probabilistic graphical models youtube lectures https www youtube com playlist list pld pudzw85acv4bgdu7whpl37hm60w4rm 2018 6 probabilistic graphical models eric xing cmu 10 708 https sailinglab github io pgm spring 2019 lecture videos https sailinglab github io pgm spring 2019 lectures br youtube lectures https www youtube com playlist list plozgvqqhoumty2caqhl45tqp6kmdndcqn s2019 7 probabilistic graphical models eric xing cmu 10 708 https www cs cmu edu epxing class 10708 20 index html youtube lectures https www youtube com playlist list plozgvqqhoumtqxihcdcpoajooimrrcgzn s2020 8 uncertainty modeling in ai gim hee lee national university of singapura nus cs 5340 ch https www coursehero com sitemap schools 2652 national university of singapore courses 7821096 cs5340 cs 5340 nb https github com clear nus cs5340 notebooks youtube lectures https www youtube com playlist list plxg0cgqviygob9eyc8ixm27doxjp2sk0h 2020 21 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign 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minus sign s no course name university instructor s course webpage lecture videos year 1 bayesian neural networks variational inference lots of legends none youtube lectures https www youtube com playlist list plm4pv4kyyzgwub4bfy183hwghpl9ytva1 2014 now 2 variational inference chieh wu northeastern university none youtube lectures https www youtube com playlist list pldk2fd27cqzsd1sq3kbyl4vtv6gjxvpse 2015 3 deep learning and bayesian methods lots of legends hse moscow dlbm ss http deepbayes ru 2018 youtube lectures https www youtube com playlist list ple5rnuydzv9q01vwcp9bv7nhjg3j7mz62 2018 4 deep learning and bayesian methods lots of legends hse moscow dlbm ss http deepbayes ru youtube lectures https www youtube com playlist list ple5rnuydzv9qhe8vdstpu0o8yp63oecdw 2019 5 nordic probabilistic ai lots of legends ntnu trondheim probai https github com probabilisticai probai 2019 youtube lectures https www youtube com playlist list plry vw 9hv8s jkhxzvnd26kgjrp2ik 2019 go to contents arrow 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lectures https www youtube com playlist list plgshh6boff5ujaut4zriazc ofxolkagk 2015 3 medical imaging summer school lots of legends sicily miss 16 http iplab dmi unict it miss16 programme html youtube lectures https www youtube com playlist list pltrcr47ytx5ixiysnex3lkf16upaw59wa 2016 4 optical and ultrasound imaging opus lots of legends universit de lyon france opus 16 https opus2016lyon sciencesconf org resource page id 2 youtube lectures https www youtube com playlist list pl95ayovlx8gdukbxu r9wqrwwzdwckjti 2016 5 medical imaging summer school lots of legends sicily miss 18 http iplab dmi unict it miss programme htm youtube lectures https www youtube com playlist list pl veuglulxqux1dv4ia3xumx6auejmgga 2018 6 seminar on ai in healthcare lots of legends stanford cs 522 http cs522 stanford edu 2018 index html youtube lectures https www youtube com playlist list plyn zmpr1dtnqj ot l2v2egueh6oh 7w 2018 7 machine learning for healthcare david sontag peter szolovits csail mit mlhc 19 https mlhc19mit github io br mit 6 s897 https ocw mit edu courses electrical engineering and computer science 6 s897 machine learning for healthcare spring 2019 lecture notes youtube lectures https www youtube com playlist list plul4u3cngp60b0pqxvqygndcyctdu1q5j s2019 8 deep learning and medical applications lots of legends ipam ucla dlm 20 https www ipam ucla edu programs workshops deep learning and medical applications tab schedule lecture videos https www ipam ucla edu programs workshops deep learning and medical applications tab schedule 2020 9 stanford symposium on artificial intelligence in medicine and imaging lots of legends stanford aimi aimi 20 https aimi stanford edu news events aimi symposium agenda youtube lectures https www youtube com watch v tr2obil4il8 list ple6zdime5b7ir0odoobxbdblyy1eqlypx 2020 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign 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6039c846b2f84e7a806024c06e3f5c5c1d 2017 3 eurographics symposium on geometry processing graduate school lots of legends siggraph london sgp 2017 http geometry cs ucl ac uk sgp2017 p gradschool youtube lectures https www youtube com playlist list plop ngxvomharqntglvnzujndznx3rdjz 2017 4 eurographics symposium on geometry processing graduate school lots of legends siggraph paris sgp 2018 https sgp2018 sciencesconf org resource page id 7 youtube lectures https www youtube com playlist list plvcorb dvamgpp8lyw7duvlxh 1vrrm v 2018 5 analysis of networks mining and learning with graphs jure leskovec stanford university cs224w http snap stanford edu class cs224w 2018 lecture videos http snap stanford edu class cs224w 2018 2018 6 machine learning with graphs jure leskovec stanford university cs224w http snap stanford edu class cs224w 2019 youtube lectures https www youtube com playlist list pl y8zk4dwcrqyasidb2mjj itw2 yyx6 2019 7 geometry and learning from data in 3d and beyond geometry and learning from data tutorials lots of legends ipam ucla gldt http www ipam ucla edu programs workshops geometry and learning from data tutorials lecture videos http www ipam ucla edu programs workshops geometry and learning from data tutorials tab schedule 2019 8 geometry and learning from data in 3d and beyond geometric processing lots of legends ipam ucla geopro http www ipam ucla edu programs workshops workshop i geometric processing lecture videos http www ipam ucla edu programs workshops workshop i geometric processing tab schedule 2019 9 geometry and learning from data in 3d and beyond shape analysis lots of legends ipam ucla shape analysis http www ipam ucla edu programs workshops workshop ii shape analysis lecture videos http www ipam ucla edu programs workshops workshop ii shape analysis tab schedule 2019 10 geometry and learning from data in 3d and beyond geometry of big data lots of legends ipam ucla geo bdata http www ipam ucla edu programs workshops workshop iii geometry of big data lecture videos http www ipam ucla edu programs workshops workshop iii geometry of big data tab schedule 2019 11 geometry and learning from data in 3d and beyond deep geometric learning of big data and applications lots of legends ipam ucla dgl bdata http www ipam ucla edu programs workshops workshop iv deep geometric learning of big data and applications lecture videos http www ipam ucla edu programs workshops workshop iv deep geometric learning of big data and applications tab schedule 2019 12 israeli geometric deep learning lots of legends israel igdl 20 https gdl israel github io schedule html lecture videos https www youtube com watch v c8 32ivn sg 2020 13 machine learning for graphs and sequential data stephan g nnemann technische universit t m nchen tum mlgs 20 https www in tum de en daml teaching summer term 2020 machine learning for graphs and sequential data lecture videos https www in tum de daml teaching mlgs s2020 14 machine learning with graphs jure leskovec stanford cs224w http web stanford edu class cs224w youtube lectures https www youtube com playlist list ploromvodv4rplkxipqhjhpgdqy7imnkdn w2021 15 geometric deep learning ammi lots of legends virtual gdl ammi https geometricdeeplearning com lectures youtube lectures https www youtube com playlist list pln2 demqetfq8yvuhbovahulnipyxkeu3 2021 16 summer school on geometric deep learning lots of legends dtu diku aau gdl dtu diku aau https geometric deep learning compute dtu dk lecture videos https geometric deep learning compute dtu dk talks and materials 2021 17 graph neural networks alejandro ribeiro university of pennsylvania ese 514 https gnn seas upenn edu youtube lectures https www youtube com channel uc yprqpieqkegog1tct0giq playlists f2021 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign 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sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage lecture videos year 1 computational linguistics i jordan boyd graber university of maryland cms 723 http users umiacs umd edu jbg teaching cmsc 723 youtube lectures https www youtube com playlist list plegwunz91wfupebli97 wueap90jo 15i 2013 2018 2 deep learning for natural language processing nils reimers tu darmstadt dl4nlp https github com ukplab deeplearning4nlp tutorial youtube lectures https www youtube com channel uc1zcutrfpjt6sv2kjk jkva videos 2015 2017 3 deep learning for natural language processing many legends deepmind oxford dl nlp http www cs ox ac uk teaching courses 2016 2017 dl youtube lectures https www youtube com playlist list pl613dyigmxozbtzhbyibqb0qtgk6ojbpm 2017 4 deep learning for speech language upc barcelona dl sl https telecombcn dl github io 2017 dlsl lecture videos https telecombcn dl github io 2017 dlsl 2017 5 neural networks for natural language processing graham neubig cmu nn4nlp http www phontron com class nn4nlp2017 code https github com neubig nn4nlp code youtube lectures https www youtube com playlist list pl8pytp1v4i8abxzdqtopb eqblvaz xpt 2017 6 neural networks for natural language processing graham neubig cmu nn4 nlp http www phontron com class nn4nlp2018 youtube lectures https www youtube com playlist list pl8pytp1v4i8ba7 ry4fob4 jfuj7vdkee 2018 7 deep learning for nlp min yen kan nus cs 6101 https www comp nus edu sg kanmy courses 6101 1810 youtube lectures https www youtube com playlist list plllwxvcs7ca5ed44ktcit7rmu hfaafxb 2018 8 neural networks for natural language processing graham neubig cmu nn4nlp http www phontron com class nn4nlp2019 youtube lectures https www youtube com playlist list pl8pytp1v4i8ajj7sy6sdtmjgkt7eo2vms 2019 9 natural language processing with deep learning abigail see chris manning richard socher stanford university cs224n http web stanford edu class cs224n youtube lectures https www youtube com playlist list ploromvodv4rohcuxmzknm7j3fvwbby42z 2019 10 natural language understanding bill maccartney and christopher potts cs224u https web stanford edu class cs224u youtube lectures https www youtube com playlist list ploromvodv4robpmcir6rnnulfan56js20 s2019 11 neural networks for natural language processing graham neubig carnegie mellon university cs 11 747 http www phontron com class nn4nlp2020 schedule html youtube lectures https www youtube com playlist list pl8pytp1v4i8cj7nmxmc8axv8wqkywj aj s2020 12 advanced natural language processing mohit iyyer umass amherst cs 685 https people cs umass edu miyyer cs685 youtube lectures https www youtube com playlist list plwnsvgp6czadmqx6qevbar3 vdbiowhjl f2020 13 machine translation philipp koehn johns hopkins university en 601 468 668 http mt class org jhu syllabus html youtube lectures https www youtube com playlist list plqrciudqdlg0lqx54o9jb4phj sli6zbq f2020 14 neural networks for nlp graham neubig carnegie mellon university cs 11 747 http www phontron com class nn4nlp2021 youtube lectures https www youtube com playlist list pl8pytp1v4i8akahej7loorlex pcxs xv 2021 15 deep learning for natural language processing kyunghyun cho new york university ds ga 1011 https drive google com drive folders 1ykxbtophay 65vhk 8ydzzqjwfjdd5ve youtube lectures https www youtube com playlist list pldh9u0f1xkw s c8ecgjpn hjz5jj1irf f2021 16 natural language processing with deep learning chris manning stanford university cs224n https web stanford edu class archive cs cs224n cs224n 1214 youtube lectures https www youtube com playlist list ploromvodv4rosh4v6133s9lfprhjembmj 2021 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign speaking head automatic speech recognition speech balloon thought balloon heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage lecture videos year 1 deep learning for speech language upc barcelona dl sl https telecombcn dl github io 2017 dlsl lecture videos https telecombcn dl github io 2017 dlsl br youtube videos https www youtube com playlist list pl 5dczhuhzkwef9ljijoc x5ghrlntiku 2017 2 speech and audio in the northeast many legends google nyc sane 15 http www saneworkshop org sane2015 youtube videos https www youtube com playlist list plbjwrpcgwk7szob4utvilwwnrg84l9o5i 2015 3 automatic speech recognition samudra vijaya k tifr none youtube videos https www youtube com channel uchk6uq1cr9j3k5knmisyunw videos 2016 4 speech and audio in the northeast many legends google nyc sane 17 http www saneworkshop org sane2017 youtube videos https www youtube com playlist list plbjwrpcgwk7tnlabvu s90zqsblo3bwjg 2017 5 speech and audio in the northeast many legends google cambridge sane 18 http www saneworkshop org sane2018 youtube videos https www youtube com playlist list plbjwrpcgwk7sjmann8jqosyhime6djhmn 2018 1 deep learning for speech recognition many legends aoe none youtube videos https www youtube com playlist list plm4pv4kyyzgyfycxv6ypwakvor2gmhnqd 2015 2018 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign fire modern computer vision camera flash movie camera heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus 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classical mubarak shah ucf cap 5415 http crcv ucf edu courses cap5415 fall2012 index php youtube lectures https www youtube com playlist list pld3hlsjsx imk bpmb h3aqjfkzs9xgzm 2012 3 image and multidimensional signal processing classical william hoff colorado school of mines csci 510 eeng 510 http inside mines edu whoff courses eeng510 youtube lectures https www youtube com playlist list plyed3w677alnv8htn0f9xh ahe1azpftv 2012 4 computer vision classical william hoff colorado school of mines csci 512 eeng 512 http inside mines edu whoff courses eeng512 index htm youtube lectures https www youtube com playlist list pl4b3f8d4a5cad8da3 2012 5 image and video processing from mars to hollywood with a stop at the hospital guillermo sapiro duke university none youtube videos https www youtube com playlist list plz9qnfmhz a79y1stvuuqgyl o0fzh2rs 2013 6 multiple view geometry classical daniel cremers technische universit t m nchen mvg https vision in tum de teaching ss2014 mvg2014 youtube lectures https www youtube com playlist list pltbdjv 4f ejn6udz34tht9eviw7lbeo4 2013 7 mathematical methods for robotics vision and graphics justin solomon stanford university cs 205a http graphics stanford edu courses cs205a youtube lectures https www youtube com playlist list plq3uicqqtfnvq vzflhykhaqzitxokswi 2013 8 computer vision classical mubarak shah ucf cap 5415 http crcv ucf edu courses cap5415 fall2014 index php youtube lectures https www youtube com playlist list pld3hlsjsx imkp68wfkzjviptd8ie5u 9 2014 9 computer vision for visual effects classical rich radke rensselaer polytechnic institute ecse 6969 https www ecse rpi edu rjradke cvfxcourse html youtube lectures https www youtube com playlist list pluh62q4sv7bujlklt84hfqswfw36mdd5a s2014 10 autonomous navigation for flying robots juergen sturm technische universit t m nchen autonavx https jsturm de wp teaching autonavx slides youtube lectures https www youtube com playlist list pltbdjv 4f ekbcus1hmmtsnxv4juofrzg 2014 11 slam mobile robotics cyrill stachniss universitaet freiburg robotmapping http ais informatik uni freiburg de teaching ws13 mapping youtube lectures https www youtube com playlist list plgnqpqtftogqrz4o5qzbihgl3b1jhimn 2014 12 computational photography irfan essa david joyner arpan chakraborty cp udacity https eu udacity com course computational photography ud955 youtube lectures https www youtube com playlist list plawxtw4syapn unawtrmley4pese4oziy 2015 13 introduction to digital image processing rich radke rensselaer polytechnic institute ecse 4540 https www ecse rpi edu rjradke improccourse html youtube lectures https www youtube com playlist list pluh62q4sv7buf60vkjepfcoqc8shxmndx s2015 14 lectures on digital photography marc levoy stanford google research lodp https sites google com site marclevoylectures youtube lectures https www youtube com playlist list pl7ddpxyvfxspun0n gobf1gxoca da 7i 2016 15 introduction to computer vision foundation aaron bobick irfan essa arpan chakraborty cv udacity https eu udacity com course introduction to computer vision ud810 youtube lectures https www youtube com playlist list plawxtw4syapnbdacyrk kb rukuxqblcm 2016 16 computer vision syed afaq ali shah university of western australia none youtube lectures https www youtube com playlist list plvqb6 mdbcdlnt84lk nvboqcxllotr8j 2016 17 photogrammetry i ii cyrill stachniss university of bonn pg i ii https www ipb uni bonn de photogrammetry i ii youtube lectures https www youtube com playlist list plgnqpqtftogrsi5vzy9piqpnwhjq bkn1 2016 18 deep learning for computer vision upc barcelona dlcv 16 http imatge upc github io telecombcn 2016 dlcv br dlcv 17 https telecombcn dl github io 2017 dlcv br dlcv 18 https telecombcn dl github io 2018 dlcv youtube lectures https www youtube com playlist list pl 5emc3hqtbbuatfp4wsfd2y2vqefqcap 2016 2018 19 convolutional neural networks andrew ng stanford university deeplearning ai https www deeplearning ai deep learning specialization youtube lectures https www youtube com playlist list plkdae6sczn6gl29aoe31iwdvwsg kndzf 2017 20 variational methods for computer vision daniel cremers technische universit t m nchen vmcv https vision in tum de teaching ws2016 vmcv2016 youtube lectures https www youtube com playlist list pltbdjv 4f ej7a2iih5l5ztqqrwyjp2ri 2017 21 winter school on computer vision lots of legends israel institute for advanced studies ws cv http www as huji ac il cse youtube lectures https www youtube com playlist list pltn74qx5mpssnia5tt6w o0ogyeekscug 2017 22 deep learning for visual computing debdoot sheet iit kgp nptel https onlinecourses nptel ac in noc18 ee08 preview notebooks https github com iitkliv dlvcnptel youtube lectures https www youtube com playlist list pluv3gm6 gse1biyakccxb3fan4wblyfwf 2018 23 the ancient secrets of computer vision joseph redmon ali farhadi tascv https pjreddie com courses computer vision tascv uw https courses cs washington edu courses cse455 18sp youtube lectures https www youtube com playlist list pljmxczuzeychvw5yysu92wry8iwhtuq7p 2018 24 modern robotics kevin lynch northwestern robotics modern robot http hades mech northwestern edu index php modern robotics youtube lectures https www youtube com playlist list plgglp4f rq02vx0oqq5vrcxbjrzamydfx 2018 25 digial image processing alex bronstein technion cs236860 https vistalab technion github io cs236860 info youtube lectures https www youtube com playlist list plm0a6z788yazoxuywda9y3n i2upij1ep 2018 26 mathematics of imaging variational methods and optimization in imaging lots of legends institut henri poincar workshop 1 http www ihp fr sites default files conf1 04 au 08 fevr imaging2019 pdf youtube lectures https www youtube com playlist list pl9kd4mpdvwcazd5aq p1trlliyckrloxw 2019 27 deep learning for video xavier gir upc barcelona deepvideo https mcv m6 video github io deepvideo 2019 youtube lectures https www youtube com playlist list pl 5emc3hqtbbpy 627gornj09pzrnqgpd 2019 28 statistical modeling for shapes and imaging lots of legends institut henri poincar paris workshop 2 https imaging in paris github io semester2019 workshop2prog youtube lectures https www youtube com playlist list pl9kd4mpdvwcazd5aq p1trlliyckrloxw 2019 29 imaging and machine learning lots of legends institut henri poincar paris workshop 3 https imaging in paris github io semester2019 workshop3prog youtube lectures https www youtube com playlist list pl9kd4mpdvwcazd5aq p1trlliyckrloxw 2019 30 computer vision jayanta mukhopadhyay iit kgp cv nptel https nptel ac in courses 106 105 106105216 youtube lectures https nptel ac in courses 106 105 106105216 2019 31 deep learning for computer vision justin johnson umichigan eecs 498 007 https web eecs umich edu justincj teaching eecs498 lecture videos http leccap engin umich edu leccap site jhygcph151x25gjj1f0 br youtube lectures https www youtube com playlist list pl5 tkqafazfbzxjbhtzdvcwe0zbhomg7r 2019 32 sensors and state estimation 2 cyrill stachniss university of bonn none youtube lectures https www youtube com playlist list plgnqpqtftogqh j16imwdlji18swq2pz6 s2020 33 computer vision iii detection segmentation and tracking laura leal taix tu m nchen cv3dst https dvl in tum de teaching cv3dst ss20 youtube lectures https www youtube com playlist list plog3nopcjkbnegyffektlxxmfv1otkmcs s2020 34 advanced deep learning for computer vision laura leal taix and matthias nie ner tu m nchen adl4cv https dvl in tum de teaching adl4cv ss20 youtube lectures https www youtube com playlist list plog3nopcjkbnjhuhmixu4ise4z4f2jm39 s2020 35 computer vision foundations fred hamprecht universit t heidelberg cvf https hci iwr uni heidelberg de ial cvf youtube lectures https www youtube com playlist list plurasnb3n4krabnmiygd77hyogzo9wpde ss2020 36 mit vision seminar lots of legends mit mit vision https sites google com view visionseminar past talks youtube lectures https www youtube com channel uclmifkfyfcnnzs6iwylpi9g videos 2015 now 37 tum ai guest lectures lots of legends technische universit t m nchen tum ai https niessner github io tum ai lecture series youtube lectures https www youtube com playlist list plq8y4kiibzy8kmlz7crqz bjbdywsflxt 2020 now 38 seminar on 3d geometry vision lots of legends virtual 3dgv seminar https 3dgv github io youtube lectures https www youtube com playlist list plzk0jtn0g8e xvtfsiv67q8iz1czo fpv 2020 now 39 event based robot vision guillermo gallego technische universit t berlin evis ss20 https sites google com view guillermogallego teaching event based robot vision youtube lectures https www youtube com playlist list pl03gm3nzjvgufyuh3v5x8jvonjrgfcal8 2020 now 40 deep learning for computer vision vineeth balasubramanian iit hyderabad dl cv 20 https onlinecourses nptel ac in noc20 cs88 preview youtube lectures https www youtube com playlist list plyqspqzte6m pi riz4o1jegffhju9ggg 2020 41 deep learning for visual computing peter wonka kaust sa none youtube lectures https www youtube com playlist list plmpqleui13s2dhbw6kttxwqma8rehlfze 2020 42 computer vision yogesh rawat university of central florida cap5415 cv https www crcv ucf edu courses cap5415 fall 2020 schedule youtube lectures https www youtube com playlist list pld3hlsjsx ikm5il1hgmdb z62beoikfx f2020 43 multimedia signal processing mark hasegawa johnson uiuc ece 417 msp https courses engr illinois edu ece417 fa2020 lecture videos https mediaspace illinois edu channel ece 20417 26816181 f2020 44 computer vision andreas geiger universit t t bingen comp vis https uni tuebingen de fakultaeten mathematisch naturwissenschaftliche fakultaet fachbereiche informatik lehrstuehle autonomous vision lectures computer vision youtube lectures https www youtube com playlist list pl05ump7r6ij35l2mhgzis8aehz7mg381 s2021 45 3d computer vision lee gim hee national univeristy of singapura none youtube lectures https www youtube com playlist list plxg0cgqviygp47ervqhw v7fvnuovjeaz 2021 46 deep learning for computer vision fundamentals and applications t dekel et al weizmann institute of science dl4cv https dl4cv github io schedule html youtube lectures https www youtube com playlist list pl z2 u9mijdngfm7 f2fz9zxjvrp jhjv s2021 47 current topics in ml methods in 3d and geometric deep learning animesh garg others university of toronto csc 2547 http www pair toronto edu csc2547 w21 youtube lectures https www youtube com channel ucrsmaxnwu6sgccwevw12dfg videos 2021 48 first principles of computer vision shree k nayar columbia university fpcv https fpcv cs columbia edu youtube lectures https www youtube com channel ucf0wb91t8ky6auycqv0cclw videos 2021 49 self driving cars andreas geiger universit t t bingen sdc 21 https uni tuebingen de de 123611 youtube lectures https www youtube com playlist list pl05ump7r6ij321zzkxk6xcqxaaayjqbzr w2021 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus 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lkre13rmuc2aybrvvaxo5demibh 2013 3 machine learning summer school lots of legends mpi is t bingen mlss 13 http mlss tuebingen mpg de 2013 2013 index html youtube lectures https www youtube com playlist list plqjm7rc5 exfv6rxapzzzlzo93hl0v91e 2013 4 graduate summer school computer vision lots of legends ipam ucla gss cv http www ipam ucla edu programs summer schools graduate summer school computer vision video lectures http www ipam ucla edu programs summer schools graduate summer school computer vision tab schedule 2013 5 machine learning summer school lots of legends reykjavik university mlss 14 http mlss2014 hiit fi youtube lectures https www youtube com playlist list plqdbxunkqow2nkn7vxyqirkwcqrkqyosf 2014 6 machine learning summer school lots of legends pittsburgh mlss 14 http www mlss2014 com youtube lectures https www youtube com playlist list plzso 6 bsqhqciyxe3ycglxhmjk3xv7iz 2014 7 deep learning summer school lots of legends universit de montr al dlss 15 https sites google com site deeplearningsummerschool home youtube lectures http videolectures net deeplearning2015 montreal 2015 8 biomedical image analysis summer school lots of legends centralesupelec paris none youtube lectures https www youtube com playlist list plgshh6boff5ujaut4zriazc ofxolkagk 2015 9 mathematics of signal processing lots of legends hausdorff institute for mathematics sigproc http www him uni bonn de signal processing 2016 youtube lectures https www youtube com playlist list plul8lct3ajqsqo3lr5rbwxj92rsgrudtx 2016 10 microsoft research machine learning course s v n vishwanathan and prateek jain ms research none youtube lectures https www youtube com playlist list pl34iye0uxtxo7vpxgfkmm6kbgzqwjf9kf 2016 11 deep learning summer school lots of legends universit de montr al dl ss 16 https sites google com site deeplearningsummerschool2016 home youtube lectures https www youtube com playlist list pl5bqic6xopcbb fvnhmd1nevlqkwgzqyr 2016 12 lisbon machine learning school lots of legends instituto superior t cnico portugal lxmls 16 http lxmls it pt 2016 youtube lectures https www youtube com playlist list pltolj8m4ao fymxxbiou6sf1ngflb5eix 2016 13 machine learning advances and applications seminar lots of legends fields institute university of toronto mlaas 16 http www fields utoronto ca activities 16 17 machine learning youtube lectures https www youtube com playlist list plfsvaysmwskuqcrkudapp40lx i08d0qk br video lectures http www fields utoronto ca video archive event 2267 2016 2017 14 machine learning advances and applications seminar lots of legends fields institute university of toronto mlaas 17 http www fields utoronto ca activities 17 18 machine learning video lectures http www fields utoronto ca video archive event 2487 2017 2018 15 machine learning summer school lots of legends mpi is t bingen mlss 17 http mlss tuebingen mpg de 2017 index html youtube lectures https www youtube com playlist list plqjm7rc5 exfuovoycdkikfck8yeucnl9 2017 16 representation learning lots of legends simons institute replearn https simons berkeley edu workshops abstracts 3750 youtube lectures https www youtube com playlist list plgkuh lkre13unv4ztswuxciuz7x zdhz 2017 17 foundations of machine learning lots of legends simons institute ml bootcamp https simons berkeley edu workshops abstracts 3748 youtube lectures https www youtube com playlist list plgkuh lkre11gbzwneln vzdlhyejo7yd 2017 18 optimization statistics and uncertainty lots of legends simons institute optim stats https simons berkeley edu workshops abstracts 4795 youtube lectures https www youtube com playlist list plgkuh lkre13acd44z2fh ivp1e8ip5jo 2017 19 deep learning theory algorithms and applications lots of legends tu berlin dl taa http doc ml tu berlin de dlworkshop2017 youtube lectures https www youtube com playlist list pljozdkh8t5kqcnv v1w2tapvtjdzyiohw 2017 20 deep learning and reinforcement learning summer school lots of legends universit de montr al dlrl 2017 https mila quebec en cours deep learning summer school 2017 lecture videos http videolectures net deeplearning2017 montreal 2017 21 statistical physics methods in machine learning lots of legends international centre for theoretical sciences tifr spmml https www icts res in discussion meeting spmml2017 youtube lectures https www youtube com playlist list pl04qvxpjcnjhtl3iivyfrmogdhwtpn7yj 2017 22 lisbon machine learning school lots of legends instituto superior t cnico portugal lxmls 17 http lxmls it pt 2017 youtube lectures https www youtube com playlist list pltolj8m4ao furfnzejccnvuw2 fej73n 2017 23 interactive learning lots of legends simons institute berkeley il 2017 https simons berkeley edu workshops schedule 3749 youtube lectures https www youtube com playlist list plgkuh lkre10t2pof wzxh0ckdpyvanux 2017 24 computational challenges in machine learning lots of legends simons institute berkeley ccml 17 https simons berkeley edu workshops schedule 3751 youtube lectures https www youtube com playlist list plgkuh lkre12exz4dnvc8oervo2 af4iu 2017 25 foundations of data science lots of legends simons institute ds bootcamp https simons berkeley edu workshops abstracts 6680 youtube lectures https www youtube com playlist list plgkuh lkre13r1qrnrejj3f498 nursf3 2018 26 deep learning and bayesian methods lots of legends hse moscow dlbm ss http deepbayes ru 2018 youtube lectures https www youtube com playlist list ple5rnuydzv9q01vwcp9bv7nhjg3j7mz62 2018 27 new deep learning techniques lots of legends ipam ucla ipam workshop https www ipam ucla edu programs workshops new deep learning techniques tab schedule youtube lectures https www youtube com playlist list plhyi3fbmv0sdm0zxj31hwjg9t9q0v2xyn 2018 28 deep learning and reinforcement learning summer school lots of legends university of toronto dlrl 2018 https dlrlsummerschool ca 2018 event lecture videos http videolectures net dlrlsummerschool2018 toronto 2018 29 machine learning summer school lots of legends universidad aut noma de madrid spain mlss 18 http mlss ii uam es mlss2018 index html youtube lectures https www youtube com channel ucbpjhr eior 7jfh3hmvhq videos br course videos http mlss ii uam es mlss2018 speakers html 2018 30 theoretical basis of machine learning lots of legends international centre for theoretical sciences tifr tbml 18 https www icts res in discussion meeting tbml2018 lecture videos https www icts res in discussion meeting tbml2018 talks br youtube videos https www youtube com playlist list pl04qvxpjcnjj1dgnxxfbo2fksju4r ggr 2018 31 polish view on machine learning lots of legends warsaw plinml 18 https plinml mimuw edu pl youtube videos https www youtube com playlist list ploawrlj9tdhpca6n9dzq6gpxboyuumdrp 2018 32 big data analysis in astronomy lots of legends tenerife bdaa 18 http research iac es winterschool 2018 pages book ws2018 php youtube lectures https www youtube com playlist list plm4pv4kyyzgx42w5psp3itetp0u penti 2018 33 machine learning advances and applications seminar lots of legends fields institute university of toronto mlass http www fields utoronto ca activities 18 19 machine learning video lectures http www fields utoronto ca video archive event 2681 2018 2019 34 mifods ml stats toc seminar lots of legends mit mifods seminar http mifods mit edu seminar php lecture videos http mifods mit edu seminar php 2018 2019 35 learning machines seminar series lots of legends cornell tech lmss https lmss tech cornell edu youtube lectures https www youtube com playlist list plycw2yy79juxbqz9uheu ns3cgnomhl2a 2018 now 36 machine learning summer school lots of legends south africa mlss 19 https mlssafrica com programme schedule youtube lectures https www youtube com channel uc722cmqvgcltxt jxr3rywg videos 2019 37 deep learning boot camp lots of legends simons institute berkeley dlbc 19 https simons berkeley edu workshops schedule 10624 youtube lectures https www youtube com playlist list plgkuh lkre12c2il9mnx0cmp9z4ofnrqh 2019 38 frontiers of deep learning lots of legends simons institute berkeley fodl 19 https simons berkeley edu workshops schedule 10627 youtube lectures https www youtube com playlist list plgkuh lkre11eku7g z qsvjdd8ct hi9 2019 39 mathematics of data structured representations for sensing approximation and learning lots of legends the alan turing institute london mod 19 https www turing ac uk sites default files 2019 05 agenda 9 3 pdf youtube lectures https www youtube com playlist list plud sqltxsdx w1ztexpzl ejgfqskwez 2019 40 deep learning and bayesian methods lots of legends hse moscow dlbm ss http deepbayes ru youtube lectures https www youtube com playlist list ple5rnuydzv9qhe8vdstpu0o8yp63oecdw 2019 41 the mathematics of deep learning and data science lots of legends isaac newton institute cambridge modl ds https gateway newton ac uk event ofbw46 lecture videos https gateway newton ac uk event ofbw46 programme 2019 42 geometry of deep learning lots of legends msr redmond godl https www microsoft com en us research event ai institute 2019 youtube lectures https www youtube com playlist list pld7hfcn7lxre30qq36it2xcljxc340o d 2019 43 deep learning for science school many folks lbnl berkeley dlfss https dl4sci school lbl gov agenda youtube lectures https www youtube com playlist list pl20s5eeaposvfveyhcpouzu7zkbcr5 el 2019 44 emerging challenges in deep learning lots of legends simons institute berkeley ecdl https simons berkeley edu workshops schedule 10629 youtube lectures https www youtube com playlist list plgkuh lkre10bpafdrv0fg2vnuwewxwvd 2019 45 full stack deep learning pieter abbeel and many others uc berkeley fsdl m19 https fullstackdeeplearning com march2019 youtube lectures day 1 https www youtube com playlist list pl1t8fo7arwlcf3hc4vmevblh8hzm nbeb br day 2 https www youtube com playlist list pl1t8fo7arwlf6twwdstb pcwlubnlrkrm 2019 46 algorithmic and theoretical aspects of machine learning lots of legends iiit bengaluru acm ml https india acm org education machine learning br nptel https nptel ac in courses 128 106 128106011 youtube lectures https nptel ac in courses 128 106 128106011 2019 47 deep learning and reinforcement learning summer school lots of legends amii edmonton canada dlrl 2019 https dlrlsummerschool ca past years youtube lectures https www youtube com playlist list plklhhkvvu8 axmpqznyg e 2ntd0tje8v 2019 48 mathematics of machine learning summer graduate school lots of legends university of washington moml sgs http www msri org summer schools 866 schedule moml ss http mathofml cs washington edu youtube lectures https www youtube com playlist list pltpqex 31jxhgucush5j7ogneorofocw9 2019 49 workshop on theory of deep learning where next lots of legends ias princeton university wtdl https www math ias edu wtdl youtube lectures https www youtube com playlist list plddzb3twjpz5dqqg s rgjqsfeh4dqqfq 2019 50 computational vision summer school lots of legends black forest germany cvss 2019 http orga cvss cc program cvss 2019 youtube lectures https www youtube com playlist list plecnfjwzkqxsvidolvltwq9s7sisx1qtc 2019 51 learning under complex structure lots of legends mit lucs https mifods mit edu complex php youtube lectures https www youtube com playlist list plm4pv4kyyzgwhihcay6zyr7m9hhfo4vud 2020 52 machine learning summer school lots of legends mpi is t bingen virtual mlss http mlss tuebingen mpg de 2020 schedule html youtube lectures https www youtube com channel ucbogpkdhquyevvjuzs5wtxw videos ss2020 53 eastern european machine learning summer school lots of legends krak w poland virtual eeml https www eeml eu program youtube lectures https www youtube com playlist list plaky4p4v3ge1j01foy2feglv4jrntqj84 s2020 54 lisbon machine learning summer school lots of legends lisbon portugal virtual lxmls http lxmls it pt 2020 page id 19 youtube lectures https www youtube com channel uckvfzwgt1jr75uvslgp9 mw s2020 55 workshop on new directions in optimization statistics and machine learning lots of legends institute of advanced study princeton ml opt new dir https www ias edu video workshop 2020 0415 16 youtube lectures https www youtube com playlist list plddzb3twjpz4ri6i0midesiepyk4lx17q 2020 56 mediterranean machine learning school lots of legends italy virtual m2l school https www m2lschool org talks youtube lectures https www youtube com playlist list plf wkqrv4u1yrbfnwn8cxxyrmxld sked 2021 57 mathematics of machine learning one world seminar lots of legends virtual 1w ml https sites google com view oneworldml past events youtube lectures https www youtube com channel ucz7wlgxs20czugkfxhfcnfg videos 2020 now 58 deep learning theory summer school lots of legends princeton university virtual dlt 21 https deep learning summer school princeton edu youtube lectures https www youtube com playlist list pl2mb9ggluejj fnjj8rwgz4nc hcsxfmu 2021 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus 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minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage lecture videos year 1 artificial general intelligence lots of legends mit 6 s099 agi https agi mit edu lecture videos https agi mit edu 2018 2019 2 ai podcast lots of legends mit ai pod https lexfridman com ai youtube lectures https www youtube com playlist list plraxtmerzgodp 8gztsuki9nrranbkkp4 2018 2019 3 nyu ai seminars lots of legends nyu modern ai https engineering nyu edu academics departments electrical and computer engineering ece seminar series modern artificial youtube lectures https www youtube com playlist list plhwo5ntex8iy9xhpswwas451ngvuqbe7u 2017 now 4 deep learning alchemy or science lots of legends institute for advanced study princeton dlas https video ias edu deeplearning 2019 0222 br agenda https www math ias edu tml dlasagenda youtube lectures https www youtube com playlist list plddzb3twjpz7aaxhihalboh8l6 uxmmnp 2019 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus 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sign heavy minus sign white large square optimization courses which form the foundation for ml dl rl white large square computer vision courses which are dl ml heavy white large square speech recognition courses which are dl heavy white large square structured courses on geometric graph neural networks white large square section on autonomous vehicles white large square section on computer graphics with ml dl focus heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign around the web earth asia montreal ai http www montreal ai ai4all pdf upc dlai 2018 https telecombcn dl github io 2018 dlai upc dlai 2019 https telecombcn dl github io dlai 2019 www hashtagtechgeek com https www hashtagtechgeek com 2019 10 250 machine learning deep learning videos courseware html upc barcelona idl 2020 https telecombcn dl github io idl 2020 upc dlai 2020 https telecombcn dl github io dlai 2020 heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign contributions pray if you find a course that fits in any of the above categories i e dl ml rl cv nlp and the course has lecture videos with slides being optional then please raise an issue or send a pr by updating the course according to the above format heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign support moneybag optional if you re a kind samaritan and want to support me please do so if possible for which i would eternally be thankful and most importantly your contribution imbues me with greater motivation to work particularly in hard times pray https www paypalobjects com en us i btn btn donatecc lg gif https www paypal com cgi bin webscr cmd s xclick hosted button id nt3eats5n35wu vielen lieben dank blue heart heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign gift heart mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board mortar board gift heart heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign | machine-learning deep-learning deep-neural-networks pattern-recognition computer-vision optimization visual-recognition reinforcement-learning deep-reinforcement-learning natural-language-processing artificial-neural-networks artificial-intelligence-algorithms probabilistic-graphical-models bayesian-statistics speech-recognition graph-neural-networks medical-imaging geometric-deep-learning explainable-ai probability | ai |
kallisti | kallisti chaos engineering framework across private public hybrid cloud environments build status https github com jpmorganchase kallisti actions workflows build yml badge svg https github com jpmorganchase kallisti actions workflows build yml manage chaos engineering practices as data kallisti is a decentralized control plane to test the resiliency of cloud native applications by helping you define execute and manage the chaos experiments it abstracts chaos testing capabilities and credential handlers as steps modules allowing the flexible design of chaos testing scenarios in a secure manner img src docs images kallisti overview png width 600 alt kallisti experiment overview background project kallisti is the open source fork of a chaos engineering framework that originally started within jpmorgan chase in 2018 today kallisti is used by a number of applications for chaos testing within jpmorgan chase it is now the upstream source https github com jpmorganchase kallisti core of our internal chaos engine and continues to be actively maintained keep an eye on our roadmap roadmap for more to come chaos injection capabilities kallisti ships with a number of chaos injection capabilities in various modules based on the different chaostoolkit https github com chaostoolkit libraries additional chaos injection can also be developed and imported into kallisti check out the customization page customization for more details component module capabilities kubernetes aws eks k8s pod replicaset termination network drop and more istio istio envoy s http fault latency filters aws aws ec2 ecs rds elb termination lambda restriction and more cloud foundry cf container service termination network drop and more prometheus prom metrics data retrieval with promql common cm http probe requests and wait list of contents project concept concept overview and concept of kallisti road map roadmap what is planned for kallisti in future contribution to kallisti contribution guideline for contribution getting started how to use kallisti how to use deployment to chaos test execution chaos modules modules chaos injection modules and capabilities credentials for chaos steps step credentials credentials for chaos testing steps access control access control controlling the access to kallisti api through oauth2 oidc integration via jwt tokens or any other authentication mechanism through a custom configuration customization customization custom modules credentials hooks and more other features result validation expectation result of each step in a chaos experiment can be verified to specific values or range expected experiment templating experiment template experiment definition can be interpolated with parameters authentication for http steps http step auth auth for http request steps scheduling scheduling recurring experiments with cron expression reporting reporting xml export through api notification notification email upon completion of an experiment access control docs access control md contribution docs contribution md customization docs customization md expectation docs expectation md experiment template docs experiment template md how to use docs how to use md modules docs modules md notification docs notification md concept docs concept md reporting docs reporting md roadmap docs roadmap md scheduling docs scheduling md http step auth docs http step auth md step credentials docs step credentials md | chaos-engineering jpmorganchase | cloud |
kwyjibo | kwyjibo brm io kwyjibo http brm io kwyjibo kwy ji bo n a big dumb balding north american ape with no chin and a short temper bart simpson http www youtube com watch v mtm6tjapzfi kwyjibo is also an experimental automated referee for scrabble games it was built to explore real time ocr computer vision and machine learning as the subject of my dissertation at the university of sheffield http www shef ac uk in 2011 about you can read a little bit about how it works in this explanation http brm io real time ocr much of the image processing is done using the aforge net http www aforgenet com computer vision library there s also a video http www youtube com watch v c3ywtfetqoe of it working note this project was for academic research only and is not associated with scrabble license kwyjibo is licensed under the mit license mit http opensource org licenses mit br copyright c 2014 liam brummitt | ai |
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prg4-javascript-2023 | javascript startproject 2023 oefening week 1 modern web development klik op use this template kies als owner jouw eigen github account geef het project een coole naam en check dat dit startproject nu in je eigen github staat clone de repository van jouw eigen github naar je lokale computer via de git url die vind je onder code voor vs code klik het source control icoontje kies voor clone repository en plak de git url voor phpstorm klik op get from vcs plak de git link bij url kies een map die je ook via localhost kan openen bijvoorbeeld xampp htdocs mijnproject jouw ide haalt nu het startproject op en opent het automatisch typ npm install en npm run dev in de terminal in vs code bekijk het instructie filmpje https youtu be uivpe4l5 p4 lees verder over het publiceren op github pages https github com hr cmgt prg04 2022 2023 blob main setup md | front_end |
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Blockchain-based-E-Voting | blockchain based e voting this project aims at implementing a voting system based on blockchain technology it is a secure transparent and decentralized way of voting it converts ballots into transactions and securely mines blocks out of them the advantage of a blockchain based voting system include the ability to vote from any place and prevent any tampering of votes technology stack used 1 python 3 7 x 2 django web framework 3 0 3 3 bootstrap 4 4 db sqlite 3 5 html5 to run the project 1 clone the repository 2 cd into the folder and run pip install django 3 python3 manage py runserver screenshots img width 960 alt vote verification src https user images githubusercontent com 54449305 80915076 5679dc80 8d6d 11ea 9650 3fe960bd9896 png img width 960 alt user authentication src https user images githubusercontent com 54449305 80915092 73161480 8d6d 11ea 923a 15f4788d8e40 png vote page https user images githubusercontent com 54449305 80915104 8a550200 8d6d 11ea 9880 d0c111d4d096 png contributors 1 aniket jayateerth 2 soureesh patil 3 rishabh agarwal 4 anup nair this is only a demonstration of the blockchain based voting system and it is entirely a prototype of the technology | blockchain e-voting ballot django merkle-tree voting | blockchain |
blockchain_trace | hyberledger fabric ip docker docker orderder 7050 0 7051 7053 5984 1 10051 10053 7984 sdk orderer 1 23 hash hash 1552751522097 c users huanxi appdata roaming typora typora user images 1552751522097 png 2 haunxi id 2 500g id 1552751693290 c users huanxi appdata roaming typora typora user images 1552751693290 png 1 peer0 1552751817529 c users huanxi appdata roaming typora typora user images 1552751817529 png 1 peer1 1552751901138 c users huanxi appdata roaming typora typora user images 1552751901138 png 2 peer0 1552751953623 c users huanxi appdata roaming typora typora user images 1552751953623 png 2 peer1 1552751936685 c users huanxi appdata roaming typora typora user images 1552751936685 png 4 4 1552752113637 c users huanxi appdata roaming typora typora user images 1552752113637 png 1552752129842 c users huanxi appdata roaming typora typora user images 1552752129842 png 1552752144453 c users huanxi appdata roaming typora typora user images 1552752144453 png 1552752163204 c users huanxi appdata roaming typora typora user images 1552752163204 png previoushash currenthash 1552752253958 c users huanxi appdata roaming typora typora user images 1552752253958 png 23 1 ws key 2 couchdb 1552752376946 c users huanxi appdata roaming typora typora user images 1552752376946 png 1552752684445 c users huanxi appdata roaming typora typora user images 1552752684445 png 24 ws 2 process info 1552752816957 c users huanxi appdata roaming typora typora user images 1552752816957 png couchdb | blockchain |
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ember-toucan-core | p align center a href https www crowdstrike com img src https github com crowdstrike ember toucan core blob main github cs logo png raw true alt crowdstrike logo width 300 a p h1 align center the toucan design system from crowdstrike h1 br ci status https github com crowdstrike ember toucan core actions workflows ci yml badge svg branch main br toucan provides a set of accessible and reusable components that make it easy to create visually consistent and efficient ember applications this repository is a monorepo publishing two packages ember toucan core ember toucan form the core package contains the toucan styled ember components the form package allows users to build forms using ember headless form https github com crowdstrike ember headless form with the core components usage visit our documentation website https ember toucan core pages dev compatibility ember js 4 8 or above embroider or ember auto import v2 0 or above glint https typed ember gitbook io glint installation to use the presentational components in your ember apps and addons run one of the following bash pnpm add crowdstrike ember toucan core or yarn add crowdstrike ember toucan core or npm install crowdstrike ember toucan core or ember install crowdstrike ember toucan core if want to use our ember headless form https github com crowdstrike ember headless form abstraction that exposes the core form components run one of the following bash pnpm add crowdstrike ember toucan form or yarn add crowdstrike ember toucan form or npm install crowdstrike ember toucan form or ember install crowdstrike ember toucan form contributing see the contributing contributing md guide for details | os |
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mad_project | n side mobile app table of contents 1 introduction introduction 2 features features 3 team members team members 4 technical details technical details introduction welcome to the n side mobile app repository our innovative mobile application is designed to elevate the university experience for students by offering a range of essential features developed for the android platform this app leverages the power of flutter to provide a seamless user interface and incorporates firebase for efficient backend services key highlights lecture schedule keep track of your daily lecture timetable right within the app ensuring you never miss a class ar navigation experience the future of navigation with our ar powered feature guiding you to your lecture halls on campus with unmatched precision available lecturers check the real time availability status of your lecturers making it easier to plan your consultations and academic interactions hall availability quickly find out which halls are currently available for your group discussions team meetings or personal study sessions features lecture schedule never miss a class again keep track of your day to day lecture timetable right within the app view and manage your schedule effortlessly ar navigation navigate your campus with ease using augmented reality ar technology let our app guide you to your lecture halls with unmatched precision available lecturers check the real time availability status of your lecturers plan your consultations and academic interactions more efficiently hall availability find out which halls are currently available for your group discussions team meetings or personal study sessions make the most of your campus resources team members nawam sahasra nawam727 https github com nawam727 project lead flutter backend developer spearheading the project developing core features and ensuring seamless integration between frontend and backend nuthara yuthmini nuthara https github com nuthara ui ux designer flutter developer crafting an intuitive and visually appealing user experience while also contributing to flutter development naveen silva naveensilva https github com naveensilva flutter backend developer bringing the app to life through flutter and working on backend functionalities using firebase kavindi viranga kavindiviranga https github com kavindiviranga ui ux designer flutter developer designing user interfaces that seamlessly blend aesthetics with functionality in the app sadeep amantha amantha07 https github com amantha07 ui ux designer flutter developer contributing to ui ux design and flutter development ensuring a delightful user experience moksha dilshan mokshadill https github com mokshadill flutter ar backend developer pioneering the ar navigation feature using unity game engine integrating it into the app for accurate indoor navigation ar development part https github com mokshadill mad ar developmentpart technical details the n side mobile app is developed using the following technologies frontend flutter backend firebase ar navigation augmented reality ar technology for more technical details and documentation please refer to the technical documentation md file in the repository thank you for your interest in the n side mobile app we hope you find it valuable in enhancing your university experience if you have any questions feedback or suggestions please don t hesitate to contact us happy coding | front_end |
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cs3210-rustos-public | cs3210 lab assignments this repository contains lab assignments for georgia tech cs3210 design of operating systems the latest course material is available here https tc gts3 org cs3210 2020 spring index html who should take cs3210 anyone wants to work on challenges in operating systems anyone cares about what s going on under the hood anyone has to build high performance systems e g cloud trading anyone wants to build embedded iot firmware e g robot anyone needs to diagnose bugs or security problems why rust historically c has been mainly used for os development because of its portability minimal runtime direct hardware memory access and decent usability rust provides all of these features with addition of memory safety guarantee strong type system and modern language abstractions which help programmers to make less mistakes when writing code acknowledgement we built our labs based on the materials originally developed for cs140e an experimental course on operating systems https cs140e sergio bz by sergio benitez https sergio bz we have ported it to use newer toolchains such as rust 2018 edition cargo xbuild instead of xargo and no std rust with a minimal shim library instead of custom built std we ve also developed it further to include topics such as virtual memory management multicore scheduling mutex designing and implementing a networking stack | os |
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Databases | bases de datos oracle covid database and twitter sql php were two assignments for the 2021 1 databases course of computer science engineering career at universidad t cnica federico santa mar a chile oracle covid database a global pandemic has been declared known as covid 21 this pandemic affects to the whole world population but mostly to chile s population given two csv files confirmed cases by city and confirmed cases by region a database is built with the following characteristics created on oracle sql a menu needs to be created by using python 3 and the commands are create city create region see total cases of a certain city see total cases of a certain region see total cases of all cities see total cases of all regions add new cases to a city by adding n cases to existing confirmed cases delete new cases to a city by substracting n cases to existing confirmed cases combine cities combine regions top 5 cities with more cases percentage according to its population top 5 regions with more cases percentage according to its population due to the existing crisis on this country if a region has more than 15 of its population with confirmed cases a notification will be shown and the region will be deleted of the country twitter sql php usmwer is a new social network where users usmers can write messages usmitos with a maximum length of 279 characters the usmers can follow each other reusmear like and reply usmitos the close friends characteristic allows to usmers that follow each other to see private usmitos also the lists feature allows usmers to create a personalized feed watching only the accounts they want a usmer can deactivate or delete his account if an account is deactivated the usmer dissapears of usmwer with his lists and usmitos but all this is not deleted from the database because the usmer can come back anytime reactivate his account and using it normally with all previous characteristics note this implementation has only the possibility to publicate usmitos reusmear like create and delete accounts see a profile and have information about the usmer reply function close friends lists and deactivation have not been implemented but could be in the future jos p southerland silva 2021 | twitter-clone oracle php python-cx-oracle covid19-data | server |
zeitgeist | a href https zeitgeist pm img src gh banner jpg a zeitgeist an evolving blockchain for prediction markets and futarchy rust https github com zeitgeistpm zeitgeist workflows rust badge svg codecov https codecov io gh zeitgeistpm zeitgeist branch main graph badge svg https codecov io gh zeitgeistpm zeitgeist discord https img shields io badge discord https 3a 2f 2fdiscord gg 2fmd3tbh3ctv purple https discord gg md3tbh3ctv telegram https img shields io badge telegram https 3a 2f 2ft me 2fzeitgeist official blue https t me zeitgeist official zeitgeist is a decentralized network for creating betting on and resolving prediction markets the platform s native currency the ztg is used to sway the direction of the network and as a means of last call dispute resolution additionally zeitgeist is a protocol for efficient trading of prediction market shares and will one day become the backbone of the decentralized finance ecosystem by allowing traders to create complex financial contracts on virtually anything modules authorized zrml authorized offers authorized resolution of disputes court zrml court an implementation of a court mechanism used to resolve disputes in a decentralized fashion global disputes zrml global disputes global disputes sets one out of multiple outcomes with the most locked ztg tokens as the canonical outcome this is the default process if a dispute mechanism fails to resolve liquidity mining zrml liquidity mining this pallet implements the time based incentivization with zeitgeist tokens for continuously providing liquidity to swap pools market commons zrml market commons contains common operations on markets that are used by multiple pallets neo swaps zrml neo swaps an implementation of the logarithmic market scoring rule as constant function market maker tailor made for decentralized combinatorial markets and futarchy orderbook v1 zrml orderbook v1 a naive orderbook implementation that s only part of zeitgeist s poc will be replaced by a v2 orderbook that uses 0x style hybrid on chain and off chain trading prediction markets zrml prediction markets the core implementation of the prediction market logic for creating and resolving markets simple disputes zrml simple disputes simple disputes selects the last dispute after a predetermined amount of disputes as the canonical outcome swaps zrml swaps an implementation of liquidity pools that allows any user to provide liquidity to the pool or swap assets in and out of the pool the market maker that is traded against is either a constant function market maker cfmm or a rikiddo market maker primitives zrml primitives contains custom and common types traits and constants rikiddo zrml rikiddo the module contains a completely modular implementation of our novel market scoring rule rikiddo rikiddo it also offers a pallet that other pallets can use to utilize the rikiddo market scoring rule rikiddo can be used by the automated market maker to determine swap prices how to build and run a zeitgeist node zeitgeist node comes in two flavors one for standalone self contained execution and another for kusama polkadot parachain integration to build the standalone version simply point to the top directory of this project and type bash cargo build release to build the parachain version execute the following command cargo build features parachain release optimized binaries release are usually used for production faster and smaller but this behavior is optional and up to you our current beta test network battery station zg beta runs as a parachain to connect your zeitgeist parachain node follow the tutorial at our documentation site bs docs alternatively you can run a non parachain node which is usually only necessary for testing purposes by executing the following command cargo run release bin zeitgeist node options and flags a common value for node options and flags is dev tmp which runs a local temporary development node using docker we publish the latest standalone and parachain version to the docker hub zg docker hub from where it can be pulled and ran locally to connect to the network with relatively low effort and high compatibility in order to fetch the latest docker image ensure you have docker installed locally then type or paste the following commands in your terminal for parachain zeitgeist node docker pull zeitgeistpm zeitgeist node parachain for standalone non parachain zeitgeist node docker pull zeitgeistpm zeitgeist node our current beta test network battery station zg beta runs as a parachain to connect your zeitgeist parachain node follow the tutorial at our documentation site bs docs alternatively you can run a non parachain node which is usually only necessary for testing purposes by executing the following command docker run zeitgeistpm zeitgeist node node options and flags bs docs https docs zeitgeist pm docs basic battery station ls lmsr https www eecs harvard edu cs286r courses fall12 papers oprs10 pdf rikiddo https blog zeitgeist pm introducing zeitgeists rikiddo scoring rule zg beta https blog zeitgeist pm zeitgeist beta zg docker hub https hub docker com r zeitgeistpm zeitgeist node | substrate prediction-markets blockchain futarchy governance | blockchain |
PCB-for-I.MX6 | pull commit push master ad09 vcs ad2022 windows work j5 j6 12v 12vin 12vin 12vin 5v core 12vin 5v ex 5v core 5v core gen 3v3 u15 lx connector u10 s153 s158 c137 c138 c139 5v ex hdmi u13 5v supply lvds j11 usb hub u9 vin u14 vin power light gen 3v3 uart u1 vcc j16 j17 hdmi u13 lv supply j13 usb hub rtc u12 vbat vdd key u2 vcc nrst user light button gen 3v3 rgmii 3v3 ethernet gen 3v3 vcc 1v2 ethernet vcc 1v2 vcc 1v2 rgmii 1v2 vcc 1v2 ethernet 3p3v connector u10 s149 s152 pwr in u15 en power light boot 2p5v connector u10 p150 p149 ethernet u8 nint nreset mdio dvddh 3 2p5v ethernet rgmii rgmii 3v3 rgmii 1v2 ethernet usb usb usb otg vbus usb h1 vbus 11 23 rain button 11 24 xrb 12mil 12 24mil n gnd gnd pwr 4 pcb 2 pcb hdmi 11 25 wz usb 11 26 rain button connector 11 27 xrb key15 r15 connector ethernet u8 key u8 connector ethernet r120 r125 r125 can r120 r125 ethernet lvds 11 28 wz rtc rtc u2 uart usb otg connector ethernet usb otg lvds power 11 29 xrb jtag layer mechanical21 18 23 connector gnd 1 2 1 3 2 4 3 4 default bind3 4 key usb sw1 connector connector bh pcb lvds zyc netc27 2 rb056l 40te25 d9 d10 u4 5v core 5v ex 5v 11 30 xrb 8 connector u2 u12 lvds u lvds connector validate img src document images problem png alt image 20221201120645966 style zoom 67 img src document images rules png alt image 20221201120827990 style zoom 67 u image 20221201120827990 document images tools png 12 1 wz ethernet uart usb otg lvds ethernet uart lvds ethernet ethernet pcb usb 12 2 zyc 12 4 xrb hdmi ad usb pcb wz ad net i2c usb gnd02 net pwr03 pwr03 net 12 5 12 6 wz usb otg usb host usb host 12 9 zyc 12 10 12 11 xrb pwr in d9 d10 pcb pcb usb update remove pcb button ethernet c107 sc0805 sc0603 qwq ethernet 0805 0603 update 0805 image 20221211221552984 document images color problem png image 20221211234321963 document images color problem2 png ethernet gnd connector 5v core vcc h5 h6 pin pcb 36mil 10mil 20mil 5mil u15 ethernet u3 u4 12 12 wz pwr 12 17 xrb gen 3v3 u11 vcc 1v2 2p5v rgmii 3v3 rgmii 1v2 5v ex 12vin gnd 12 21 xrb usb otg vbus u8 nc | os |
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FreeRTOS | the freertos 202212 00 https github com freertos freertos tree 202212 00 release updates freertos kernel freertos tcp coremqtt corepkcs11 corehttp corejson aws iot over the air updates ota aws iot device shadow aws iot jobs aws iot device defender backoff algorithm aws iot fleet provisioning coresntp sigv4 and freertos cellular interface libraries to their lts 2 0 https github com freertos freertos lts blob 202210 lts changelog md versions it also updates coremqtt agent to v1 2 0 to be compatible with coremqtt v2 x x and updates mbedtls to v3 2 1 this release also adds visual studio static library projects for the freertos kernel freertos tcp logging mbedtls corehttp and corepkcs11 with the addition of the static library projects all visual studio projects have been updated to use them additionally all demos dependent on coremqtt have been updated to work with coremqtt v2 x x getting started the freertos org https www freertos org website contains a freertos kernel quick start guide https www freertos org freertos quick start guide html a list of supported devices and compilers https www freertos org rtos ports html the api reference https www freertos org a00106 html and many other resources getting help you can use your github login to get support from both the freertos community and directly from the primary freertos developers on our active support forum https forums freertos org the faq https www freertos org faq html provides another support resource cloning this repository this repo uses git submodules https git scm com book en v2 git tools submodules to bring in dependent components note if you download the zip file provided by the github ui you will not get the contents of the submodules the zip file is also not a valid git repository if using windows because this repository and its submodules contain symbolic links set core symlinks to true with the following command git config global core symlinks true in addition to this either enable developer mode https docs microsoft com en us windows apps get started enable your device for development or whenever using a git command that writes to the system e g git pull git clone and git submodule update init recursive use a console elevated as administrator so that git can properly create symbolic links for this repository otherwise symbolic links will be written as normal files with the symbolic links paths in them as text this https blogs windows com windowsdeveloper 2016 12 02 symlinks windows 10 gives more explanation to clone using https git clone https github com freertos freertos git recurse submodules using ssh git clone git github com freertos freertos git recurse submodules if you have downloaded the repo without using the recurse submodules argument you need to run git submodule update init recursive repository structure this repository contains the freertos kernel a number of supplementary libraries including the lts ones and a comprehensive set of example projects many libraries including the freertos kernel are included as git submodules from their own git repositories kernel source code and example projects freertos source contains the freertos kernel source code submoduled from https github com freertos freertos kernel freertos demo contains pre configured example projects that demonstrate the freertos kernel executing on different hardware platforms and using different compilers supplementary library source code and example projects freertos plus source contains source code for additional freertos component libraries as well as select partner provided libraries these subdirectories contain further readme files and links to documentation freertos plus demo contains pre configured example projects that demonstrate the freertos kernel used with the additional freertos component libraries previous releases releases https github com freertos freertos releases contains older freertos releases freertos lab projects freertos lab projects are libraries and demos that are fully functional but may be experimental or undergoing optimizations and refactorization to improve memory usage modularity documentation demo usability or test coverage most freertos lab libraries can be found in the freertos labs repository https github com freertos freertos labs a number of freertos lab demos can be found in the freertos github organization https github com freertos by searching for lab or following this link https github com freertos q lab type language to the search results coremqtt agent demos the freertos coremqtt agent demos https github com freertos coremqtt agent demos repository contains demos to showcase use of the coremqtt agent https github com freertos coremqtt agent library to share an mqtt connection between multiple application tasks the demos show a single mqtt connection usage between multiple application tasks for interacting with aws services including over the air updates https docs aws amazon com freertos latest userguide freertos ota dev html device shadow https docs aws amazon com iot latest developerguide iot device shadows html device defender https docs aws amazon com iot latest developerguide device defender html alongside performing simple publish subscribe operations cbmc the freertos test cbmc proofs directory contains cbmc proofs to learn more about cbmc and proofs specifically review the training material here https model checking github io cbmc training in order to run these proofs you will need to install cbmc and other tools by following the instructions here https model checking github io cbmc training installation html | os |
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NTU_CVSD_2021 | ntu cvsd 2021 computer aided vlsi system design | os |
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database-engineering-guideline | database engineering guideline dot indonesia database engineering guideline such as designing indexing etc | server |
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Flutter-Firebase | flutter firebase a new flutter project getting started this project is a starting point for a flutter application a few resources to get you started if this is your first flutter project lab write your first flutter app https docs flutter dev get started codelab cookbook useful flutter samples https docs flutter dev cookbook for help getting started with flutter development view the online documentation https docs flutter dev which offers tutorials samples guidance on mobile development and a full api reference | server |
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system-design-101 | p a href https blog bytebytego com utm source site img src images banner jpg a p p align center a href https www youtube com channel uczgt6azoyjslhtc9dz0uotw youtube a a href https blog bytebytego com utm source site newsletter a p system design 101 explain complex systems using visuals and simple terms whether you re preparing for a system design interview or you simply want to understand how systems work beneath the surface we hope this repository will help you achieve that table of contents toc toc levels 2 communication protocols communication protocols rest api vs graphql rest api vs graphql how does grpc work how does grpc work what is a webhook what is a webhook how to improve api performance how to improve api performance http 1 0 http 1 1 http 2 0 http 3 0 quic http 10 http 11 http 20 http 30 quic soap vs rest vs graphql vs rpc soap vs rest vs graphql vs rpc code first vs api first code first vs api first http status codes http status codes what does api gateway do what does api gateway do how do we design effective and safe apis how do we design effective and safe apis tcp ip encapsulation tcpip encapsulation why is nginx called a reverse proxy why is nginx called a reverse proxy what are the common load balancing algorithms what are the common load balancing algorithms url uri urn do you know the differences url uri urn do you know the differences ci cd cicd ci cd pipeline explained in simple terms cicd pipeline explained in simple terms netflix tech stack ci cd pipeline netflix tech stack cicd pipeline architecture patterns architecture patterns mvc mvp mvvm mvvm c and viper mvc mvp mvvm mvvm c and viper 18 key design patterns every developer should know 18 key design patterns every developer should know database database a nice cheat sheet of different databases in cloud services a nice cheat sheet of different databases in cloud services 8 data structures that power your databases 8 data structures that power your databases how is an sql statement executed in the database how is an sql statement executed in the database cap theorem cap theorem types of memory and storage types of memory and storage visualizing a sql query visualizing a sql query sql language sql language cache cache data is cached everywhere data is cached everywhere why is redis so fast why is redis so fast how can redis be used how can redis be used top caching strategies top caching strategies microservice architecture microservice architecture what does a typical microservice architecture look like what does a typical microservice architecture look like microservice best practices microservice best practices what tech stack is commonly used for microservices what tech stack is commonly used for microservices why is kafka fast why is kafka fast payment systems payment systems how to learn payment systems how to learn payment systems why is the credit card called the most profitable product in banks how does visa mastercard make money why is the credit card called the most profitable product in banks how does visamastercard make money how does visa work when we swipe a credit card at a merchant s shop how does visa work when we swipe a credit card at a merchants shop payment systems around the world series part 1 unified payments interface upi in india payment systems around the world series part 1 unified payments interface upi in india devops devops devops vs sre vs platform engineering what is the difference devops vs sre vs platform engineering what is the difference what is k8s kubernetes what is k8s kubernetes docker vs kubernetes which one should we use docker vs kubernetes which one should we use how does docker work how does docker work git git how git commands work how git commands work how does git work how does git work git merge vs git rebase git merge vs git rebase cloud services cloud services a nice cheat sheet of different cloud services 2023 edition a nice cheat sheet of different cloud services 2023 edition what is cloud native what is cloud native developer productivity tools developer productivity tools visualize json files visualize json files automatically turn code into architecture diagrams automatically turn code into architecture diagrams linux linux linux file system explained linux file system explained 18 most used linux commands you should know 18 most used linux commands you should know security security how does https work how does https work oauth 2 0 explained with simple terms oauth 20 explained with simple terms top 4 forms of authentication mechanisms top 4 forms of authentication mechanisms session cookie jwt token sso and oauth 2 0 what are they session cookie jwt token sso and oauth 20 what are they how to store passwords safely in the database and how to validate a password how to store passwords safely in the database and how to validate a password explaining json web token jwt to a 10 year old kid explaining json web token jwt to a 10 year old kid how does google authenticator or other types of 2 factor authenticators work how does google authenticator or other types of 2 factor authenticators work real world case studies real world case studies netflix s tech stack netflixs tech stack twitter architecture 2022 twitter architecture 2022 evolution of airbnb s microservice architecture over the past 15 years evolution of airbnbs microservice architecture over the past 15 years monorepo vs microrepo monorepo vs microrepo how will you design the stack overflow website how will you design the stack overflow website why did amazon prime video monitoring move from serverless to monolithic how can it save 90 cost why did amazon prime video monitoring move from serverless to monolithic how can it save 90 cost how does disney hotstar capture 5 billion emojis during a tournament how does disney hotstar capture 5 billion emojis during a tournament how discord stores trillions of messages how discord stores trillions of messages how do video live streamings work on youtube tiktok live or twitch how do video live streamings work on youtube tiktok live or twitch toc communication protocols architecture styles define how different components of an application programming interface api interact with one another as a result they ensure efficiency reliability and ease of integration with other systems by providing a standard approach to designing and building apis here are the most used styles p img src images api architecture styles png style width 640px p soap mature comprehensive xml based best for enterprise applications restful popular easy to implement http methods ideal for web services graphql query language request specific data reduces network overhead faster responses grpc modern high performance protocol buffers suitable for microservices architectures websocket real time bidirectional persistent connections perfect for low latency data exchange webhook event driven http callbacks asynchronous notifies systems when events occur rest api vs graphql the diagram below shows a quick comparison between rest and graphql p img src images graphql jpg p graphql is a query language for apis developed by meta it provides a complete description of the data in the api and gives clients the power to ask for exactly what they need graphql servers sit in between the client and the backend services graphql can aggregate multiple rest requests into one query graphql server organizes the resources in a graph graphql supports queries mutations applying data modifications to resources and subscriptions receiving notifications on schema modifications how does grpc work rpc remote procedure call is called remote because it enables communications between remote services when services are deployed to different servers under microservice architecture from the user s point of view it acts like a local function call the diagram below illustrates the overall data flow for grpc p img src images grpc jpg p step 1 a rest call is made from the client the request body is usually in json format steps 2 4 the order service grpc client receives the rest call transforms it and makes an rpc call to the payment service grpc encodes the client stub into a binary format and sends it to the low level transport layer step 5 grpc sends the packets over the network via http2 because of binary encoding and network optimizations grpc is said to be 5x faster than json steps 6 8 the payment service grpc server receives the packets from the network decodes them and invokes the server application steps 9 11 the result is returned from the server application and gets encoded and sent to the transport layer steps 12 14 the order service receives the packets decodes them and sends the result to the client application what is a webhook the diagram below shows a comparison between polling and webhook p img src images webhook jpeg style width 680px p assume we run an ecommerce website the clients send orders to the order service via the api gateway which goes to the payment service for payment transactions the payment service then talks to an external payment service provider psp to complete the transactions there are two ways to handle communications with the external psp 1 short polling after sending the payment request to the psp the payment service keeps asking the psp about the payment status after several rounds the psp finally returns with the status short polling has two drawbacks constant polling of the status requires resources from the payment service the external service communicates directly with the payment service creating security vulnerabilities 2 webhook we can register a webhook with the external service it means call me back at a certain url when you have updates on the request when the psp has completed the processing it will invoke the http request to update the payment status in this way the programming paradigm is changed and the payment service doesn t need to waste resources to poll the payment status anymore what if the psp never calls back we can set up a housekeeping job to check payment status every hour webhooks are often referred to as reverse apis or push apis because the server sends http requests to the client we need to pay attention to 3 things when using a webhook 1 we need to design a proper api for the external service to call 2 we need to set up proper rules in the api gateway for security reasons 3 we need to register the correct url at the external service how to improve api performance the diagram below shows 5 common tricks to improve api performance p img src images api performance jpg p pagination this is a common optimization when the size of the result is large the results are streaming back to the client to improve the service responsiveness asynchronous logging synchronous logging deals with the disk for every call and can slow down the system asynchronous logging sends logs to a lock free buffer first and immediately returns the logs will be flushed to the disk periodically this significantly reduces the i o overhead caching we can cache frequently accessed data into a cache the client can query the cache first instead of visiting the database directly if there is a cache miss the client can query from the database caches like redis store data in memory so the data access is much faster than the database payload compression the requests and responses can be compressed using gzip etc so that the transmitted data size is much smaller this speeds up the upload and download connection pool when accessing resources we often need to load data from the database opening the closing db connections adds significant overhead so we should connect to the db via a pool of open connections the connection pool is responsible for managing the connection lifecycle http 1 0 http 1 1 http 2 0 http 3 0 quic what problem does each generation of http solve the diagram below illustrates the key features p img src images http3 jpg p http 1 0 was finalized and fully documented in 1996 every request to the same server requires a separate tcp connection http 1 1 was published in 1997 a tcp connection can be left open for reuse persistent connection but it doesn t solve the hol head of line blocking issue hol blocking when the number of allowed parallel requests in the browser is used up subsequent requests need to wait for the former ones to complete http 2 0 was published in 2015 it addresses hol issue through request multiplexing which eliminates hol blocking at the application layer but hol still exists at the transport tcp layer as you can see in the diagram http 2 0 introduced the concept of http streams an abstraction that allows multiplexing different http exchanges onto the same tcp connection each stream doesn t need to be sent in order http 3 0 first draft was published in 2020 it is the proposed successor to http 2 0 it uses quic instead of tcp for the underlying transport protocol thus removing hol blocking in the transport layer quic is based on udp it introduces streams as first class citizens at the transport layer quic streams share the same quic connection so no additional handshakes and slow starts are required to create new ones but quic streams are delivered independently such that in most cases packet loss affecting one stream doesn t affect others soap vs rest vs graphql vs rpc the diagram below illustrates the api timeline and api styles comparison over time different api architectural styles are released each of them has its own patterns of standardizing data exchange you can check out the use cases of each style in the diagram p img src images soap vs rest vs graphql vs rpc jpeg p code first vs api first the diagram below shows the differences between code first development and api first development why do we want to consider api first design p img src images api first jpg style width 680px p microservices increase system complexity we have separate services to serve different functions of the system while this kind of architecture facilitates decoupling and segregation of duty we need to handle the various communications among services it is better to think through the system s complexity before writing the code and carefully defining the boundaries of the services separate functional teams need to speak the same language the dedicated functional teams are only responsible for their own components and services it is recommended that the organization speak the same language via api design we can mock requests and responses to validate the api design before writing code improve software quality and developer productivity since we have ironed out most of the uncertainties when the project starts the overall development process is smoother and the software quality is greatly improved developers are happy about the process as well because they can focus on functional development instead of negotiating sudden changes the possibility of having surprises toward the end of the project lifecycle is reduced because we have designed the api first the tests can be designed while the code is being developed in a way we also have tdd test driven design when using api first development http status codes p img src images http status code jpg style width 540px p the response codes for http are divided into five categories informational 100 199 success 200 299 redirection 300 399 client error 400 499 server error 500 599 what does api gateway do the diagram below shows the details p img src images api gateway jpg style width 520px p step 1 the client sends an http request to the api gateway step 2 the api gateway parses and validates the attributes in the http request step 3 the api gateway performs allow list deny list checks step 4 the api gateway talks to an identity provider for authentication and authorization step 5 the rate limiting rules are applied to the request if it is over the limit the request is rejected steps 6 and 7 now that the request has passed basic checks the api gateway finds the relevant service to route to by path matching step 8 the api gateway transforms the request into the appropriate protocol and sends it to backend microservices steps 9 12 the api gateway can handle errors properly and deals with faults if the error takes a longer time to recover circuit break it can also leverage elk elastic logstash kibana stack for logging and monitoring we sometimes cache data in the api gateway how do we design effective and safe apis the diagram below shows typical api designs with a shopping cart example p img src images safe apis jpg p note that api design is not just url path design most of the time we need to choose the proper resource names identifiers and path patterns it is equally important to design proper http header fields or to design effective rate limiting rules within the api gateway tcp ip encapsulation how is data sent over the network why do we need so many layers in the osi model p img src images osi model jpeg p the diagram below shows how data is encapsulated and de encapsulated when transmitting over the network step 1 when device a sends data to device b over the network via the http protocol it is first added an http header at the application layer step 2 then a tcp or a udp header is added to the data it is encapsulated into tcp segments at the transport layer the header contains the source port destination port and sequence number step 3 the segments are then encapsulated with an ip header at the network layer the ip header contains the source destination ip addresses step 4 the ip datagram is added a mac header at the data link layer with source destination mac addresses step 5 the encapsulated frames are sent to the physical layer and sent over the network in binary bits steps 6 10 when device b receives the bits from the network it performs the de encapsulation process which is a reverse processing of the encapsulation process the headers are removed layer by layer and eventually device b can read the data we need layers in the network model because each layer focuses on its own responsibilities each layer can rely on the headers for processing instructions and does not need to know the meaning of the data from the last layer why is nginx called a reverse proxy the diagram below shows the differences between a and a p img src images forward proxy v s reverse proxy2x jpg style width 720px p a forward proxy is a server that sits between user devices and the internet a forward proxy is commonly used for 1 protecting clients 2 circumventing browsing restrictions 3 blocking access to certain content a reverse proxy is a server that accepts a request from the client forwards the request to web servers and returns the results to the client as if the proxy server had processed the request a reverse proxy is good for 1 protecting servers 2 load balancing 3 caching static contents 4 encrypting and decrypting ssl communications what are the common load balancing algorithms the diagram below shows 6 common algorithms p img src images lb algorithms jpg p static algorithms 1 round robin the client requests are sent to different service instances in sequential order the services are usually required to be stateless 3 sticky round robin this is an improvement of the round robin algorithm if alice s first request goes to service a the following requests go to service a as well 4 weighted round robin the admin can specify the weight for each service the ones with a higher weight handle more requests than others 6 hash this algorithm applies a hash function on the incoming requests ip or url the requests are routed to relevant instances based on the hash function result dynamic algorithms 5 least connections a new request is sent to the service instance with the least concurrent connections 7 least response time a new request is sent to the service instance with the fastest response time url uri urn do you know the differences the diagram below shows a comparison of url uri and urn p img src images url uri urn jpg p uri uri stands for uniform resource identifier it identifies a logical or physical resource on the web url and urn are subtypes of uri url locates a resource while urn names a resource a uri is composed of the following parts scheme authority path query fragment url url stands for uniform resource locator the key concept of http it is the address of a unique resource on the web it can be used with other protocols like ftp and jdbc urn urn stands for uniform resource name it uses the urn scheme urns cannot be used to locate a resource a simple example given in the diagram is composed of a namespace and a namespace specific string if you would like to learn more detail on the subject i would recommend w3c s clarification https www w3 org tr uri clarification ci cd ci cd pipeline explained in simple terms p img src images ci cd pipeline jpg style width 680px p section 1 sdlc with ci cd the software development life cycle sdlc consists of several key stages development testing deployment and maintenance ci cd automates and integrates these stages to enable faster and more reliable releases when code is pushed to a git repository it triggers an automated build and test process end to end e2e test cases are run to validate the code if tests pass the code can be automatically deployed to staging production if issues are found the code is sent back to development for bug fixing this automation provides fast feedback to developers and reduces the risk of bugs in production section 2 difference between ci and cd continuous integration ci automates the build test and merge process it runs tests whenever code is committed to detect integration issues early this encourages frequent code commits and rapid feedback continuous delivery cd automates release processes like infrastructure changes and deployment it ensures software can be released reliably at any time through automated workflows cd may also automate the manual testing and approval steps required before production deployment section 3 ci cd pipeline a typical ci cd pipeline has several connected stages the developer commits code changes to the source control ci server detects changes and triggers the build code is compiled and tested unit integration tests test results reported to the developer on success artifacts are deployed to staging environments further testing may be done on staging before release cd system deploys approved changes to production netflix tech stack ci cd pipeline p img src images netflix ci cd jpg style width 720px p planning netflix engineering uses jira for planning and confluence for documentation coding java is the primary programming language for the backend service while other languages are used for different use cases build gradle is mainly used for building and gradle plugins are built to support various use cases packaging package and dependencies are packed into an amazon machine image ami for release testing testing emphasizes the production culture s focus on building chaos tools deployment netflix uses its self built spinnaker for canary rollout deployment monitoring the monitoring metrics are centralized in atlas and kayenta is used to detect anomalies incident report incidents are dispatched according to priority and pagerduty is used for incident handling architecture patterns mvc mvp mvvm mvvm c and viper these architecture patterns are among the most commonly used in app development whether on ios or android platforms developers have introduced them to overcome the limitations of earlier patterns so how do they differ p img src images client arch patterns png style width 720px p mvc the oldest pattern dates back almost 50 years every pattern has a view v responsible for displaying content and receiving user input most patterns include a model m to manage business data controller presenter and view model are translators that mediate between the view and the model entity in the viper pattern 18 key design patterns every developer should know patterns are reusable solutions to common design problems resulting in a smoother more efficient development process they serve as blueprints for building better software structures these are some of the most popular patterns p img src images 18 oo patterns png p abstract factory family creator makes groups of related items builder lego master builds objects step by step keeping creation and appearance separate prototype clone maker creates copies of fully prepared examples singleton one and only a special class with just one instance adapter universal plug connects things with different interfaces bridge function connector links how an object works to what it does composite tree builder forms tree like structures of simple and complex parts decorator customizer adds features to objects without changing their core facade one stop shop represents a whole system with a single simplified interface flyweight space saver shares small reusable items efficiently proxy stand in actor represents another object controlling access or actions chain of responsibility request relay passes a request through a chain of objects until handled command task wrapper turns a request into an object ready for action iterator collection explorer accesses elements in a collection one by one mediator communication hub simplifies interactions between different classes memento time capsule captures and restores an object s state observer news broadcaster notifies classes about changes in other objects visitor skillful guest adds new operations to a class without altering it database a nice cheat sheet of different databases in cloud services p img src images cloud dbs2 png p choosing the right database for your project is a complex task many database options each suited to distinct use cases can quickly lead to decision fatigue we hope this cheat sheet provides high level direction to pinpoint the right service that aligns with your project s needs and avoid potential pitfalls note google has limited documentation for their database use cases even though we did our best to look at what was available and arrived at the best option some of the entries may need to be more accurate 8 data structures that power your databases the answer will vary depending on your use case data can be indexed in memory or on disk similarly data formats vary such as numbers strings geographic coordinates etc the system might be write heavy or read heavy all of these factors affect your choice of database index format p img src images 8 ds db jpg p the following are some of the most popular data structures used for indexing data skiplist a common in memory index type used in redis hash index a very common implementation of the map data structure or collection sstable immutable on disk map implementation lsm tree skiplist sstable high write throughput b tree disk based solution consistent read write performance inverted index used for document indexing used in lucene suffix tree for string pattern search r tree multi dimension search such as finding the nearest neighbor how is an sql statement executed in the database the diagram below shows the process note that the architectures for different databases are different the diagram demonstrates some common designs p img src images sql execution order in db jpeg style width 580px p step 1 a sql statement is sent to the database via a transport layer protocol e g tcp step 2 the sql statement is sent to the command parser where it goes through syntactic and semantic analysis and a query tree is generated afterward step 3 the query tree is sent to the optimizer the optimizer creates an execution plan step 4 the execution plan is sent to the executor the executor retrieves data from the execution step 5 access methods provide the data fetching logic required for execution retrieving data from the storage engine step 6 access methods decide whether the sql statement is read only if the query is read only select statement it is passed to the buffer manager for further processing the buffer manager looks for the data in the cache or data files step 7 if the statement is an update or insert it is passed to the transaction manager for further processing step 8 during a transaction the data is in lock mode this is guaranteed by the lock manager it also ensures the transaction s acid properties cap theorem the cap theorem is one of the most famous terms in computer science but i bet different developers have different understandings let s examine what it is and why it can be confusing p img src images cap theorem jpeg p cap theorem states that a distributed system can t provide more than two of these three guarantees simultaneously consistency consistency means all clients see the same data at the same time no matter which node they connect to availability availability means any client that requests data gets a response even if some of the nodes are down partition tolerance a partition indicates a communication break between two nodes partition tolerance means the system continues to operate despite network partitions the 2 of 3 formulation can be useful but this simplification could be misleading 1 picking a database is not easy justifying our choice purely based on the cap theorem is not enough for example companies don t choose cassandra for chat applications simply because it is an ap system there is a list of good characteristics that make cassandra a desirable option for storing chat messages we need to dig deeper 2 cap prohibits only a tiny part of the design space perfect availability and consistency in the presence of partitions which are rare quoted from the paper cap twelve years later how the rules have changed 3 the theorem is about 100 availability and consistency a more realistic discussion would be the trade offs between latency and consistency when there is no network partition see pacelc theorem for more details is the cap theorem actually useful i think it is still useful as it opens our minds to a set of tradeoff discussions but it is only part of the story we need to dig deeper when picking the right database types of memory and storage p img src images types of memory and storage jpeg style width 420px p visualizing a sql query p img src images sql execution order jpg style width 580px p sql statements are executed by the database system in several steps including parsing the sql statement and checking its validity transforming the sql into an internal representation such as relational algebra optimizing the internal representation and creating an execution plan that utilizes index information executing the plan and returning the results the execution of sql is highly complex and involves many considerations such as the use of indexes and caches the order of table joins concurrency control transaction management sql language in 1986 sql structured query language became a standard over the next 40 years it became the dominant language for relational database management systems reading the latest standard ansi sql 2016 can be time consuming how can i learn it p img src images how to learn sql jpg p there are 5 components of the sql language ddl data definition language such as create alter drop dql data query language such as select dml data manipulation language such as insert update delete dcl data control language such as grant revoke tcl transaction control language such as commit rollback for a backend engineer you may need to know most of it as a data analyst you may need to have a good understanding of dql select the topics that are most relevant to you cache data is cached everywhere this diagram illustrates where we cache data in a typical architecture p img src images where do we cache data jpeg style width 720px p there are multiple layers along the flow 1 client apps http responses can be cached by the browser we request data over http for the first time and it is returned with an expiry policy in the http header we request data again and the client app tries to retrieve the data from the browser cache first 2 cdn cdn caches static web resources the clients can retrieve data from a cdn node nearby 3 load balancer the load balancer can cache resources as well 4 messaging infra message brokers store messages on disk first and then consumers retrieve them at their own pace depending on the retention policy the data is cached in kafka clusters for a period of time 5 services there are multiple layers of cache in a service if the data is not cached in the cpu cache the service will try to retrieve the data from memory sometimes the service has a second level cache to store data on disk 6 distributed cache distributed cache like redis holds key value pairs for multiple services in memory it provides much better read write performance than the database 7 full text search we sometimes need to use full text searches like elastic search for document search or log search a copy of data is indexed in the search engine as well 8 database even in the database we have different levels of caches wal write ahead log data is written to wal first before building the b tree index bufferpool a memory area allocated to cache query results materialized view pre compute query results and store them in the database tables for better query performance transaction log record all the transactions and database updates replication log used to record the replication state in a database cluster why is redis so fast there are 3 main reasons as shown in the diagram below p img src images why redis fast jpeg p 1 redis is a ram based data store ram access is at least 1000 times faster than random disk access 2 redis leverages io multiplexing and single threaded execution loop for execution efficiency 3 redis leverages several efficient lower level data structures question another popular in memory store is memcached do you know the differences between redis and memcached you might have noticed the style of this diagram is different from my previous posts please let me know which one you prefer how can redis be used p img src images top redis use cases jpg style width 520px p there is more to redis than just caching redis can be used in a variety of scenarios as shown in the diagram session we can use redis to share user session data among different services cache we can use redis to cache objects or pages especially for hotspot data distributed lock we can use a redis string to acquire locks among distributed services counter we can count how many likes or how many reads for articles rate limiter we can apply a rate limiter for certain user ips global id generator we can use redis int for global id shopping cart we can use redis hash to represent key value pairs in a shopping cart calculate user retention we can use bitmap to represent the user login daily and calculate user retention message queue we can use list for a message queue ranking we can use zset to sort the articles top caching strategies designing large scale systems usually requires careful consideration of caching below are five caching strategies that are frequently utilized p img src images top caching strategy jpeg style width 680px p microservice architecture what does a typical microservice architecture look like p img src images typical microservice arch jpg style width 520px p the diagram below shows a typical microservice architecture load balancer this distributes incoming traffic across multiple backend services cdn content delivery network cdn is a group of geographically distributed servers that hold static content for faster delivery the clients look for content in cdn first then progress to backend services api gateway this handles incoming requests and routes them to the relevant services it talks to the identity provider and service discovery identity provider this handles authentication and authorization for users service registry discovery microservice registration and discovery happen in this component and the api gateway looks for relevant services in this component to talk to management this component is responsible for monitoring the services microservices microservices are designed and deployed in different domains each domain has its own database the api gateway talks to the microservices via rest api or other protocols and the microservices within the same domain talk to each other using rpc remote procedure call benefits of microservices they can be quickly designed deployed and horizontally scaled each domain can be independently maintained by a dedicated team business requirements can be customized in each domain and better supported as a result microservice best practices a picture is worth a thousand words 9 best practices for developing microservices p img src images microservice best practices jpeg p when we develop microservices we need to follow the following best practices 1 use separate data storage for each microservice 2 keep code at a similar level of maturity 3 separate build for each microservice 4 assign each microservice with a single responsibility 5 deploy into containers 6 design stateless services 7 adopt domain driven design 8 design micro frontend 9 orchestrating microservices what tech stack is commonly used for microservices below you will find a diagram showing the microservice tech stack both for the development phase and for production p img src images microservice tech jpeg p define api this establishes a contract between frontend and backend we can use postman or openapi for this development node js or react is popular for frontend development and java python go for backend development also we need to change the configurations in the api gateway according to api definitions continuous integration junit and jenkins for automated testing the code is packaged into a docker image and deployed as microservices nginx is a common choice for load balancers cloudflare provides cdn content delivery network api gateway we can use spring boot for the gateway and use eureka zookeeper for service discovery the microservices are deployed on clouds we have options among aws microsoft azure or google gcp cache and full text search redis is a common choice for caching key value pairs elasticsearch is used for full text search communications for services to talk to each other we can use messaging infra kafka or rpc persistence we can use mysql or postgresql for a relational database and amazon s3 for object store we can also use cassandra for the wide column store if necessary management monitoring to manage so many microservices the common ops tools include prometheus elastic stack and kubernetes why is kafka fast there are many design decisions that contributed to kafka s performance in this post we ll focus on two we think these two carried the most weight p img src images why is kafka fast jpeg p 1 the first one is kafka s reliance on sequential i o 2 the second design choice that gives kafka its performance advantage is its focus on efficiency zero copy principle the diagram illustrates how the data is transmitted between producer and consumer and what zero copy means step 1 1 1 3 producer writes data to the disk step 2 consumer reads data without zero copy 2 1 the data is loaded from disk to os cache 2 2 the data is copied from os cache to kafka application 2 3 kafka application copies the data into the socket buffer 2 4 the data is copied from socket buffer to network card 2 5 the network card sends data out to the consumer step 3 consumer reads data with zero copy 3 1 the data is loaded from disk to os cache 3 2 os cache directly copies the data to the network card via sendfile command 3 3 the network card sends data out to the consumer zero copy is a shortcut to save the multiple data copies between application context and kernel context payment systems how to learn payment systems p img src images learn payments jpg p why is the credit card called the most profitable product in banks how does visa mastercard make money the diagram below shows the economics of the credit card payment flow p img src images how does visa makes money jpg style width 640px p 1 nbsp nbsp the cardholder pays a merchant 100 to buy a product 2 nbsp the merchant benefits from the use of the credit card with higher sales volume and needs to compensate the issuer and the card network for providing the payment service the acquiring bank sets a fee with the merchant called the merchant discount fee 3 4 the acquiring bank keeps 0 25 as the acquiring markup and 1 75 is paid to the issuing bank as the interchange fee the merchant discount fee should cover the interchange fee the interchange fee is set by the card network because it is less efficient for each issuing bank to negotiate fees with each merchant 5 nbsp nbsp the card network sets up the network assessments and fees with each bank which pays the card network for its services every month for example visa charges a 0 11 assessment plus a 0 0195 usage fee for every swipe 6 nbsp nbsp the cardholder pays the issuing bank for its services why should the issuing bank be compensated the issuer pays the merchant even if the cardholder fails to pay the issuer the issuer pays the merchant before the cardholder pays the issuer the issuer has other operating costs including managing customer accounts providing statements fraud detection risk management clearing settlement etc how does visa work when we swipe a credit card at a merchant s shop p img src images visa payment jpeg p visa mastercard and american express act as card networks for the clearing and settling of funds the card acquiring bank and the card issuing bank can be and often are different if banks were to settle transactions one by one without an intermediary each bank would have to settle the transactions with all the other banks this is quite inefficient the diagram below shows visa s role in the credit card payment process there are two flows involved authorization flow happens when the customer swipes the credit card capture and settlement flow happens when the merchant wants to get the money at the end of the day authorization flow step 0 the card issuing bank issues credit cards to its customers step 1 the cardholder wants to buy a product and swipes the credit card at the point of sale pos terminal in the merchant s shop step 2 the pos terminal sends the transaction to the acquiring bank which has provided the pos terminal steps 3 and 4 the acquiring bank sends the transaction to the card network also called the card scheme the card network sends the transaction to the issuing bank for approval steps 4 1 4 2 and 4 3 the issuing bank freezes the money if the transaction is approved the approval or rejection is sent back to the acquirer as well as the pos terminal capture and settlement flow steps 1 and 2 the merchant wants to collect the money at the end of the day so they hit capture on the pos terminal the transactions are sent to the acquirer in batch the acquirer sends the batch file with transactions to the card network step 3 the card network performs clearing for the transactions collected from different acquirers and sends the clearing files to different issuing banks step 4 the issuing banks confirm the correctness of the clearing files and transfer money to the relevant acquiring banks step 5 the acquiring bank then transfers money to the merchant s bank step 4 the card network clears the transactions from different acquiring banks clearing is a process in which mutual offset transactions are netted so the number of total transactions is reduced in the process the card network takes on the burden of talking to each bank and receives service fees in return payment systems around the world series part 1 unified payments interface upi in india what s upi upi is an instant real time payment system developed by the national payments corporation of india it accounts for 60 of digital retail transactions in india today upi payment markup language standard for interoperable payments p img src images how does upi work png style width 600px p devops devops vs sre vs platform engineering what is the difference the concepts of devops sre and platform engineering have emerged at different times and have been developed by various individuals and organizations p img src images devops sre platform jpg p devops as a concept was introduced in 2009 by patrick debois and andrew shafer at the agile conference they sought to bridge the gap between software development and operations by promoting a collaborative culture and shared responsibility for the entire software development lifecycle sre or site reliability engineering was pioneered by google in the early 2000s to address operational challenges in managing large scale complex systems google developed sre practices and tools such as the borg cluster management system and the monarch monitoring system to improve the reliability and efficiency of their services platform engineering is a more recent concept building on the foundation of sre engineering the precise origins of platform engineering are less clear but it is generally understood to be an extension of the devops and sre practices with a focus on delivering a comprehensive platform for product development that supports the entire business perspective it s worth noting that while these concepts emerged at different times they are all related to the broader trend of improving collaboration automation and efficiency in software development and operations what is k8s kubernetes k8s is a container orchestration system it is used for container deployment and management its design is greatly impacted by google s internal system borg p img src images k8s jpeg style width 680px p a k8s cluster consists of a set of worker machines called nodes that run containerized applications every cluster has at least one worker node the worker node s host the pods that are the components of the application workload the control plane manages the worker nodes and the pods in the cluster in production environments the control plane usually runs across multiple computers and a cluster usually runs multiple nodes providing fault tolerance and high availability control plane components 1 api server the api server talks to all the components in the k8s cluster all the operations on pods are executed by talking to the api server 2 scheduler the scheduler watches pod workloads and assigns loads on newly created pods 3 controller manager the controller manager runs the controllers including node controller job controller endpointslice controller and serviceaccount controller 4 etcd etcd is a key value store used as kubernetes backing store for all cluster data nodes 1 pods a pod is a group of containers and is the smallest unit that k8s administers pods have a single ip address applied to every container within the pod 2 kubelet an agent that runs on each node in the cluster it ensures containers are running in a pod 3 kube proxy kube proxy is a network proxy that runs on each node in your cluster it routes traffic coming into a node from the service it forwards requests for work to the correct containers docker vs kubernetes which one should we use p img src images docker vs k8s jpg style width 680px p what is docker docker is an open source platform that allows you to package distribute and run applications in isolated containers it focuses on containerization providing lightweight environments that encapsulate applications and their dependencies what is kubernetes kubernetes often referred to as k8s is an open source container orchestration platform it provides a framework for automating the deployment scaling and management of containerized applications across a cluster of nodes how are both different from each other docker docker operates at the individual container level on a single operating system host you must manually manage each host and setting up networks security policies and storage for multiple related containers can be complex kubernetes kubernetes operates at the cluster level it manages multiple containerized applications across multiple hosts providing automation for tasks like load balancing scaling and ensuring the desired state of applications in short docker focuses on containerization and running containers on individual hosts while kubernetes specializes in managing and orchestrating containers at scale across a cluster of hosts how does docker work the diagram below shows the architecture of docker and how it works when we run docker build docker pull and docker run p img src images docker jpg style width 680px p there are 3 components in docker architecture docker client the docker client talks to the docker daemon docker host the docker daemon listens for docker api requests and manages docker objects such as images containers networks and volumes docker registry a docker registry stores docker images docker hub is a public registry that anyone can use let s take the docker run command as an example 1 docker pulls the image from the registry 1 docker creates a new container 1 docker allocates a read write filesystem to the container 1 docker creates a network interface to connect the container to the default network 1 docker starts the container git how git commands work to begin with it s essential to identify where our code is stored the common assumption is that there are only two locations one on a remote server like github and the other on our local machine however this isn t entirely accurate git maintains three local storages on our machine which means that our code can be found in four places p img src images git commands png style width 600px p working directory where we edit files staging area a temporary location where files are kept for the next commit local repository contains the code that has been committed remote repository the remote server that stores the code most git commands primarily move files between these four locations how does git work the diagram below shows the git workflow p img src images git workflow jpeg style width 520px p git is a distributed version control system every developer maintains a local copy of the main repository and edits and commits to the local copy the commit is very fast because the operation doesn t interact with the remote repository if the remote repository crashes the files can be recovered from the local repositories git merge vs git rebase what are the differences p img src images git merge git rebase jpeg style width 680px p when we merge changes from one git branch to another we can use git merge or git rebase the diagram below shows how the two commands work git merge this creates a new commit g in the main branch g ties the histories of both main and feature branches git merge is non destructive neither the main nor the feature branch is changed git rebase git rebase moves the feature branch histories to the head of the main branch it creates new commits e f and g for each commit in the feature branch the benefit of rebase is that it has a linear commit history rebase can be dangerous if the golden rule of git rebase is not followed the golden rule of git rebase never use it on public branches cloud services a nice cheat sheet of different cloud services 2023 edition p img src images cloud compare jpg p what is cloud native below is a diagram showing the evolution of architecture and processes since the 1980s p img src images cloud native jpeg style width 640px p organizations can build and run scalable applications on public private and hybrid clouds using cloud native technologies this means the applications are designed to leverage cloud features so they are resilient to load and easy to scale cloud native includes 4 aspects 1 development process this has progressed from waterfall to agile to devops 2 application architecture the architecture has gone from monolithic to microservices each service is designed to be small adaptive to the limited resources in cloud containers 3 deployment packaging the applications used to be deployed on physical servers then around 2000 the applications that were not sensitive to latency were usually deployed on virtual servers with cloud native applications they are packaged into docker images and deployed in containers 4 application infrastructure the applications are massively deployed on cloud infrastructure instead of self hosted servers developer productivity tools visualize json files nested json files are hard to read jsoncrack generates graph diagrams from json files and makes them easy to read additionally the generated diagrams can be downloaded as images p img src images json cracker jpeg p automatically turn code into architecture diagrams p img src images diagrams as code jpeg style width 640px p what does it do draw the cloud system architecture in python code diagrams can also be rendered directly inside the jupyter notebooks no design tools are needed supports the following providers aws azure gcp kubernetes alibaba cloud oracle cloud etc github repo https github com mingrammer diagrams linux linux file system explained p img src images linux file systems jpg style width 680px p the linux file system used to resemble an unorganized town where individuals constructed their houses wherever they pleased however in 1994 the filesystem hierarchy standard fhs was introduced to bring order to the linux file system by implementing a standard like the fhs software can ensure a consistent layout across various linux distributions nonetheless not all linux distributions strictly adhere to this standard they often incorporate their own unique elements or cater to specific requirements to become proficient in this standard you can begin by exploring utilize commands such as cd for navigation and ls for listing directory contents imagine the file system as a tree starting from the root with time it will become second nature to you transforming you into a skilled linux administrator 18 most used linux commands you should know linux commands are instructions for interacting with the operating system they help manage files directories system processes and many other aspects of the system you need to become familiar with these commands in order to navigate and maintain linux based systems efficiently and effectively this diagram below shows popular linux commands p img src images 18 most used linux commands you should know 01 jpeg style width 680px p ls list files and directories cd change the current directory mkdir create a new directory rm remove files or directories cp copy files or directories mv move or rename files or directories chmod change file or directory permissions grep search for a pattern in files find search for files and directories tar manipulate tarball archive files vi edit files using text editors cat display the content of files top display processes and resource usage ps display processes information kill terminate a process by sending a signal du estimate file space usage ifconfig configure network interfaces ping test network connectivity between hosts security how does https work hypertext transfer protocol secure https is an extension of the hypertext transfer protocol http https transmits encrypted data using transport layer security tls if the data is hijacked online all the hijacker gets is binary code p img src images https jpg p how is the data encrypted and decrypted step 1 the client browser and the server establish a tcp connection step 2 the client sends a client hello to the server the message contains a set of necessary encryption algorithms cipher suites and the latest tls version it can support the server responds with a server hello so the browser knows whether it can support the algorithms and tls version the server then sends the ssl certificate to the client the certificate contains the public key host name expiry dates etc the client validates the certificate step 3 after validating the ssl certificate the client generates a session key and encrypts it using the public key the server receives the encrypted session key and decrypts it with the private key step 4 now that both the client and the server hold the same session key symmetric encryption the encrypted data is transmitted in a secure bi directional channel why does https switch to symmetric encryption during data transmission there are two main reasons 1 security the asymmetric encryption goes only one way this means that if the server tries to send the encrypted data back to the client anyone can decrypt the data using the public key 2 server resources the asymmetric encryption adds quite a lot of mathematical overhead it is not suitable for data transmissions in long sessions oauth 2 0 explained with simple terms oauth 2 0 is a powerful and secure framework that allows different applications to securely interact with each other on behalf of users without sharing sensitive credentials p img src images oauth2 jpg p the entities involved in oauth are the user the server and the identity provider idp what can an oauth token do when you use oauth you get an oauth token that represents your identity and permissions this token can do a few important things single sign on sso with an oauth token you can log into multiple services or apps using just one login making life easier and safer authorization across systems the oauth token allows you to share your authorization or access rights across various systems so you don t have to log in separately everywhere accessing user profile apps with an oauth token can access certain parts of your user profile that you allow but they won t see everything remember oauth 2 0 is all about keeping you and your data safe while making your online experiences seamless and hassle free across different applications and services top 4 forms of authentication mechanisms p img src images top4 most used auth jpg p 1 ssh keys cryptographic keys are used to access remote systems and servers securely 1 oauth tokens tokens that provide limited access to user data on third party applications 1 ssl certificates digital certificates ensure secure and encrypted communication between servers and clients 1 credentials user authentication information is used to verify and grant access to various systems and services session cookie jwt token sso and oauth 2 0 what are they these terms are all related to user identity management when you log into a website you declare who you are identification your identity is verified authentication and you are granted the necessary permissions authorization many solutions have been proposed in the past and the list keeps growing p img src images session jpeg p from simple to complex here is my understanding of user identity management www authenticate is the most basic method you are asked for the username and password by the browser as a result of the inability to control the login life cycle it is seldom used today a finer control over the login life cycle is session cookie the server maintains session storage and the browser keeps the id of the session a cookie usually only works with browsers and is not mobile app friendly to address the compatibility issue the token can be used the client sends the token to the server and the server validates the token the downside is that the token needs to be encrypted and decrypted which may be time consuming jwt is a standard way of representing tokens this information can be verified and trusted because it is digitally signed since jwt contains the signature there is no need to save session information on the server side by using sso single sign on you can sign on only once and log in to multiple websites it uses cas central authentication service to maintain cross site information by using oauth 2 0 you can authorize one website to access your information on another website how to store passwords safely in the database and how to validate a password p img src images salt jpg style width 720px p things not to do storing passwords in plain text is not a good idea because anyone with internal access can see them storing password hashes directly is not sufficient because it is pruned to precomputation attacks such as rainbow tables to mitigate precomputation attacks we salt the passwords what is salt according to owasp guidelines a salt is a unique randomly generated string that is added to each password as part of the hashing process how to store a password and salt 1 the hash result is unique to each password 1 the password can be stored in the database using the following format hash password salt how to validate a password to validate a password it can go through the following process 1 a client enters the password 1 the system fetches the corresponding salt from the database 1 the system appends the salt to the password and hashes it let s call the hashed value h1 1 the system compares h1 and h2 where h2 is the hash stored in the database if they are the same the password is valid explaining json web token jwt to a 10 year old kid p img src images jwt jpg p imagine you have a special box called a jwt inside this box there are three parts a header a payload and a signature the header is like the label on the outside of the box it tells us what type of box it is and how it s secured it s usually written in a format called json which is just a way to organize information using curly braces and colons the payload is like the actual message or information you want to send it could be your name age or any other data you want to share it s also written in json format so it s easy to understand and work with now the signature is what makes the jwt secure it s like a special seal that only the sender knows how to create the signature is created using a secret code kind of like a password this signature ensures that nobody can tamper with the contents of the jwt without the sender knowing about it when you want to send the jwt to a server you put the header payload and signature inside the box then you send it over to the server the server can easily read the header and payload to understand who you are and what you want to do how does google authenticator or other types of 2 factor authenticators work google authenticator is commonly used for logging into our accounts when 2 factor authentication is enabled how does it guarantee security google authenticator is a software based authenticator that implements a two step verification service the diagram below provides detail p img src images google authenticate jpeg p there are two stages involved stage 1 the user enables google two step verification stage 2 the user uses the authenticator for logging in etc let s look at these stages stage 1 steps 1 and 2 bob opens the web page to enable two step verification the front end requests a secret key the authentication service generates the secret key for bob and stores it in the database step 3 the authentication service returns a uri to the front end the uri is composed of a key issuer username and secret key the uri is displayed in the form of a qr code on the web page step 4 bob then uses google authenticator to scan the generated qr code the secret key is stored in the authenticator stage 2 steps 1 and 2 bob wants to log into a website with google two step verification for this he needs the password every 30 seconds google authenticator generates a 6 digit password using totp time based one time password algorithm bob uses the password to enter the website steps 3 and 4 the frontend sends the password bob enters to the backend for authentication the authentication service reads the secret key from the database and generates a 6 digit password using the same totp algorithm as the client step 5 the authentication service compares the two passwords generated by the client and the server and returns the comparison result to the frontend bob can proceed with the login process only if the two passwords match is this authentication mechanism safe can the secret key be obtained by others we need to make sure the secret key is transmitted using https the authenticator client and the database store the secret key and we need to make sure the secret keys are encrypted can the 6 digit password be guessed by hackers no the password has 6 digits so the generated password has 1 million potential combinations plus the password changes every 30 seconds if hackers want to guess the password in 30 seconds they need to enter 30 000 combinations per second real world case studies netflix s tech stack this post is based on research from many netflix engineering blogs and open source projects if you come across any inaccuracies please feel free to inform us p img src images netflix tech stack png style width 680px p mobile and web netflix has adopted swift and kotlin to build native mobile apps for its web application it uses react frontend server communication netflix uses graphql backend services netflix relies on zuul eureka the spring boot framework and other technologies databases netflix utilizes ev cache cassandra cockroachdb and other databases messaging streaming netflix employs apache kafka and fink for messaging and streaming purposes video storage netflix uses s3 and open connect for video storage data processing netflix utilizes flink and spark for data processing which is then visualized using tableau redshift is used for processing structured data warehouse information ci cd netflix employs various tools such as jira confluence pagerduty jenkins gradle chaos monkey spinnaker atlas and more for ci cd processes twitter architecture 2022 yes this is the real twitter architecture it is posted by elon musk and redrawn by us for better readability p img src images twitter arch jpeg p evolution of airbnb s microservice architecture over the past 15 years airbnb s microservice architecture went through 3 main stages p img src images airbnb arch jpeg p monolith 2008 2017 airbnb began as a simple marketplace for hosts and guests this is built in a ruby on rails application the monolith what s the challenge confusing team ownership unowned code slow deployment microservices 2017 2020 microservice aims to solve those challenges in the microservice architecture key services include data fetching service business logic data service write workflow service ui aggregation service each service had one owning team what s the challenge hundreds of services and dependencies were difficult for humans to manage micro macroservices 2020 present this is what airbnb is working on now the micro and macroservice hybrid model focuses on the unification of apis monorepo vs microrepo which is the best why do different companies choose different options p img src images monorepo microrepo jpg p monorepo isn t new linux and windows were both created using monorepo to improve scalability and build speed google developed its internal dedicated toolchain to scale it faster and strict coding quality standards to keep it consistent amazon and netflix are major ambassadors of the microservice philosophy this approach naturally separates the service code into separate repositories it scales faster but can lead to governance pain points later on within monorepo each service is a folder and every folder has a build config and owners permission control every service member is responsible for their own folder on the other hand in microrepo each service is responsible for its repository with the build config and permissions typically set for the entire repository in monorepo dependencies are shared across the entire codebase regardless of your business so when there s a version upgrade every codebase upgrades their version in microrepo dependencies are controlled within each repository businesses choose when to upgrade their versions based on their own schedules monorepo has a standard for check ins google s code review process is famously known for setting a high bar ensuring a coherent quality standard for monorepo regardless of the business microrepo can either set its own standard or adopt a shared standard by incorporating best practices it can scale faster for business but the code quality might be a bit different google engineers built bazel and meta built buck there are other open source tools available including nix lerna and others over the years microrepo has had more supported tools including maven and gradle for java npm for nodejs and cmake for c c among others how will you design the stack overflow website if your answer is on premise servers and monolith on the bottom of the following image you would likely fail the interview but that s how it is built in reality p img src images stackoverflow jpg p what people think it should look like the interviewer is probably expecting something like the top portion of the picture microservice is used to decompose the system into small components each service has its own database use cache heavily the service is sharded the services talk to each other asynchronously through message queues the service is implemented using event sourcing with cqrs showing off knowledge in distributed systems such as eventual consistency cap theorem etc what it actually is stack overflow serves all the traffic with only 9 on premise web servers and it s on monolith it has its own servers and does not run on the cloud this is contrary to all our popular beliefs these days why did amazon prime video monitoring move from serverless to monolithic how can it save 90 cost the diagram below shows the architecture comparison before and after the migration p img src images serverless to monolithic jpeg p what is amazon prime video monitoring service prime video service needs to monitor the quality of thousands of live streams the monitoring tool automatically analyzes the streams in real time and identifies quality issues like block corruption video freeze and sync problems this is an important process for customer satisfaction there are 3 steps media converter defect detector and real time notification what is the problem with the old architecture the old architecture was based on amazon lambda which was good for building services quickly however it was not cost effective when running the architecture at a high scale the two most expensive operations are 1 the orchestration workflow aws step functions charge users by state transitions and the orchestration performs multiple state transitions every second 2 data passing between distributed components the intermediate data is stored in amazon s3 so that the next stage can download the download can be costly when the volume is high monolithic architecture saves 90 cost a monolithic architecture is designed to address the cost issues there are still 3 components but the media converter and defect detector are deployed in the same process saving the cost of passing data over the network surprisingly this approach to deployment architecture change led to 90 cost savings this is an interesting and unique case study because microservices have become a go to and fashionable choice in the tech industry it s good to see that we are having more discussions about evolving the architecture and having more honest discussions about its pros and cons decomposing components into distributed microservices comes with a cost what did amazon leaders say about this amazon cto werner vogels building evolvable software systems is a strategy not a religion and revisiting your architecture with an open mind is a must ex amazon vp sustainability adrian cockcroft the prime video team had followed a path i call serverless first i don t advocate serverless only how does disney hotstar capture 5 billion emojis during a tournament p img src images hotstar emojis jpeg style width 720px p 1 clients send emojis through standard http requests you can think of golang service as a typical web server golang is chosen because it supports concurrency well threads in golang are lightweight 2 since the write volume is very high kafka message queue is used as a buffer 3 emoji data are aggregated by a streaming processing service called spark it aggregates data every 2 seconds which is configurable there is a trade off to be made based on the interval a shorter interval means emojis are delivered to other clients faster but it also means more computing resources are needed 4 aggregated data is written to another kafka 5 the pubsub consumers pull aggregated emoji data from kafka 6 emojis are delivered to other clients in real time through the pubsub infrastructure the pubsub infrastructure is interesting hotstar considered the following protocols socketio nats mqtt and grpc and settled with mqtt a similar design is adopted by linkedin which streams a million likes sec how discord stores trillions of messages the diagram below shows the evolution of message storage at discord p img src images discord store messages jpg p mongodb cassandra scylladb in 2015 the first version of discord was built on top of a single mongodb replica around nov 2015 mongodb stored 100 million messages and the ram couldn t hold the data and index any longer the latency became unpredictable message storage needs to be moved to another database cassandra was chosen in 2017 discord had 12 cassandra nodes and stored billions of messages at the beginning of 2022 it had 177 nodes with trillions of messages at this point latency was unpredictable and maintenance operations became too expensive to run there are several reasons for the issue cassandra uses the lsm tree for the internal data structure the reads are more expensive than the writes there can be many concurrent reads on a server with hundreds of users resulting in hotspots maintaining clusters such as compacting sstables impacts performance garbage collection pauses would cause significant latency spikes scylladb is cassandra compatible database written in c discord redesigned its architecture to have a monolithic api a data service written in rust and scylladb based storage the p99 read latency in scylladb is 15ms compared to 40 125ms in cassandra the p99 write latency is 5ms compared to 5 70ms in cassandra how do video live streamings work on youtube tiktok live or twitch live streaming differs from regular streaming because the video content is sent via the internet in real time usually with a latency of just a few seconds the diagram below explains what happens behind the scenes to make this possible p img src images live streaming updated jpg style width 640px p step 1 the raw video data is captured by a microphone and camera the data is sent to the server side step 2 the video data is compressed and encoded for example the compressing algorithm separates the background and other video elements after compression the video is encoded to standards such as h 264 the size of the video data is much smaller after this step step 3 the encoded data is divided into smaller segments usually seconds in length so it takes much less time to download or stream step 4 the segmented data is sent to the streaming server the streaming server needs to support different devices and network conditions this is called adaptive bitrate streaming this means we need to produce multiple files at different bitrates in steps 2 and 3 step 5 the live streaming data is pushed to edge servers supported by cdn content delivery network millions of viewers can watch the video from an edge server nearby cdn significantly lowers data transmission latency step 6 the viewers devices decode and decompress the video data and play the video in a video player steps 7 and 8 if the video needs to be stored for replay the encoded data is sent to a storage server and viewers can request a replay from it later standard protocols for live streaming include rtmp real time messaging protocol this was originally developed by macromedia to transmit data between a flash player and a server now it is used for streaming video data over the internet note that video conferencing applications like skype use rtc real time communication protocol for lower latency hls http live streaming it requires the h 264 or h 265 encoding apple devices accept only hls format dash dynamic adaptive streaming over http dash does not support apple devices both hls and dash support adaptive bitrate streaming license p xmlns cc http creativecommons org ns this work is licensed under a href http creativecommons org licenses by nc nd 4 0 ref chooser v1 target blank rel license noopener noreferrer style display inline block cc by nc nd 4 0 img style height 22px important margin left 3px vertical align text bottom src https mirrors creativecommons org presskit icons cc svg ref chooser v1 img style height 22px important margin left 3px vertical align text bottom src https mirrors creativecommons org presskit icons by svg ref chooser v1 img style height 22px important margin left 3px vertical align text bottom src https mirrors creativecommons org presskit icons nc svg ref chooser v1 img style height 22px important margin left 3px vertical align text bottom src https mirrors creativecommons org presskit icons nd svg ref chooser v1 a p | aws cloud-computing coding-interviews computer-science interview-questions software-architecture software-development software-engineering system-design system-design-interview | os |
spago | p align center br img src https github com nlpodyssey spago blob main assets spago logo png width 400 br p p align center a href https github com nlpodyssey spago actions workflows go yml query branch 3amain img alt build src https github com nlpodyssey spago actions workflows go yml badge svg branch main a a href https codecov io gh nlpodyssey spago img alt coverage src https codecov io gh nlpodyssey spago branch main badge svg a a href https goreportcard com report github com nlpodyssey spago img alt go report card src https goreportcard com badge github com nlpodyssey spago a a href https codeclimate com github nlpodyssey spago maintainability img alt maintainability src https api codeclimate com v1 badges be7350d3eb1a6a8aa503 maintainability a a href https pkg go dev github com nlpodyssey spago img alt documentation src https pkg go dev badge github com nlpodyssey spago svg a a href https opensource org licenses bsd 2 clause img alt license src https img shields io badge license bsd 202 clause orange svg a a href http makeapullrequest com img alt prs welcome src https img shields io badge prs welcome brightgreen svg style flat square a a href https github com avelino awesome go img alt awesome go src https awesome re mentioned badge svg a p p align center br i if you like the project please star this repository to show your support i br br p if you re interested in nlp related functionalities be sure to explore the cybertron https github com nlpodyssey cybertron package spago is a machine learning library written in pure go designed to support relevant neural architectures in natural language processing spago is self contained in that it uses its own lightweight computational graph both for training and inference easy to understand from start to finish it provides automatic differentiation via dynamic define by run execution feed forward layers linear highway convolution recurrent layers lstm gru bilstm attention layers self attention multi head attention gradient descent optimizers adam radam rms prop adagrad sgd gob compatible neural models for serialization usage requirements go 1 19 https golang org dl clone this repo or get the library console go get u github com nlpodyssey spago getting started a good place to start is by looking at the implementation of built in neural models such as the lstm example 1 here is an example of how to calculate the sum of two variables go package main import fmt github com nlpodyssey spago ag github com nlpodyssey spago mat type t float32 func main create a new node of type variable with a scalar a mat scalar t 2 0 mat withgrad true create another node of type variable with a scalar b mat scalar t 5 0 mat withgrad true create an addition operator the calculation is actually performed here c ag add a b print the result fmt printf c v float d n c value c value scalar bitsize c accgrad mat scalar t 0 5 ag backward c fmt printf ga v n a grad fmt printf gb v n b grad output console c 7 float32 ga 0 5 gb 0 5 example 2 here is a simple implementation of the perceptron formula go package main import github com nlpodyssey spago ag github com nlpodyssey spago mat func main x mat scalar 0 8 w mat scalar 0 4 b mat scalar 0 2 y sigmoid add mul w x b y contributing if you think something is missing or could be improved please open issues and pull requests to start contributing check the contributing guidelines https github com nlpodyssey spago blob main contributing md contact we highly encourage you to create an issue as it will contribute to the growth of the community however if you prefer to communicate with us privately please feel free to email matteo grella mailto matteogrella gmail com with any questions or comments you may have acknowledgments spago is part of the open source nlp odyssey https github com nlpodyssey initiative initiated by members of the exop team now part of crisis24 sponsors see our open collective https opencollective com nlpodyssey contribute page if you too are interested in becoming a sponsor | deep-learning machine-learning natural-language-processing neural-network computation-graph automatic-differentiation artificial-intelligence deeplearning transformer-architecture recurrent-networks lstm language-model question-answering bert bert-as-service nlp automatic-translation bart named-entities-recognition | ai |
FINA4350 | fina4350 finding the correlation of fed speeches and predicting the fed fund rate decision using nlp and sentiment analysis 1 project description blog post can be checked out here https financiallinguists wordpress com please refer to the post for a comprehensive understanding project overview and analysis result 2 create the directories run the following in the terminal to create the directories mkdir data mkdir fomc marketdata loughranmcdonald glove preprocessed models result cd fomc mkdir statement minutes presconf script meeting script script pdf speech testimony chair cd marketdata mkdir quandl please refer to the above link for data with all the files link to data https drive google com drive folders 10qyq nvp4x7hpwwx5jcokxcf6 nj vis usp sharing 3 running the files 1 get data from fomc website by specifying which year br python fomcgetdata py all 1980 2 get calendar from fomc website br python fomcgetcalendar py 1980 3 get data from quandl specify the api key and the date yyyy mm dd br python quandlgetdata py the api key 1980 01 01 4 to run notebook 1 go to top directory br cd src 2 open and run notebooks br jupyter notebook | ai |
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TizenRT | tizenrt license https img shields io badge licence apache 202 0 brightgreen svg style flat license build status https travis ci org samsung tizenrt svg branch master https travis ci org samsung tizenrt tizenrt is lightweight rtos based platform to support low end iot devices please find project details at wiki https github com samsung tizenrt wiki especially documentations page https github com samsung tizenrt wiki documentations contents environment setup environment setup how to build how to build supported board emulator supported board emulator environment setup tizenrt provides the easiest way to build with the use of docker https www docker com there is no need to install the required libraries and toolchains since the provided docker container already includes everything required for tizenrt development however if your development systems are not eligible for running the docker container all libraries and toolchains should be manually installed please refer to manual setup build environment docs howtosetenv md for more information of libraries in the tizenrt docker image see https hub docker com r tizenrt tizenrt 1 install docker to install os specific docker engines see https docs docker com install for example if you are using ubuntu you need to install the docker engine located at https docs docker com install linux docker ce ubuntu if you already have a docker engine please skip this step 2 get tizenrt source code if you are building tizenrt on a windows environment you need to first configure crlf as shown below git config global core autocrlf input now clone tizenrt source code as shown below bash git clone https github com samsung tizenrt git cd tizenrt tizenrt basedir pwd note to contribute in this community you need to create a fork and clone your forked private repository instead of the main repository github guides this by working with forks https help github com articles working with forks how to build there are two ways to build tizenrt using an interactive tool using an interactive tool using specific build options usign specific build options use an interactive tool tizenrt provides an interactive tool dbuild sh where you are prompted to select a option among multiple choices according to your selection it consecutively provides next step options when you become familiar to the tizenrt build system you may use the dbuild sh script with a specific build option note as the build script is running based on docker it requires sudo for root permission to run docker without sudo refer to https docs docker com install linux linux postinstall to get started use the dbuild sh script with the menu option as follows bash cd os dbuild sh menu this command shows you the complete list of supported boards first as shown below bash select board 1 artik053 2 cy4390x x exit after the board selection you are prompted to select configuration of the given board bash select configuration of artik053 1 hello 2 tc x exit finally you are prompted to select a build option as shown below bash select build option 1 build with current configuration 2 re configure 3 modify current configuration 4 clean build 5 clean build and re configure 6 build smartfs image d download t build test x exit once the board and configuration selection is finished you are prompted to select a build option repeatedly until you remove configuration by the re configure or build dist clean option use specific build options 1 configuration bash cd os tools configure sh board configuration set this command retrieves the specific pre configured file named defconfig and make defs according to board configuration set once the configuration is done you can skip this step next time unless you want to change your configuration you can see collection of all configuration files at tizenrt basedir build configs to check all pre defined configurations type as follows bash tools configure sh help 1 1 additional configuration optional after basic configuration by 1 configuration 1 configuration you can additionally modify your configuration with menuconfig bash dbuild sh menuconfig note in docker environment make menuconfig command from other readme files should be replaced with this command make menuconfig applies only in manual setup build environment docs howtosetenv md 2 compilation there are two aspects to compilation namely build and clean which are described as follows build to compile simply type the following bash dbuild sh after compilation built binaries will be located in tizenrt basedir build output bin clean there are two types of clean commands clean and distclean bash dbuild sh clean this command removes built files including objects libraries depend make dep etc after modifying configuration with menuconfig this command is required bash dbuild sh distclean this command includes the clean option and additionally removes configured files including config make defs and linked folders files before changing basic configuration with configure sh command this command is required to delete pre set configurations 3 programming bash dbuild sh download option tizenrt supports download command to program a binary into a board you might be required to set up usb driver for more information please refer to supported board emulator supported board emulator option designates which flash partitions are flashed for example all means programming all of binaries this also depends on each board advanced you can give multiple build options to the dbuild sh script as mentioned below bash dbuild sh distclean configure artik053 hello build download all this executes sequentially multiple commands including 1 execute distclean 2 configure with artik053 hello 3 build 4 program all of binaries supported board emulator tizenrt supports multiple boards as well as qemu the linked page for each board includes board specific environments programming method and board information artik053 details build configs artik053 readme md artik053s details build configs artik053s readme md artik055s details build configs artik055s readme md cy4390x details build configs cy4390x readme md esp32 devkitc details build configs esp32 devkitc readme md esp wrover kit details build configs esp wrover kit readme md imx rt 1020 evk details build configs imxrt1020 evk readme md imx rt 1050 evk details build configs imxrt1050 evk readme md sidk s5jt200 details build configs sidk s5jt200 readme md stm32f407 disc1 details build configs stm32f407 disc1 readme md stm32f429i disco details build configs stm32f429i disco readme md stm32l4r9ai disco details build configs stm32l4r9ai disco readme md qemu details build configs qemu readme md | os |
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computer-science | computer science in javascript contains the basic fundamental data structures and algorithms a front end engineer should know written all in javascript front end engineers need to know a mixture of technologies and methodologies from javascript css html to design patterns mvc mvvm browser performance and responsive design to name but a few concepts however computer science and in particular data structures and algorithms are often ignored while you may not use these on a day to day basis user interfaces are becoming more complex and feature rich every front end engineer should be able to grasp the fundamentals of the following data structures binary trees doubly linked lists hashtables maxheaps queues singly linked lists stacks tries sorting algorithms binary search merge sort quick sort | computer-science javascript front-end frontend | front_end |
Azure-Data-Engineering-with-Databricks | azure data engineering with databricks in this project we used the azure cloud services to design and orchestrate a data pipeline to perform data engineering operations ingestion transformation analysis load on a formula 1 racing dataset formula 1 racing data pipeline p align center img src https i redd it jcfumtrdhue61 jpg width 800 height 400 p summary in this project we used the azure cloud services to design and orchestrate a data pipeline to perform data engineering operations ingestion transformation analysis load on a formula 1 racing dataset data source the data for all the formula 1 races from 1950s onwards is obtained from an open source api called ergast developer api http ergast com mrd the structure of the database is shown in the following er diagram and explained in the database user guide http ergast com docs f1db user guide txt erdiagram http ergast com images ergast db png tools python https www python org pyspark https spark apache org docs latest api python azure databricks https azure microsoft com en us products databricks azure data factory adf https azure microsoft com en us products data factory azure data lake storage gen2 adls https azure microsoft com en us solutions data lake delta lake https learn microsoft com en us azure databricks delta architecture the solution used in this project is based on the modern analytics architecture with azure databricks https learn microsoft com en us azure architecture solution ideas articles azure databricks modern analytics architecture from the azure architecture center p align center img src https learn microsoft com en us azure architecture solution ideas media azure databricks modern analytics architecture diagram png width 70 height 70 p project structure 1 data contains sample raw data from ergast api 2 set up notebooks to mount adls storages raw ingested presentaton in databricks 3 raw contains sql file to create ingested tables using spark sql 4 ingestion contains notebooks to ingest all the data files from raw layer to ingested layer handles the incremental data for files results pitstopes laptimes and qualifying 5 trans contains notebooks to transform the data from ingested layer to presentation layer notebook performs transformations to setup for analysis 6 analysis contains sql files for finding the dominant drivers and teams and to prepare the results for visualization 7 includes includes notebooks containing helper functions used in transformations 8 utils contains sql file to drop all databases for incremental load | cloud |
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USG_RL_Test | usg rl test this project is test for model engineering mlops massive multi cloud service for rl algorithm license gnu 3 0 affero no robots allow image source from opengame art site test game example sdl2 0 socket tcp ip data control by table for game data test https user images githubusercontent com 47798805 230782492 e7639f0b 4253 4cdb b171 42050b61c4ff gif example for table data function cpp include basetable h int main int argc char argv basetable base table data mydata make new data mydata name std string tommy mydata age int 42 mydata size double 182 5f no float is allow only double data mydata2 make new data mydata2 name std string amy mydata2 age int 22 mydata2 size double 168 0f base table add std string int double mydata add new data to table base table add std string int double mydata2 add new data to table auto col base table name age get cols that have name and age auto rows base table col get rows from columns for auto r rows std cout r get int age show all age data base table showtable show all data in string | cloud |
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alternative-front-ends | alternative front ends overview of alternative open source front ends for popular internet platforms e g youtube twitter etc call to action do you know any other free open source projects that are not included in the overview yet front ends alternatives to websites apps etc just create an issue https github com mendel5 alternative front ends issues and let me know i m always looking for new free open source projects to add contents youtube youtube youtube music youtube music twitter twitter reddit reddit tiktok tiktok imgur imgur spotify spotify apple music apple music bandcamp bandcamp twitch twitch discord discord google search google search google translate google translate facebook facebook facebook messenger facebook messenger mastodon mastodon medium medium imdb imdb quora quora reuters reuters apple airplay apple airplay shazam shazam hacker news hacker news telegram telegram other services other services redirection redirection related projects related projects about this repository about this repository overview youtube invidious https github com iv org invidious invidious is an alternative front end to youtube lightweight no ads no tracking no javascript required home page https invidious io invidious instances https docs invidious io instances invidious instances api https api invidious io installation guide for self hosting https docs invidious io installation piped https github com teampiped piped an alternative privacy friendly youtube frontend which is efficient by design lightweight no ads no tracking official instance https piped video public instances https github com teampiped piped wiki instances installation guide for self hosting https github com teampiped documentation blob main content docs self hosting index md poketube https github com ashley0143 poketube the privacy friendly youtube front end built with the innertube api official instance https poketube fun https poketube fun cloudtube https git sr ht cadence cloudtube alternative front end for invidious official instance https tube cadence moe installation guide for self hosting https git sr ht cadence tube docs tree main item docs cloudtube installing 20cloudtube md youtube js https github com luanrt youtube js full featured wrapper around the innertube api which is what youtube itself uses freetube https github com freetubeapp freetube open source youtube desktop player for privacy on windows mac and linux official instance https freetubeapp io invuedious https github com bocchilorenzo invuedious an alternative frontend for invidious built with vue js official instance https bocchilorenzo github io invuedious youtube viewer https github com trizen youtube viewer lightweight youtube client for linux pipe viewer https github com trizen pipe viewer a lightweight application fork of straw viewer for searching and playing videos from youtube invidious viewer https github com git bruh invidious viewer python application to watch youtube videos through the invidious api in the terminal requires mpv player and libmpv so provided by linux distro newpipe https github com teamnewpipe newpipe a libre lightweight streaming front end for android youtube dl https github com ytdl org youtube dl command line program to download videos from youtube com and other video sites openvideodownloader aka jely2002 youtube dl gui https github com jely2002 youtube dl gui a cross platform gui for youtube dl made in electron and node js ytdl gui https github com jagoli ytdl gui a simple to use cross platform graphical interface for youtube dl alltube https github com rudloff alltube web gui for youtube dl vividl https github com bluegrams vividl modern windows gui for youtube dl tartube https github com axcore tartube a gui front end for youtube dl partly based on youtube dl gui and written in python 3 gtk 3 ytmdl https github com deepjyoti30 ytmdl a simple app to get songs from youtube in mp3 format with artist name album name etc from sources like itunes lastfm deezer gaana etc plumber https github com keshavbhatt plumber local and remote video trimmer can trim parts of video without downloading whole video utilizes youtube dl allows conversion to gifs viewtube https github com viewtube viewtube vue an alternative front end for youtube written in vue js uses plyr video player supports sponsorblock multiple invidious instances support chapters youtube local https github com user234683 youtube local browser based client for watching youtube anonymously and with greater page performance yt local https git sr ht heckyel yt local browser based client for watching youtube anonymously without forcing javascript fork of youtube local https github com user234683 youtube local skytube https github com skytubeteam skytube an open source youtube app for android yt dlp https github com yt dlp yt dlp a youtube dl fork with additional features and fixes uyouplus https github com qnblackcat uyouplus uyouplus uyou is an alternative youtube app for apple s ios and ipados smarttubenext https github com yuliskov smarttubenext smarttubenext is an advanced youtube app for android tvs and tv boxes free and open source it is not a live tv client and does not support youtube tv tubesync https github com meeb tubesync tubesync is a pvr personal video recorder for youtube it syncs youtube channels and playlists to a locally hosted media server tubearchivist https github com tubearchivist tubearchivist a self hosted youtube media server ytfzf https github com pystardust ytfzf a posix script that helps you find youtube videos without api and opens downloads them using mpv youtube dl ytcc https github com woefe ytcc command line tool to keep track of your favorite playlists on youtube and many other places can import youtube subscriptions from google takeout and provide them as an rss feed for your favorite reader smtube https github com smplayer dev smtube stand alone youtube video player website https www smtube org smtube is part of smplayer smplayer website https www smplayer info smplayer repository https github com smplayer dev smplayer mps youtube https github com mps youtube mps youtube terminal based youtube player and downloader minitube https github com flaviotordini minitube lightweight youtube client with a kid friendly interface can make playlists from search keywords yattee https github com yattee yattee alternative youtube frontend for ios tvos and macos built with invidious and piped supports sponsorblock ytcast https github com marcolucidi01 ytcast cast youtube videos to your smart tv from the command line this program does roughly the same thing as the play on tv button that appears on the player bar when you visit youtube com with chrome or when you use the youtube smartphone app libretube https github com libre tube libretube android frontend for youtube based on piped blackhole https github com sangwan5688 blackhole android music player app for youtube music and spotify made with flutter oleksis youtube dl gui https github com oleksis youtube dl gui cross platform front end gui of the popular youtube dl written in wxpython youtube music ytmdesktop https github com ytmdesktop ytmdesktop cross platform windows mac and linux desktop app for youtube music has a proprietary remote control app for android beatbump https github com snuffydev beatbump an alternative frontend for youtube music created using svelte sveltekit powered by cloudflare workers official instance https beatbump ml audiotube https invent kde org plasma mobile audiotube client for youtube music plasma mobile project with an interface designed for linux phones th ch youtube music https github com th ch youtube music youtube music desktop app based on electron bundled with custom plugins including built in ad blocker and downloader twitter nitter https github com zedeus nitter alternative twitter front end lightweight no ads no tracking no javascript required official instance nitter net https nitter net public instances https github com zedeus nitter wiki instances example troy hunt on twitter https twitter com troyhunt and nitter https nitter net troyhunt shitter https github com nuclearfog shitter android alternative front end for twitter built with java harpy https github com robertodoering harpy android alternative front end for twitter built with flutter dart twidere x https github com twidereproject twiderex android android alternative front end for twitter built mostly with kotlin in early stage tweeterr https github com sherwyn11 tweeterr a tool to use twitter from the command line on the fly tweet app https github com rhysd tweet app desktop twitter client only for tweeting timeline never shows up tweepy https github com tweepy tweepy twitter for python fritter https github com jonjomckay fritter a free open source twitter client for android reddit teddit https codeberg org teddit teddit alternative reddit front end focused on privacy lightweight no ads no javascript unofficial api official instance https teddit net public instances https github com teddit net teddit instances github mirror repository https github com teddit net teddit example r privacy on reddit https www reddit com r privacy and teddit https teddit net r privacy libreddit https github com libreddit libreddit alternative front end for reddit themed around reddit s new design lightweight no javascript no ads no tracking public instances https github com libreddit libreddit instances blob master instances md xeddit https github com erlingmk xeddit a xamarin forms app for reddit official instance xeddit com https www xeddit com example r privacy on reddit https www reddit com r privacy and xeddit https www xeddit com r privacy redditclient https github com grey r redditsharp alternative front end for reddit built with angular updoot https github com adityam49 updoot android alternative front end for reddit eddrit https github com corenting eddrit alternative front end for reddit inspired by nitter built with python starlette top of reddit https github com mgerb top of reddit top reddit posts every day snew https github com snew snew open source client for reddit forked from the reddit source code stealth https gitlab com cosmosapps stealth account free privacy oriented and feature rich reddit client available on f droid https f droid org en packages com cosmos unreddit infinity https github com docile alligator infinity for reddit reddit client for android available on f droid https f droid org en packages ml docilealligator infinityforreddit dawn https github com tunous dawn open source reddit app available on f droid https f droid org en packages me thanel dank forked from dank https github com saket dank slide https github com ccrama slide open source ad free reddit browser for android available on f droid https f droid org en packages me ccrama redditslide junipf reddit frontend https github com junipf reddit frontend a reddit front end written in react official instance https jpf reddit netlify app kddit https git kalli st kallist kddit spaghetti uwsgi frontend for reddit com written in python official instance https kddit kalli st troddit https github com burhan syed troddit a web client for reddit official instance https www troddit com roffline https github com darkle roffline a self hosted offline reddit server it allows you to browse reddit posts including any media in the post while offline it is targeted at people that have intermittent internet tiktok proxitok https github com pablouser1 proxitok open source alternative frontend for tiktok made with php official instance https proxitok pabloferreiro es imgur rimgo https codeberg org video prize ranch rimgo self hosted frontend for imgur ritten in go public instances https codeberg org video prize ranch rimgo instances rimgu https codeberg org 3np rimgu self hosted alternative frontend proxy for imgur imgin https git voidnet tech kev imgin minimal imgur front end official instance https imgin voidnet tech example album on imgur com https imgur com a gd6p5fi same album on imgin https imgin voidnet tech a gd6p5fi omgur https github com geraldwuhoo omgur omgur is a free and open source alternative imgur front end focused on privacy inspired by the invidious nitter and teddit projects no javascript or ads all requests go through the omgur backend client never talks to imgur prevents imgur from tracking your ip or javascript fingerprint lightweight self hostable imgrs https github com geraldwuhoo imgrs imgrs is a free and open source alternative imgur front end focused on privacy it s a rust rewrite of a previous imgur proxy project omgur spotify psst https github com jpochyla psst fast and multi platform spotify client with native gui spotiqueue https github com toothbrush spotiqueue minimalistic queue oriented macos native client for spotify with guile scheme scriptability spot https github com xou816 spot gtk rust native spotify client for the gnome desktop only works with premium accounts spotube https github com krtirtho spotube a lightweight and free spotify crossplatform client which handles playback manually streams music using youtube no spotify premium account is needed spotx https github com amd64fox spotx modified spotify client for windows windows only blocking ads and updates for the desktop version of spotify disabling podcasts and more kotify https github com dzirbel kotify requires spotify account multiplatform desktop client for spotify focused on library organization for power users relies on official spotify client for playback librespot https github com librespot org librespot requires spotify premium account librespot is an open source client library for spotify it enables applications to use spotify s service to control and play music via various backends and to act as a spotify connect receiver it is an alternative to the official and now deprecated closed source libspotify additionally it will provide extra features which are not available in the official library spotifyd https github com spotifyd spotifyd unix daemon using librespot apple music cider https github com ciderapp cider cross platform apple music experience based on electron and vue js written from scratch with performance in mind bandcamp tent https codeberg org sun tent a simple alternative front end for bandcamp that does not require javascript and proxies all requests twitch streamlink twitch gui https github com streamlink streamlink twitch gui multi platform twitch tv browser for streamlink twire https github com twireapp twire alternative and open source twitch client for android xtra https github com crackededed xtra twitch player and browser for android electronplayer https github com oscartbeaumont electronplayer electron based web video services player supports netflix youtube twitch floatplane hulu and more discord gtkcord4 https github com diamondburned gtkcord4 a lightweight discord client written in golang which uses gtk3 for the user interface openasar https github com goosemod openasar an open source alternative of discord desktop s app asar google search whoogle search https github com benbusby whoogle search a self hosted ad free privacy respecting metasearch engine for google public instances https github com benbusby whoogle search public instances searx https github com searx searx searx is a free privacy respecting internet metasearch engine which aggregates results from more than 70 search services users are neither tracked nor profiled additionally searx can be used over tor for online anonymity public instances https searx space searxng https github com searxng searxng searxng is a free internet metasearch engine which aggregates results from various search services and databases users are neither tracked nor profiled searxng is a fork of searx librex https github com hnhx librex privacy respecting free meta search engine free as in freedom small and simple meta search engine fetches and anonymizes results from google only has api support allows redirects to invidious bibliogram nitter libreddit google translate lingva translate https github com thedaviddelta lingva translate alternative front end for google translate serving as a free and open source translator with over a hundred languages available official instance lingva ml https lingva ml public instances https github com thedaviddelta lingva translate instances simplytranslate https codeberg org simpleweb simplytranslate web provide fast and private translations to the user without wasting much overhead for extensive styling or javascript supports google translate deepl iciba and libretranslate home page and public instances https simple web org projects simplytranslate html simplytranslate mobile https github com manerakai simplytranslate mobile unofficial android client of simplytranslate available on f droid https f droid org en packages com simplytranslate mobile instalate https gitlab com concept1tech instalate distraction free translation for android to be used directly from within any app supports beolingus deepl dict cc gnu cide heinzelnisse libretranslate linguee wikdict and wiktionary available on f droid https f droid org en packages com concept1tech instalate deepl android https github com sakusaku3939 deeplandroid unofficial android client for deepl available on f droid https f droid org en packages com example deeplviewer crow translate https github com crow translate crow translate simple and lightweight cross platform translator that allows translation using libretranslate lingva google bing and yandex as well as text to speech using google facebook slimsocial https github com rignaneseleo slimsocial for facebook android alternative front end for facebook built with java frost https github com allanwang frost for facebook an extensive and functional third party app for facebook android app facebook messenger caprine https github com sindresorhus caprine unofficial and privacy focused facebook messenger app with many useful features mastodon sengi https github com nicolasconstant sengi cross platform multi account mastodon pleroma desktop client thedesk https github com cutls thedesk cross platform mastodon misskey desktop client tootle https gitlab gnome org world tootle simple gtk based linux mastodon client tusky https github com tuskyapp tusky lightweight android mastodon client fedilab https codeberg org tom79 fedilab multi account android mastodon client pinafore https github com nolanlawson pinafore alternative web client for mastodon focused on speed and simplicity unmaintained https nolanlawson com 2023 01 09 retiring pinafore hyperspace https github com hyperspacedev hyperspace cross platform mastodon client for the fediverse written in typescript and react in maintenance mode https github com hyperspacedev hyperspace issues 232 medium scribe https sr ht edwardloveall scribe alternative front end to medium com official website https scribe rip libmedium https github com realaravinth libmedium alternative front end to medium com official website https libmedium batsense net imdb libremdb https github com zyachel libremdb a foss alternative front end to imdb official instance https libremdb iket me public instances https github com zyachel libremdb instances quora quetre https github com zyachel quetre a libre front end for quora official website https quetre iket me public instances https github com zyachel quetre instances reuters neuters https github com hookedbehemoth neuters an alternative front end to reuters com it is intented to be lightweight and fast and was heavily inspired by nitter official instance https neuters de apple airplay rpiplay https github com fd rpiplay an open source airplay mirroring server for the raspberry pi supports ios 9 and up air pi play https github com rahul thakoor air pi play turn a raspberry pi into an airplay server using rpiplay to enable screen mirroring on tvs monitors and projectors shazam songrec https github com marin m songrec open source shazam client for linux written in rust telegram telegram foss https github com telegram foss team telegram foss unofficial foss friendly fork of the original telegram client for android hacker news hn search https github com algolia hn search algolia hacker news search example highest rated submissions of the past 24 hours https hn algolia com sort bypopularity page 0 daterange last24h type all hntoplinks https github com eguller hntoplinks top links on hacker news hackerweb https github com cheeaun hackerweb a simply readable hacker news web app official instance https hackerweb app about hackerweb https hackerwebapp com hckrnws https github com rajatkulkarni95 hckrnws a custom front end for a better reading experience of hackernews official instance https www hckrnws com hackers https github com weiran hackers a native ios app for hacker news available to download on the apple appstore https apps apple com us app hackers for hacker news id603503901 other services pastewin https github com beucismis pastewin free alternative pastebin front end mediathekviewweb https github com mediathekview mediathekviewweb video content of german public service television broadcasters e g ard zdf official instance mediathekviewweb de https mediathekviewweb de nopaste https github com bokub nopaste nopaste is an open source website similar to pastebin where you can store any piece of code and generate links for easy sharing privatebin https github com privatebin privatebin zero knowledge encrypted paste bin a minimalist open source online pastebin where the server has zero knowledge of pasted data data is encrypted decrypted in the browser using 256 bits aes vaultwarden https github com dani garcia vaultwarden password manager unofficial bitwarden compatible server written in rust formerly known as bitwarden rs snapdrop https github com robinlinus snapdrop similar to apple s airdrop but in your browser a progressive web app for local file sharing hedgedoc https github com hedgedoc hedgedoc collaborative markdown editor a platform to write and share markdown etherpad lite https github com ether etherpad lite collaborative rich text editor a modern really real time collaborative document editor gitea https github com go gitea gitea lightweight git server git with a cup of tea painless self hosted git service archivebox https github com archivebox archivebox open source self hosted web archiving takes urls browser history bookmarks pocket pinboard etc saves html js pdfs media and more wikiless https github com metastem wikiless a free open source alternative wikipedia front end focused on privacy official instance https wikiless org public instances https github com metastem wikiless librarian https codeberg org librarian librarian alternative frontend for lbry odysee com public instances https codeberg org librarian librarian instances redirection privacy redirect https github com simonbrazell privacy redirect a simple web extension that redirects twitter youtube google maps requests to privacy friendly alternatives firefox add on privacy redirect https addons mozilla org en us firefox addon privacy redirect chrome extension privacy redirect https chrome google com webstore detail privacy redirect pmcmeagblkinmogikoikkdjiligflglb libredirect https github com libredirect libredirect a web extension that redirects popular sites to alternative privacy friendly frontends and backends actively maintained fork of privacy redirect that supports youtube youtube music twitter tiktok imgur reddit searx google translate google maps wikipedia and medium firefox add on https addons mozilla org firefox addon libredirect chrome extension https libredirect github io download chromium html farside https github com benbusby farside farside provides links that automatically redirect to working instances of privacy oriented alternative frontends such as nitter libreddit etc this allows for users to have more reliable access to the available public instances for a particular service while also helping to distribute traffic more evenly across all instances and avoid performance bottlenecks and rate limiting untrackme https framagit org tom79 nitterizeme untrackme transforms twitter youtube reddit and medium and wikipedia links to links of open source privacy friendly front ends converts google maps links to openstreetmap links removes tracking parameters from any url then delegates the action to other apps that are capable of handling them android app redirector https github com einaregilsson redirector web browser extension firefox vivaldi chrome opera edge to redirect urls based on regex or wildcard patterns firefox add on redirector https addons mozilla org firefox addon redirector chrome extension redirector https chrome google com webstore detail redirector ocgpenflpmgnfapjedencafcfakcekcd related projects ublock origin https github com gorhill ublock an efficient blocker for chromium and firefox fast and lean firefox add on ublock origin https addons mozilla org en us firefox addon ublock origin chrome extension ublock origin https chrome google com webstore detail ublock origin cjpalhdlnbpafiamejdnhcphjbkeiagm streetcomplete https github com streetcomplete streetcomplete easy to use openstreetmap editor for android matrix org s synapse https github com matrix org synapse end to end encrypted messaging matrix reference homeserver see also matrix org https matrix org pluja s awesome privacy https github com pluja awesome privacy a curated list of services and alternatives that respect your privacy because privacy matters 12ft io 12ft ladder https 12ft io 12ft ladder is a free service for reading news articles prepend 12ft io to the url of any paywalled page and we ll try our best to remove the paywall and get you access to the article it is similar to outline com which is not available anymore note the source code of 12ft ladder is not available under a free open source license youtube vanced https github com ytvanced youtube replacement app for the android platform youtube vanced is the stock android youtube app but better it includes adblocking true amoled dark mode and a lot more use the vanced manager to install youtube vanced with ease official website with install instructions https vancedapp com note the source code of youtube vanced is not available under a free open source license for an explanation about the origin of youtube vanced see here https old reddit com r vanced comments o3xm9m if youtube vanced isnt open source and doesnt h2ec7wf vanced was forced to shut down by google due to legal reasons the project https github com revanced tries to continue its legacy about this repository this overview originally included three alternative front ends invidious for youtube bibliogram for instagram and nitter for twitter therefore it was named alternative front ends as more projects have been added to the repository the listed projects partially left the scope of alternative front ends for example youtube dl is not a front end but can be generally described as an open source project that interacts with the internet platform youtube therefore the name alternative front ends does not capture the full scope of the listed projects anymore maybe this repository will be renamed in the future to better reflect the larger scope a possible name might be open source alternatives or something similar | frontend invidious youtube nitter twitter instagram bibliogram reddit privacy alternative-frontends degoogle adblock alternative awesome awesome-list tracking awesome-lists youtube-dl | front_end |
iceCreamTruckTracker | ice track project | server |
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Linkedin-Analysis | linkedin analysis final year project of master of information technology the aim of the project is scraping data from linkedin and doing an analysis structure web the web application to show the result linkedintest contains the scarper developed by selenium and beartifulsoup in python2 7 and the row data dataextract contains the data and extract code by python developer xunwan wang email xunwanw student unimelb edu au siyu zhang email siyuz6 student unimelb edu au contribution xunwan wang scraping data cleaning and extract data and writing report siyu zhang web development scraping data and writing report running web start from npm start using the command in terminal under the web directory linkedtest the code cannot be run directly at the beginning please download the browser driver from https github com mozilla geckodriver releases or http chromedriver chromium org for google chrome then add the abolute path of the driver in function webdriver firefox path or webdriver chrome path project video link https www youtube com watch v 6v2j9hrc48e feature youtu be copyright data collection selenium http www apache org licenses license 2 0 beautifulsoup https github com akalongman python beautifulsoup blob master license phantombuster https phantombuster com node js https raw githubusercontent com nodejs node master license express js https github com expressjs express blob master license jquery https jquery org license ajax https github com forbeslindesay ajax blob master license echart https github com zhuhuihuihui echart blob master license md web page template www chinaz com icons8 https icons8 com webstorm https www jetbrains com webstorm pycharm https www jetbrains com pycharm photoshop https www adobe com au products photoshop html gclid cjwkcajwvnxebrajeiwajqyhft5rrw3hva aidwt ligsyfenn6frcgkmdurtimng8uhwn2pqdbrxochn0qavd bwe sdid ty6xl4mr mv search ef id cjwkcajwvnxebrajeiwajqyhft5rrw3hva aidwt ligsyfenn6frcgkmdurtimng8uhwn2pqdbrxochn0qavd bwe g s s kwcid al 3085 3 102933806464 b g adobe 20photoshop 20cs6 | server |
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politwoops_sunlight | setting up the app these instructions assume that you are using rvm rbenv or something else that is compatible with ruby version gem install bundler bundle install create config database yml see config database yml example create config config yml see config config yml example rake db create rake db schema load rake db migrate you can now run rails server and navigate to admin users to manually add politicians alternatively you can load politicians in bulk from a csv spreadsheet using the rake politicians import csv csv myfile csv command see twoopsters csv as an example of the format to use if you use the politicians import csv task you should then use the politicians reset avatars task as well this will download the avatars currently in use by each politician to restart workers sudo initctl start beanstalkd sudo initctl restart pt tweets client | front_end |
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crosstalk-generation | crosstalk generation rocket rocket rocket rocket rocket rocket https github com anonno2 crosstalk generation blob main readme zh cn md english https github com anonno2 crosstalk generation tada the largest chinese open source crosstalk dataset so far collected from the internet books and other open source projects tada data types include stand up crosstalk paired up crosstalk group crosstalk and sketches tada since this study is only for cross talk the training set provided only includes cross talk for other types please refer to the data description initial data module quick start quick start data description data description model description model description machine metrics machine metrics innovative metrics innovative metrics human scoring metrics human scoring metrics correlation coefficient correlation coefficient between human metrics and machine metrics generate examples generate examples quick start image 20220417153303002 https github com anonno2 crosstalk generation blob main img container server png model type docker file googledriver file baidudriver t5 anon2010 crosstalk gen env t5 download https drive google com file d 1xcrhkk1d7cakeyjyctwbirf0kaieebsr view usp sharing download https pan baidu com s 1wv73 io7oytujzbkddf9iw pwd t8i9 gpt pangu a cpm to be continued steps for usage 1 download the relevant images and files first 2 start the model image take t5 as an example here docker run dp 32488 8080 memory 3g v model res model res anon2010 crosstalk gen env t5 3 access the host port model res model files decompression path memory specify the maximum memory limit too small may cause failure to start data description the data is divided into two parts initial data and training data initial data including sketches crawled from the internet single performing dual performing group performing and ketch comedy src common data meta json metadata for full data completemetaexportdata zip full data type number single performing 168 dual performing 3685 group performing 256 ketch comedy 5222 plain dialogue texts extract from which text above 4859 full data 9331 number total words 16481376 number of utterances 663305 number of long utterances 8717 number of short utterances 446756 median word numbers of utterances 16 mean utterances per script 71 metadata format description isalldialog true whether it is a pure dialogue format charsize 526 word count in this scripts filepath u399dy relative path roles roles sentencesize 25 utterances size source https www 399dy com xiangsheng 11176 html source idx 28 index title title type type in text formatting example train data the 2948 dialogues were cleaned and screened from the initial data the validation set was 368 dialogues selected from the initial data and the test set was 10 rounds of dialogue among the 50 dialogues selected from the initial data opened in project src common data src common data train raw zip training corpus in original format contains meta information dev raw zip verification corpus in original format contains meta information train pure text txt training corpus in plain text format dev pure text txt verification corpus in plain text format test filter 50x20 txt test corpus machine index calculation and manual evaluation are based on this data data number of scripts number of utterances word count training corpus 2948 173194 3184664 verification 368 41482 905201 test corpus 50 1000 19268 model description finetune gpt t5 t5 small unilm rnn gpt3 zero shot inference cpm large gpt3 pangu a zhouwenwang machine metrics for details of the data files and benchmarking methods used for machine index scoring see machine metrics https github com anonno2 crosstalk generation blob main eval data machine eval readme md bleu 1 bleu 2 bleu 3 bleu 4 gleu rouge 1 rouge 2 rouge l distinct 1 distinct 2 gpt ep50 10 04 3 69 1 53 0 7 2 75 15 28 1 78 13 7 6 89 37 39 t5 pesg ep15 11 75 5 58 3 13 1 77 3 94 20 8 4 98 19 25 9 02 42 68 small t5 pesg ep95 11 71 5 39 2 93 1 67 3 64 19 98 4 37 18 61 8 08 36 38 cpm large 7 94 2 87 1 19 0 5 1 68 9 88 1 28 8 83 5 82 34 43 unilm ep45 8 88 4 32 2 47 1 41 3 36 20 22 4 91 18 98 7 53 29 90 rnn 11 77 4 02 1 47 0 57 2 49 17 25 2 13 15 94 4 73 16 23 gpt3 base davinci 14 68 7 45 4 44 2 77 5 13 22 25 5 65 20 03 8 43 40 7 gpt3 ft200 davinci 9 66 4 89 3 01 1 92 4 66 21 79 5 5 20 22 9 725 43 15 gpt3 ft1000 davinci 13 39 6 86 4 14 2 55 5 18 22 83 5 68 20 73 9 55 45 56 panggu a 6 42 2 09 0 83 0 37 1 31 7 0 75 6 14 8 25 50 98 zhouwenwang 7 33 2 26 0 9 0 4 1 81 10 41 1 01 8 61 9 72 53 53 ps the reason why gpt3 ft1000 is not included in the paper is because we only select one of the similar models when manually marking in the previous gpt3 finetune manual comparison the manual score of ft200 is higher general quality humor coherence ethically risky flag gpt3 ft200 davinci 145 115 43 2 gpt3 ft1000 davinci 136 109 40 2 some small discoveries 1 gpt3 davinci s machine metrics are significantly higher than any other generation method 2 most large models zero shot inference significantly worse direct inference than finetune 3 after gpt3 goes through finetune the machine index drops but in the following manual scoring it will get a higher score higher machine indicators are not necessarily more in line with human reading habits innovative metrics zhouwenwang unilm t5 smallt5 95 rnn pangu a gpt3 finetune gpt3 base gpt ep50 cpm large 1 0 0695 0 0 0 0 0 00754 0 0 00293 0 0 04777 2 0 23223 0 07405 0 03709 0 02538 0 01244 0 10615 0 02337 0 028 0 00826 0 22447 3 0 58549 0 37475 0 22624 0 19325 0 10785 0 43833 0 16998 0 18063 0 13619 0 53327 4 0 83795 0 68884 0 51332 0 45936 0 32618 0 73634 0 4297 0 42368 0 43385 0 78466 5 0 94343 0 86874 0 75219 0 68772 0 62232 0 91339 0 67156 0 64273 0 73764 0 91419 6 0 97811 0 94026 0 88568 0 82587 0 84941 0 97563 0 81931 0 76029 0 91134 0 9615 7 0 98968 0 96837 0 94607 0 90037 0 94703 0 99402 0 88814 0 80665 0 97395 0 98139 8 0 99347 0 98052 0 97288 0 94256 0 98062 0 9987 0 91946 0 82957 0 99267 0 99015 9 0 99544 0 98527 0 98457 0 9669 0 99051 0 99972 0 93244 0 84394 0 99757 0 99252 10 0 99711 0 98799 0 99007 0 98092 0 99472 1 0 93793 0 85047 0 9993 0 99393 image 20220417153303002 https github com anonno2 crosstalk generation blob main img image 20220417153303002 png some small discoveries 1 zhouwenwang cpm pangu a zero shot inference is more innovative 2 in the finetune model unilm and t5 are relatively more innovative 3 models with higher innovative evaluations are often generated more randomly human scoring metrics the numbers in parentheses of the column names are human scoring of the real data the combined score and humor score are capped at 750 fluency and discrimination scores are capped at 150 for details of the data files used for manual scoring and the scoring method see manual scoring https github com anonno2 crosstalk generation blob main eval data human eval readme md test design here we will infer a total of 10 pre trained models and large models excluding t5 small because the effect is not as good as t5 plus the original data for the first 10 sentences of the 50 segments of the test set to infer the next 10 sentences a total of 30 subjects with different genders occupations ages and beliefs were found and asked to rate the samples generated by the model the comprehensive score and humor score were scored on a 5 point scale and the fluency and discrimination were on a 1 point scale 1 if yes otherwise 0 general quality 528 humor 519 coherence 143 ethically risky flag 3 gpt ep50 225 256 59 2 t5 pesg ep15 270 296 76 7 cpm large 213 240 60 34 unilm ep45 276 301 84 2 rnn 217 242 41 4 gpt3 base davinci 322 325 98 5 gpt3 ft200 davinci 341 353 106 2 panggu a 230 257 63 4 zhouwenwang 184 191 28 8 some small discoveries 1 although gpt3 s machine indicators have declined after finetune all dimensions of manual scoring are ranked first machine metrics don t fully assess how well a generation works 2 compared with gpt and rnn the generation quality of model structures such as unilm and t5 will be significantly improved correlation coefficient between human metrics and machine metrics pearsonr correlation https github com anonno2 crosstalk generation blob main img pearsonr correlation 20coefficient png pearsonr correlation https github com anonno2 crosstalk generation blob main img spearman correlation 20coefficient png generate examples short stories generated by gpt3 chinese https github com anonno2 crosstalk generation blob main gpt3 generate samples short sample zh cn md english https github com anonno2 crosstalk generation blob main gpt3 generate samples short sample en us md long stories generated by gpt3 chinese https github com anonno2 crosstalk generation blob main gpt3 generate samples long sample zh cn md english https github com anonno2 crosstalk generation blob main gpt3 generate samples long sample en us md interactive generation demo 1 user chatbot user chatbot user chatbot user chatbot user chatbot user chatbot user chatbot demo 2 user chatbot user chatbot user chatbot user chatbot user chatbot demo 3 user chatbot user chatbot user chatbot user chatbot user chatbot user anon chatbot script generation demo 1 demo 2 demo 3 example of generating different models in the same context demo 1 2700 context context unilm 2700 context context gpt 2700 context context gpt3 2700 context context seq2seq 2700 context context demo 2 context context unilm context context gpt context context gpt3 context context seq2seq context context demo 3 context context unilm context context gpt context context gpt3 context context seq2seq context context | chinese crosstalk-dataset gpt-2 humor-generation pretrained-models t5 text-generation | ai |
Effecient-Urdu-Caption-Generation-using-Attention-Mechanism | h1 effecient urdu caption generation using attention mechanism h1 h2 abstract h2 p recent advancements in deep learning has created a lot of opportunities to solve those real world problems which remained unsolved for more than a decade automatic caption generation is a major research field and the research community has done a lot of work on this problem in most common languages like english urdu is the national language of pakistan and also much spoken and understood in the sub continent region of pakistan india and yet no work has been done for urdu language caption generation our research aims to fill this gap by developing an attention based deep learning model using techniques of sequence modelling specialized for urdu language we have prepared a dataset in urdu language by translating a subset of flickr8k dataset containing 700 man images we evaluate our proposed technique on this dataset and show that it is able to achieve a bleu score of 0 83 on urdu language we improve on the previously proposed techniques by using better cnn architectures and optimization techniques furthermore we also tried adding a grammar loss to the model in order to make the predictions grammatically correct p h2 dataset h2 p as our main task is caption generation on images in urdu language there was no publicaly available dataset for this task then we decided to translate a popular image captioning dataset called flickr8k dataset from scratch as available translators were not suffeciently accurate especially on idioms and context understanding flickr8k dataset has 8 000 images and for each image there are 5 captions in english we selected about 700 images with 3500 captions with similar context to translate into urdu the selected captions are related to a man who is doing different activities such as water boarding snow boarding and biking p br img src images data jpg alt sample from translated dataset br original flickr8k dataset a href https www kaggle com shadabhussain flickr8k download here a br translated captions in urdu a href https drive google com file d 1xtepgkvoqkzwrdw0 hx tiyiplig3xy9 view usp sharing download here a hr h2 model h2 p most of our caption generation model is inspired by show attend and tell neural image caption generation with visual attention a href https www google com url sa t rct j q esrc s source web cd cad rja uact 8 ved 2ahukewj06ssislnqahubdxokhxxwdviqfjaaegqiaxab url https 3a 2f 2farxiv org 2fabs 2f1502 03044 usg aovvaw2fkpna9ngkhsomoo1ipkea 1 a model consist of encoder and a decoder architecture encoder part uses a cnn we did experiments of resnet 101 v2 inceptionv3 and xception to extract features from images while decoder uses attention mechanism and gru we used bahdanau attention in our model the main purpose of the attention mechanism is to focus on relevant part of image gru is just like lstm with less parameters and gates that makes it faster and less computationally expansive gru is used to generate next word of caption in current time step the generated word is based on features of image previous hidden state of gru current input to decoder and context vector generated by attention mechanism in the end model check if the grammar of generated caption is good or bad p h4 example of working of attention mechanism h4 img src images attention png alt attention h4 model diagram h4 img src images model png alt model h2 generated captions h2 img src images generated png alt model h2 results h2 p we used bleu score as evaluation matrix to evaluate performance of model p br img src images results png alt results h4 contribution h4 members of our group are my self b hafiz muhammad abdullah zia b inam ilahi armughan ahmed rauf tabassum and ahtazaz ahsan | caption-generation image-captioning urdu-image-captioning | server |
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hyperledger-typescript-boilerplate | center https cdn images 1 medium com max 1200 1 2646bxdq2ich onfpolaxq jpeg hyperledger typescript boilerplate https medium com wearetheledger hyperledger fabric typescript boilerplate 455004d0c6c8 note this boilerplate has been designed for quickstarting prototype applications some practices used in this boilerplate are not recommended for a production environment build status https travis ci org wearetheledger hyperledger typescript boilerplate svg branch master https travis ci org wearetheledger hyperledger typescript boilerplate greenkeeper badge https badges greenkeeper io wearetheledger hyperledger typescript boilerplate svg https greenkeeper io center this is a starter template that interacts between hyperledger fabric peers and a front end currently this boilerplate provides the following features connects out of the box with the fabcar sample network https github com hyperledger fabric samples tree release fabcar express backend built with typescript using nest https github com kamilmysliwiec nest restful routing to connect a custom frontend automatic openapi swagger generation fabric client and fabric ca client abstraction https martinfowler com bliki cqrs html frontend chain transaction notifications using pusher https pusher com example authentication using auth0 https auth0 com automatic attribute based access crontrol using auth0 installation in order for this application to run properly and handle invokes concurrently you should have set up an account on aws to be able to use the aws sqs service a pusher https pusher com account is also required if you want to make use of transaction event handling in a frontend auth0 https auth0 com will require auth0 credentials be sure to have an up to date node js configuration engines node 8 9 npm 5 6 install dependencies npm i starting the app npm start without auth to test this application without auth you can enable the skip middleware flag in the env beware that this is not best practise and will also disable other security measures e2e tests using jest wip npm test work in progress and future plans switch to nestjs microservices use of redis queue instead of sqs ci pipeline config for gitlab kubernetes on google cloud graphql integration auth mock to circumvent auth0 for rapid prototyping contributing 1 fork it 2 create your feature branch git checkout b my new feature 3 commit your changes git commit am add some feature 4 push to the branch git push origin my new feature 5 submit a pull request credits developer jonas superjo149 https github com superjo149 developer jo jestersimpps https github com jestersimpps company theledger theledger be https theledger be license the mit license mit copyright c 2018 theledger permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | hyperledger-fabric typescript starter-template starter-kit starter-project fabric pusher boilerplate hyperledger-typescript-boilerplate | front_end |
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jaredwolff com blog get started with bluetooth low energy crackle cracking encryption https github com mikeryan crackle bettercap https github com bettercap bettercap btlejuice bluetooth smart man in the middle framework https github com digitalsecurity btlejuice gattacker https github com securing gattacker btlejack bluetooth low energy swiss army knife https github com virtualabs btlejack bluing an intelligence gathering tool for hacking bluetooth https github com fo 000 bluing dedsec bluetooth exploit https github com 0xbitx dedsec bluetooth exploit hardware for bluetooth hacking nrfconnect 52840 https www nordicsemi com software and tools development kits nrf52840 dongle edimax https www nordicsemi com software and tools development kits nrf52840 dongle csr 4 0 https www amazon in generic ultra mini bluetooth dongle adapter dp b0117h7gz6 ref asc df b0117h7gz6 tag googleshopdes 21 linkcode df0 hvadid 396984700257 hvpos 1o1 hvnetw g hvrand 2179727910417729406 hvpone hvptwo hvqmt 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testing mojobox security bluetooth hacking https github com zedxpace bluetooth hacking dect digital enhanced cordless telecommunications real time interception and monitoring of a dect cordless telephone https www youtube com watch v mdf1euvote0 ab channel robvk8foes eavesdropping on unencrypted dect voice traffic https www youtube com watch v wbvysxrs3di ab channel robvk8foes decoding dect voice traffic in depth explanation https www youtube com watch v oimkirm xfy ab channel robvk8foes software tools hardware tools software hardware mobile security android ios android app reverse engineering 101 https maddiestone github io androidappre android application pentesting book https www packtpub com hardware and creative learning pentesting android devices android pentest video course tutorialspoint https www youtube com watch v zhknria3i6s list plwpirh4ewfpeslreb04c4ezocvjqjrc6h ios pentesting https web securityinnovation com hubfs ios 20hacking 20guide pdf owasp mobile security testing guide https owasp org www project mobile security testing guide android tamer android tamer is a virtual live platform for android security professionals https androidtamer com online assemblers azm online arm assembler by azeria https azeria labs com azm online disassembler https onlinedisassembler com odaweb compiler explorer is an interactive online compiler which shows the assembly output of compiled c rust go https godbolt org arm azeria labs https azeria labs com arm exploitation for iot https www exploit db com docs english 43906 arm exploitation for iot pdf damn vulnerable arm router dvar https blog exploitlab net 2018 01 dvar damn vulnerable arm router html exploit education https exploit education pentesting firmwares and emulating and analyzing firmware analysis tools emba an analyzer for embedded linux firmware https p4cx medium com emba b370ce503602 fact firmware analysis and comparison tool https github com fkie cad fact core binwalk https github com refirmlabs binwalk 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embedded linux devices with qemu cf chl captcha tk 2167fb6cf097848dbf0dea8e4ecccc66f2a55e55 1585030085 0 avfo7wg mhgvnigeil aiklnw1imb5imlyqlosolydnzfzhyayyswgfkvjvllatytmpbjjnatlwyawio2khxh4apqluott0r7ureyctz3u g4ajbk4eonel2btjcahg3fzmxhrc 3iaqccnqc4jx1rwez60y mkfq63nveoe1pc0ebywkk7vqdwusbfbgpj6zrnv0ifklc3olyjck og13jesbpisvlmcn6bchvlatp2gw7qg6greliwgdyfp9viymdsaww3u r1nmugrqzctxiymwh1mdl5p8lqbspca160cw3jaz16ixt7ip1hkcburx7rcovp3daci8zrc19v9mi ju9nxiw0xf9eipqlup r txfnw4vf10pwigkmg0cpl2iduy1ty3j8koqkdvxfe emulating embedded linux systems with qemu https www novetta com 2018 02 emulating embedded linux systems with qemu cf chl captcha tk 9dd83a08cffb28fae75286f63f399c34eec56852 1585030087 0 ablgaud4lcdvbghngqyfl5hgpxnc8pucliabpupx2tbob l4gvvc1sz7ivg0g 06wpkdpev kylzu3t yqgr7gdfpc2ckzxatdc bsev7uu1ljictflohtw b1vvjfae3qxdex4kkn2d4huqiw9wlszvo2ff8svvfephobumoavj c2ixceb2ddfmok3 hb 3 y7q beax3xqdcjqz7tpcoiwt wtsqwrfx vubfo87hrtsx43yzq6bnjce9s15zqmpp nouyihnmnx3augafkwzbsua0r43gba 3jlmjste qvcn7gmz har gpna usn cr94vqtynpl0vesc1omf48obmmofqja6jjn1hgpv5hv4m4abtjrtnforp2ygwxayntm3df9qw1iybb8r58 fuzzing embedded linux devices https www novetta com 2018 07 fuzzing embedded linux devices cf chl captcha tk f07f3f76e61b43f9ae6340e94cf4adeaec87977e 1585030089 0 aykrnbh1wpuia0p5wbgrrfhf92uy6pl2meeboxi2fuvxrooj9obk4zis78y4icrrmdi3umwqrjeyf0u3epwhpu3 22f5pwovvdfc0qwfpyw7lky5bluansi 8uoeunulieq1vpizhfpht1vt0 rw4yrygc8osjzpubahxfyze1g7u ibpzj9tdrue6swga ph0io4lrfbjuvpem03nhuc1sttqrvdkwiw47kmr9usak10zmqeve7zpbpkejm2slchjdyq6hzim3l5l8vb eem jvxsshbgfddm3ksftw3oxlykvxvly llsyyefuub4yobrqngzv1gj pdtmurtmxobgo7vzardr2lgoxml58kpg6ntdlb3a4yzwvw9u32errh4ab89vn90rshlwnu928oc emulating arm router firmware https azeria labs com emulating arm firmware reversing firmware with radare https www bored nerds com reversing radare automotive 2019 07 07 reversing firmware with radare html samsung firmware magic unpacking and decrypting https github com chrivers samsung firmware magic qiling binary emulation for automatic unpacking https kernemporium github io articles en auto unpacking m html reverse engineering with ghidra breaking an embedded firmware encryption scheme https www youtube com watch v 4urmitjkqqs ab channel stacksmashing simulating and hunting firmware vulnerabilities with qiling https blog vincss net 2020 12 pt007 simulating and hunting firmware vulnerabilities with qiling html m 1 s 09 firmware samples to pentest download from here by firmware center https firmware center symlinks attacks zip slip vulnerability https security snyk io research zip slip vulnerability secureboot dev writing a bootloader http 3zanders co uk 2017 10 13 writing a bootloader hacking pwn the esp32 secure boot https limitedresults com 2019 09 pwn the esp32 secure boot pwn the esp32 forever flash encryption and sec boot keys extraction https limitedresults com 2019 11 pwn the esp32 forever flash encryption and sec boot keys extraction amlogic s905 soc bypassing the not so secure boot to 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hardware guide https www postscapes com internet of things hardware intro to hardware hacking dumping your first firmware https blog nvisium com intro to hardware hacking dumping your first firmware hardware gadgets to pentest bus pirate https www sparkfun com products 12942 eeprom reader soic cable https www sparkfun com products 13153 jtagulator jtagenum https www adafruit com product 1550 logic analyzer https www saleae com the shikra https int3 cc products the shikra facedancer21 usb emulator usb fuzzer https int3 cc products facedancer21 rfcat https int3 cc products rfcat hak5gear hak5fieldkits https hakshop com ultra mini bluetooth csr 4 0 usb dongle adapter https www ebay in itm ultra mini bluetooth csr 4 0 usb dongle adapter black golden with 2 yr wrnty 332302813975 attify badge uart jtag spi i2c w headers https www attify store com products attify badge assess security of iot devices attacking hardware interfaces an introduction to hardware hacking https securityboulevard com 2020 09 an introduction to hardware hacking serial terminal basics https learn sparkfun com tutorials terminal basics all reverse engineering serial ports http www devttys0 com 2012 11 reverse engineering serial ports reverse engineering architecture and pinout of custom asics https sec consult com en blog 2019 02 reverse engineering architecture pinout plc chipwhisperer hardware attacks http wiki newae com main page hardware hacking tutorial dumping and reversing firmware https ivanorsolic github io post hardwarehacking1 spi reading flashroms youtube https www youtube com results search query reading chip flash rom dumping the firmware from router using buspirate spi dump https www iotpentest com 2019 06 dumping firmware from device using html how to flash chip of a router with a programmer tp link router repair mac address change https www youtube com watch v fbt4ojxjdoc ab channel electricalprojects 5bcreativelab 5d extracting flash memory over spi https akimbocore com article 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journey into iot hardware hacking uart uartbruteforcer https github com firefart uartbruteforcer uart connections and dynamic analysis on linksys e1000 https www youtube com watch v ix6rsv2dj44 ab channel defenceindepth accessing and dumping firmware through uart https www cyberark com resources threat research blog accessing and dumping firmware through uart uart exploiter https github com exploitsecurityio uart exploiter jtag hardware hacking 101 introduction to jtag https www riverloopsecurity com blog 2021 05 hw 101 jtag how to find the jtag interface hardware hacking tutorial https www youtube com watch v fsm 10jxsm ab channel makemehack buspirate jtag connections openocd https research kudelskisecurity com 2014 05 01 jtag debugging made easy with bus pirate and openocd text the 20bus 20pirate 20is 20an protocols 20like 20i c2 b2c 20and 20spi extracting firmware from external memory via jtag https www youtube com watch v iadnbujavks ab channel joegrand analyzing jtag https nse 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com shodan pentesting guide car hacking practical guide 101 https medium com yogeshojha car hacking 101 practical guide to exploiting can bus using instrument cluster simulator part i cd88d3eb4a53 owasp firmware security testing methodology https scriptingxss gitbook io firmware security testing methodology awesome bluetooth security https github com engn33r awesome bluetooth security fuzzing things owasp fuzzing info https owasp org www community fuzzing fuzzing ics protocols https 1modm github io fuzzing ics protocols html fuzzowski the network protocol fuzzer that we will want to use https hakin9 org fuzzowski the network protocol fuzzer that we will want to use fuzz testing of application reliability https pages cs wisc edu bart fuzz firm afl high throughput greybox fuzzing of iot firmware via augmented process emulation https www usenix org conference usenixsecurity19 presentation zheng snipuzz black box fuzzing of iot firmware via message snippet inference https arxiv org pdf 2105 05445 pdf fuzzing iot binaries part1 https blog attify com fuzzing iot devices part 1 part2 https blog attify com fuzzing iot binaries with afl part ii modern vulnerability research techniques on embedded systems https breaking bits gitbook io breaking bits vulnerability discovery reverse engineering modern approaches toward embedded research fuzzingpaper https github com wcventure fuzzingpaper tree master paper exercises to learn how to fuzz with american fuzzy lop https github com mykter afl training broadcom and cypress firmware emulation for fuzzing and further full stack debugging https github com seemoo lab frankenstein bluetooth experimentation framework for broadcom and cypress chips https github com seemoo lab internalblue flipperzero custom firmwares flipper zero unleashed firmware https github com darkflippers unleashed firmware roguemaster flipper zero firmware https github com roguemaster flipperzero firmware wplugins interesting research cve 2022 40363 exploiting 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com asia 17 arsenal html damn vulnerable ss7 network voip https www vulnhub com entry hacklab vulnvoip 40 hardware hacking 101 https github com rdomanski hardware hacking rhme 2015 https github com riscure rhme 2015 rhme 2016 https github com riscure rhme 2016 rhme 2017 https github com riscure rhme 2017 ctf for iot and embeddded awesome hardware iot firmware arm and reverse engineering ctfs and platforms hardware ctfs ble ctf https github com hackgnar ble ctf a framework focused on bluetooth low energy security rhme 2016 https github com riscure rhme 2016 riscure s hardware security competition for 2016 rhme 2017 https github com riscure rhme 2017 riscure s hardware security competition for 2017 iot ctfs iotgoat https github com scriptingxss iotgoat deliberately insecure firmware based on openwrt for iot security training iot village ctf https www iotvillage org a capture the flag event specifically focused on iot security iotsec ctf https ctf iotsec io offers iot related challenges for continuous learning firmware ctfs damn vulnerable arm router https blog exploitlab net 2018 01 dvar damn vulnerable arm router html a deliberately vulnerable arm router for exploitation practice firmware security training ctf https github com 0x6d696368 routeranalysistoolkit firmware analysis tools and challenges by router analysis toolkit arm ctfs arm x ctf https github com therealsaumil armx a set of challenges focused on arm exploitation azeria labs arm challenges https azeria labs com writing arm assembly part 1 offers arm assembly challenges and tutorials reverse engineering ctfs microcorruption https www microcorruption com embedded security ctf focusing on lock systems pwnable kr https pwnable kr offers various reverse engineering challenges platforms for continuous learning hack the box https www hackthebox eu platform offering a range of challenges including hardware and reverse engineering root me https www root me org platform with various types of challenges including hardware and reverse engineering ctftime https ctftime org lists various ctfs including those in hardware iot and firmware follow the people jilles https twitter com jilles com joe fitz https twitter com securelyfitz aseem jakhar https twitter com aseemjakhar cybergibbons https twitter com cybergibbons jasper https twitter com jzvw dave jones https twitter com eevblog bunnie https twitter com bunniestudios ilya shaposhnikov https twitter com drakylar mark c https twitter com largecardinal a a ron guzman https twitter com scriptingxss yashin mehaboobe https twitter com yashinmehaboobe arun magesh https www linkedin com in marunmagesh mr iot https twitter com v33riot qkaiser https twitter com qkaiser 9lyph https twitter com 9lyph | iot-security iot-device hardware-hacking firmware-pentesting linux radio pentesting-guides embedded-devices hardware firmware iot | server |
MobileCyberSecurity | mobilecybersecurity it is a development tutorial that provides security defenses for mobile applications | front_end |
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verde | img src https github com fatiando verde raw main doc static readme banner png alt verde h2 align center processing and gridding spatial data machine learning style h2 p align center a href https www fatiando org verde strong documentation strong latest a a href https www fatiando org verde dev strong documentation strong main branch a a href https github com fatiando verde blob main contributing md strong contributing strong a a href https www fatiando org contact strong contact strong a p p align center part of the a href https www fatiando org strong fatiando a terra strong a project p p align center a href https pypi python org pypi verde img src http img shields io pypi v verde svg style flat square alt latest version on pypi a a href https github com conda forge verde feedstock img src https img shields io conda vn conda forge verde svg style flat square alt latest version on conda forge a a href https codecov io gh fatiando verde img src https img shields io codecov c github fatiando verde main svg style flat square alt test coverage status a a href https pypi python org pypi verde img src https img shields io pypi pyversions verde svg style flat square alt compatible python versions a a href https doi org 10 21105 joss 00957 img src https img shields io badge doi 10 21105 2fjoss 00957 blue style flat square alt doi used to cite this software a p about verde is a python library for processing spatial data topography point clouds bathymetry geophysics surveys etc and interpolating them on a 2d surface i e gridding with a hint of machine learning our core interpolation methods are inspired by machine learning as such verde implements an interface that is similar to the popular scikit learn https scikit learn org library we also provide other analysis methods that are often used in combination with gridding like trend removal blocked windowed operations cross validation and more project goals provide a machine learning inspired interface for gridding spatial data integration with the scipy stack numpy pandas scikit learn and xarray include common processing and data preparation tasks like blocked means and 2d trends support for gridding scalar and vector data like wind speed or gps velocities support for both cartesian and geographic coordinates project status verde is stable and ready for use this means that we are careful about introducing backwards incompatible changes and will provide ample warning when doing so upgrading minor versions of verde should not require making changes to your code the first major release of verde was focused on meeting most of these initial goals and establishing the look and feel of the library later releases will focus on expanding the range of gridders available optimizing the code and improving algorithms so that larger than memory datasets can also be supported getting involved contact us find out more about how to reach us at fatiando org contact https www fatiando org contact contributing to project development please read our contributing guide https github com fatiando verde blob main contributing md to see how you can help and give feedback code of conduct this project is released with a code of conduct https github com fatiando community blob main code of conduct md by participating in this project you agree to abide by its terms imposter syndrome disclaimer we want your help no really there may be a little voice inside your head that is telling you that you re not ready that you aren t skilled enough to contribute we assure you that the little voice in your head is wrong most importantly there are many valuable ways to contribute besides writing code this disclaimer was adapted from the metpy project https github com unidata metpy license this is free software you can redistribute it and or modify it under the terms of the bsd 3 clause license a copy of this license is provided in license txt https github com fatiando verde blob main license txt | geophysics earth-science geospatial python scipy interpolation python3 scipy-stack fatiando-a-terra geoscience machine-learning | ai |
base-design-docs | development first clone and install dependencies bash git clone https github com uber base design docs git cd base design docs yarn then create a env local file like so bash the base figma file for the org figma file key xyz the figma project to build pages from figma project id xyz a figma api auth token with access to the above file figma auth token xyz you can find the first two variables by looking at the url for a project https www figma com files figma file key project figma project id some cool project you can create an auth token on your figma user settings page note this auth token needs viewing access to the project and each file in it once you have your environment set up run the development server bash yarn dev open http localhost 3000 http localhost 3000 with your browser to see the result pages auto update as you edit files how this works this project is built with next js and vercel we use getstaticpaths and getstaticprops to render a webpage for each top level frame in each file in our figma project so what is a top level frame first recall that every figma file has the following structure project file page frame every project can have multuple files which can have multiple pages which in turn can have multiple frames top level frames are the direct child frames of a page we render a webpage for every one of these top level frames take the following figma file structure docs setup getting started living styleguides color light tokens dark tokens typography uber move uber move mono grid columns rows this results in the following webpages being rendered getting started living styleguides light tokens dark tokens uber move uber move mono columns rows in our navigation these pages will be grouped by their parent pages something like this setup getting started living styleguides we apply this to each file in the figma project note the file structure itself only impacts the rendered website by influencing the order of pages we have a step that essentially joins each page into one list before processing top level frames there are a couple more conventions to keep in mind we only use pages that start with a capital letter we only use frames that start with a captial letter and are visible so given any arbitrary project provided at build time as figma project id so long as the files in the project follow the above conventions we can build a website building the site at build time we ask figma for all of the files in our project figma project key this is saved in data project json with that information we can query each file in the project we ask for the first two levels of each file s node tree this will give us the pages top level frames for the file with the pages frames for each file we can build a site map which gets saved in data sitemap json this first part of the build is initiated by getstaticpaths which next js calls to build a static collection of pages the second part of the build is calling getstaticprops for each of the pages we ve returned in getstaticpaths during the getstaticprops call we ask the figma api for a png of the relevant frame we receive a link to the generated image from the api download it to the public directory and then embed the image on the page with next image prior art we ve done some prototyping https v9 80 0 baseweb design guidelines where we render the frame as html css through react the main benefit is that the pages are indexable which is useful for seo and cross page search it would also be interesting if the webpages could be made responsive there have been a lot of issues with the html css approach though to start it isn t clear if you can do responsive pages without an excessive amount of convention in your figma file we want our designers to focus on presenting useful documentation not fussing over the rules for adding it furthermore for a large file the api can take quite a long time to query all of the json necessary to render the frame accurately at a certain point a cms is probably better suited for delivering structured data that can be rendered nicely across multiple mediums so with all of this considered for now we use images pngs | design-system documentation | os |
aspnet-starter-kit-2.0 | asp net core starter kit 2 0 this project is a mix of asp net core starter kit https github com kriasoft aspnet starter kit and microsoft asp net core react redux template https docs microsoft com en us aspnet core spa react tabs visual studio why i like original asp net core starter kit because it embraces node js https nodejs org and allows you to run on top of kestrel web server but unfortunatelly it wasn t updated to support net core sdk 2 1 https www microsoft com net download i like microsoft asp net core react redux template but i don t like how it integrates with node js https nodejs org using node commands from inside csproj file nbsp nbsp it is less flexible than npm scripts and also is less familiar to front end developers nbsp nbsp node js commands are embedded inside the csproj project file which is great if you are just using visual studio to compile net core but is quite annoying if you like to work with npm scripts and cli this projects takes the best parts from asp net core starter kit and asp net core react redux template to create a new template levaraging the full power of node js and net core 2 1 sdk with kestrel as a web server typescript https www typescriptlang org was also added to the mix features nbsp nbsp component based front end development via webpack https webpack github io and react https facebook github io react see webpack config js webpack config js nbsp nbsp static type checking with typescript https www typescriptlang org nbsp nbsp application state management via redux http redux js org nbsp nbsp universal cross stack routing and navigation history https github com reactjstraining history see client routes tsx client routes tsx nbsp nbsp hot module replacement hmr https webpack github io docs hot module replacement html w react hot loader http gaearon github io react hot loader nbsp nbsp lightweight build automation with plain javascript see run js run js nbsp nbsp cross device testing with browsersync https browsersync io styling the project is framework agnostic so you can easily add your preferred styling framework nbsp nbsp bootstrap 4 components https reactstrap github io nbsp nbsp ant design components https ant design nbsp nbsp material ui components https material ui com or extend the loaders in webpack config js to compile your own sass https github com webpack contrib sass loader or less https github com webpack contrib less loader styles another option instead of css could be to use css in js your can see a list of frameworks at michelebertoli s great repo here https github com michelebertoli css in js emotion https github com emotion js emotion has worked in a few test projects but feedback on a good library that plays well with typescript are appreciated prerequisites os x windows or linux node js https nodejs org v6 or newer net core https www microsoft com net core and net core sdk https www microsoft com net core visual studio code https code visualstudio com or your prefered ide getting started step 1 clone the latest version of asp net core starter kit 2 0 on your local machine by running shell git clone o aspnet starter kit b master single branch https github com luteceo aspnet starter kit 2 0 myapp cd myapp step 2 install project dependencies listed in project json server project json and package json package json files shell npm install install both node js and net core dependencies or using yarn shell yarn install install both node js and net core dependencies step 3 finally launch your web app shell node run start compile and lanch the app same as running npm start the app should become available at http localhost 5000 http localhost 5000 see run js run js for other available commands such as node run build etc you can also run your app in a release production mode by running node run release or without hot module replacement hmr by running node run no hmr how to deploy todo how to update you can always fetch and merge the latest changes from this upstream repo back into your project by running shell git checkout master git fetch aspnet starter kit 2 0 git merge aspnet starter kit 2 0 master how to contribute anyone and everyone is welcome to contribute the best way to start is by checking our open issues https github com luteceo aspnet starter kit 2 0 issues submit a new issues https github com luteceo aspnet starter kit 2 0 issues new labels bug or feature request https github com luteceo aspnet starter kit 2 0 issues new labels enhancement participate in discussions upvote or downvote the issues you like or dislike send pull requests get in touch luteceo fr https twitter com luteceo fr tjaskula https twitter com tjaskula license copyright 2018 present luteceo http luteceo com this source code is licensed under the mit made by tomasz jaskula tjaskula https twitter com tjaskula | visual-studio-code typescript react-redux asp-net-core kestrel nodejs hmr-support csharp fsharp | front_end |
data-validation | see www tensorflow org tfx data validation tensorflow data validation python https img shields io badge python 7c3 9 7c3 10 7c3 11 blue https github com tensorflow data validation pypi https badge fury io py tensorflow data validation svg https badge fury io py tensorflow data validation documentation https img shields io badge api reference blue svg https www tensorflow org tfx data validation api docs python tfdv tensorflow data validation tfdv is a library for exploring and validating machine learning data it is designed to be highly scalable and to work well with tensorflow and tensorflow extended tfx https www tensorflow org tfx tf data validation includes scalable calculation of summary statistics of training and test data integration with a viewer for data distributions and statistics as well as faceted comparison of pairs of features facets https github com pair code facets automated data schema https github com tensorflow metadata blob master tensorflow metadata proto v0 schema proto generation to describe expectations about data like required values ranges and vocabularies a schema viewer to help you inspect the schema anomaly detection to identify anomalies https github com tensorflow data validation blob master g3doc anomalies md such as missing features out of range values or wrong feature types to name a few an anomalies viewer so that you can see what features have anomalies and learn more in order to correct them for instructions on using tfdv see the get started guide https github com tensorflow data validation blob master g3doc get started md and try out the example notebook https colab research google com github tensorflow tfx blob master docs tutorials data validation tfdv basic ipynb some of the techniques implemented in tfdv are described in a technical paper published in sysml 19 https mlsys org conferences 2019 doc 2019 167 pdf installing from pypi the recommended way to install tfdv is using the pypi package https pypi org project tensorflow data validation bash pip install tensorflow data validation nightly packages tfdv also hosts nightly packages on google cloud to install the latest nightly package please use the following command bash export tfx dependency selector nightly pip install extra index url https pypi nightly tensorflow org simple tensorflow data validation this will install the nightly packages for the major dependencies of tfdv such as tfx basic shared libraries tfx bsl and tensorflow metadata tfmd sometimes tfdv uses those dependencies most recent changes which are not yet released because of this it is safer to use nightly versions of those dependent libraries when using nightly tfdv export the tfx dependency selector environment variable to do so note these nightly packages are unstable and breakages are likely to happen the fix could often take a week or more depending on the complexity involved build with docker this is the recommended way to build tfdv under linux and is continuously tested at google 1 install docker please first install docker and docker compose by following the directions docker https docs docker com install docker compose https docs docker com compose install 2 clone the tfdv repository shell git clone https github com tensorflow data validation cd data validation note that these instructions will install the latest master branch of tensorflow data validation if you want to install a specific branch such as a release branch pass b branchname to the git clone command 3 build the pip package then run the following at the project root bash sudo docker compose build manylinux2010 sudo docker compose run e python version python version manylinux2010 where python version is one of 39 310 311 a wheel will be produced under dist 4 install the pip package shell pip install dist whl build from source 1 prerequisites to compile and use tfdv you need to set up some prerequisites install numpy if numpy is not installed on your system install it now by following these directions https www scipy org scipylib download html install bazel if bazel is not installed on your system install it now by following these directions https bazel build versions master docs install html 2 clone the tfdv repository shell git clone https github com tensorflow data validation cd data validation note that these instructions will install the latest master branch of tensorflow data validation if you want to install a specific branch such as a release branch pass b branchname to the git clone command 3 build the pip package tfdv wheel is python version dependent to build the pip package that works for a specific python version use that python binary to run shell python setup py bdist wheel you can find the generated whl file in the dist subdirectory 4 install the pip package shell pip install dist whl supported platforms tfdv is tested on the following 64 bit operating systems macos 12 5 monterey or later ubuntu 20 04 or later windows 7 or later notable dependencies tensorflow is required apache beam https beam apache org is required it s the way that efficient distributed computation is supported by default apache beam runs in local mode but can also run in distributed mode using google cloud dataflow https cloud google com dataflow and other apache beam runners https beam apache org documentation runners capability matrix apache arrow https arrow apache org is also required tfdv uses arrow to represent data internally in order to make use of vectorized numpy functions compatible versions the following table shows the package versions that are compatible with each other this is determined by our testing framework but other untested combinations may also work tensorflow data validation apache beam gcp pyarrow tensorflow tensorflow metadata tensorflow transform tfx bsl github master https github com tensorflow data validation blob master release md 2 47 0 10 0 0 nightly 2 x 1 14 0 n a 1 14 0 1 14 0 https github com tensorflow data validation blob v1 14 0 release md 2 47 0 10 0 0 2 13 1 14 0 n a 1 14 0 1 13 0 https github com tensorflow data validation blob v1 13 0 release md 2 40 0 6 0 0 2 12 1 13 1 n a 1 13 0 1 12 0 https github com tensorflow data validation blob v1 12 0 release md 2 40 0 6 0 0 2 11 1 12 0 n a 1 12 0 1 11 0 https github com tensorflow data validation blob v1 11 0 release md 2 40 0 6 0 0 1 15 2 10 1 11 0 n a 1 11 0 1 10 0 https github com tensorflow data validation blob v1 10 0 release md 2 40 0 6 0 0 1 15 2 9 1 10 0 n a 1 10 1 1 9 0 https github com tensorflow data validation blob v1 9 0 release md 2 38 0 5 0 0 1 15 2 9 1 9 0 n a 1 9 0 1 8 0 https github com tensorflow data validation blob v1 8 0 release md 2 38 0 5 0 0 1 15 2 8 1 8 0 n a 1 8 0 1 7 0 https github com tensorflow data validation blob v1 7 0 release md 2 36 0 5 0 0 1 15 2 8 1 7 0 n a 1 7 0 1 6 0 https github com tensorflow data validation blob v1 6 0 release md 2 35 0 5 0 0 1 15 2 7 1 6 0 n a 1 6 0 1 5 0 https github com tensorflow data validation blob v1 5 0 release md 2 34 0 5 0 0 1 15 2 7 1 5 0 n a 1 5 0 1 4 0 https github com tensorflow data validation blob v1 4 0 release md 2 32 0 4 0 1 1 15 2 6 1 4 0 n a 1 4 0 1 3 0 https github com tensorflow data validation blob v1 3 0 release md 2 32 0 2 0 0 1 15 2 6 1 2 0 n a 1 3 0 1 2 0 https github com tensorflow data validation blob v1 2 0 release md 2 31 0 2 0 0 1 15 2 5 1 2 0 n a 1 2 0 1 1 1 https github com tensorflow data validation blob v1 1 1 release md 2 29 0 2 0 0 1 15 2 5 1 1 0 n a 1 1 1 1 1 0 https github com tensorflow data validation blob v1 1 0 release md 2 29 0 2 0 0 1 15 2 5 1 1 0 n a 1 1 0 1 0 0 https github com tensorflow data validation blob v1 0 0 release md 2 29 0 2 0 0 1 15 2 5 1 0 0 n a 1 0 0 0 30 0 https github com tensorflow data validation blob v0 30 0 release md 2 28 0 2 0 0 1 15 2 4 0 30 0 n a 0 30 0 0 29 0 https github com tensorflow data validation blob v0 29 0 release md 2 28 0 2 0 0 1 15 2 4 0 29 0 n a 0 29 0 0 28 0 https github com tensorflow data validation blob v0 28 0 release md 2 28 0 2 0 0 1 15 2 4 0 28 0 n a 0 28 1 0 27 0 https github com tensorflow data validation blob v0 27 0 release md 2 27 0 2 0 0 1 15 2 4 0 27 0 n a 0 27 0 0 26 1 https github com tensorflow data validation blob v0 26 1 release md 2 28 0 0 17 0 1 15 2 3 0 26 0 0 26 0 0 26 0 0 26 0 https github com tensorflow data validation blob v0 26 0 release md 2 25 0 0 17 0 1 15 2 3 0 26 0 0 26 0 0 26 0 0 25 0 https github com tensorflow data validation blob v0 25 0 release md 2 25 0 0 17 0 1 15 2 3 0 25 0 0 25 0 0 25 0 0 24 1 https github com tensorflow data validation blob v0 24 1 release md 2 24 0 0 17 0 1 15 2 3 0 24 0 0 24 1 0 24 1 0 24 0 https github com tensorflow data validation blob v0 24 0 release md 2 23 0 0 17 0 1 15 2 3 0 24 0 0 24 0 0 24 0 0 23 1 https github com tensorflow data validation blob v0 23 1 release md 2 24 0 0 17 0 1 15 2 3 0 23 0 0 23 0 0 23 0 0 23 0 https github com tensorflow data validation blob v0 23 0 release md 2 23 0 0 17 0 1 15 2 3 0 23 0 0 23 0 0 23 0 0 22 2 https github com tensorflow data validation blob v0 22 2 release md 2 20 0 0 16 0 1 15 2 2 0 22 0 0 22 0 0 22 1 0 22 1 https github com tensorflow data validation blob v0 22 1 release md 2 20 0 0 16 0 1 15 2 2 0 22 0 0 22 0 0 22 1 0 22 0 https github com tensorflow data validation blob v0 22 0 release md 2 20 0 0 16 0 1 15 2 2 0 22 0 0 22 0 0 22 0 0 21 5 https github com tensorflow data validation blob v0 21 5 release md 2 17 0 0 15 0 1 15 2 1 0 21 0 0 21 1 0 21 3 0 21 4 https github com tensorflow data validation blob v0 21 4 release md 2 17 0 0 15 0 1 15 2 1 0 21 0 0 21 1 0 21 3 0 21 2 https github com tensorflow data validation blob v0 21 2 release md 2 17 0 0 15 0 1 15 2 1 0 21 0 0 21 0 0 21 0 0 21 1 https github com tensorflow data validation blob v0 21 1 release md 2 17 0 0 15 0 1 15 2 1 0 21 0 0 21 0 0 21 0 0 21 0 https github com tensorflow data validation blob v0 21 0 release md 2 17 0 0 15 0 1 15 2 1 0 21 0 0 21 0 0 21 0 0 15 0 https github com tensorflow data validation blob v0 15 0 release md 2 16 0 0 14 0 1 15 2 0 0 15 0 0 15 0 0 15 0 0 14 1 https github com tensorflow data validation blob v0 14 1 release md 2 14 0 0 14 0 1 14 0 14 0 0 14 0 n a 0 14 0 https github com tensorflow data validation blob v0 14 0 release md 2 14 0 0 14 0 1 14 0 14 0 0 14 0 n a 0 13 1 https github com tensorflow data validation blob v0 13 1 release md 2 11 0 n a 1 13 0 12 1 0 13 0 n a 0 13 0 https github com tensorflow data validation blob v0 13 0 release md 2 11 0 n a 1 13 0 12 1 0 13 0 n a 0 12 0 https github com tensorflow data validation blob v0 12 0 release md 2 10 0 n a 1 12 0 12 1 0 12 0 n a 0 11 0 https github com tensorflow data validation blob v0 11 0 release md 2 8 0 n a 1 11 0 9 0 0 11 0 n a 0 9 0 https github com tensorflow data validation blob v0 9 0 release md 2 6 0 n a 1 9 n a n a n a questions please direct any questions about working with tf data validation to stack overflow https stackoverflow com using the tensorflow data validation https stackoverflow com questions tagged tensorflow data validation tag links tensorflow data validation getting started guide https www tensorflow org tfx data validation get started tensorflow data validation notebook https colab research google com github tensorflow tfx blob master docs tutorials data validation tfdv basic ipynb tensorflow data validation api documentation https www tensorflow org tfx data validation api docs python tfdv tensorflow data validation blog post https medium com tensorflow introducing tensorflow data validation data understanding validation and monitoring at scale d38e3952c2f0 tensorflow data validation pypi https pypi org project tensorflow data validation tensorflow data validation paper https mlsys org conferences 2019 doc 2019 167 pdf tensorflow data validation slides https conf slac stanford edu xldb2018 sites xldb2018 conf slac stanford edu files tues 09 45 neoklispolyzotis data 20analysis 20and 20validation 20 1 pdf | ai |
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BookStore | bookstore this repository contains the files concerning the semestral project involving the disciplines of object oriented programming databases and software engineering iii from the systems analysis and development course at fatec zl team members caique vidal freitas https github com caiquevidal daniel caldeira da mota https github com danielmtt gustavo alves brito de almeida https github com guto alves murillo meira de azedino https github com yamiau | server |
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PSoC-6-MCU-RTOS-Based-Design | table of contents introduction introduction navigating the repository navigating the repository required tools required tools code examples list code examples list references references introduction this repository contains examples and demos for psoc 6 mcu family of devices a single chip solution for the emerging iot devices psoc 6 mcu bridges the gap between expensive power hungry application processors and low performance microcontrollers mcus the ultra low power dual core architecture of psoc 6 mcu offers the processing performance needed by iot devices eliminating the tradeoffs between power and performance cypress provides a wealth of data at www cypress com http www cypress com to help you select the right psoc device and effectively integrate it into your design visit our psoc 6 mcu http www cypress com products 32 bit arm cortex m4 psoc 6 webpage to explore more about psoc 6 mcu family of device feel free to explore through the code example source files and let us innovate together navigating the repository this repository contains reference for developing psoc 6 mcu designs using rtos the projects use freertos platform for developing the design you can visit the freertos https www freertos org webpage to learn the concepts of rtos if you are new to developing projects with psoc 6 mcu we recommend you to refer the psoc 6 getting started github https github com cypresssemiconductorco psoc 6 mcu getting started page which can help you familiarize with device features and guides you to create a simple psoc 6 design with psoc creator ide for other block specific design please visit the following github pages 1 analog designs https github com cypresssemiconductorco psoc 6 mcu analog designs 2 digital designs https github com cypresssemiconductorco psoc 6 mcu digital designs 3 ble connectivity designs https github com cypresssemiconductorco psoc 6 mcu ble connectivity designs 4 audio designs https github com cypresssemiconductorco psoc 6 mcu audio designs 5 device related designs https github com cypresssemiconductorco psoc 6 mcu device related design 6 system level designs https github com cypresssemiconductorco psoc 6 mcu system level designs 7 psoc 6 pioneer kit designs https github com cypresssemiconductorco psoc 6 mcu pioneer kits you can use these block level examples to guide you through the development of a system level design using psoc 6 mcu all the code examples in this repository comes with well documented design guidelines to help you understand the design and how to develop it the code examples and their associated documentation are in the code example folder in the repository required tools software integrated development environment ide to use the code examples in this repository please download and install psoc creator http www cypress com products psoc creator hardware psoc 6 mcu development kits cy8ckit 062 ble psoc 6 ble pioneer kit http www cypress com documentation development kitsboards psoc 6 ble pioneer kit cy8ckit 062 wifi bt psoc 6 wifi bt pioneer kit http www cypress com documentation development kitsboards psoc 6 wifi bt pioneer kit note please refer to the code example documentation for selecting the appropriate kit for testing the project code examples list 1 ce218136 psoc 6 mcu e ink display with capsense rtos this code example demonstrates how to create a user interface solution using an e ink display with a capsense slider and buttons e ink displays consume no power for image retention together with psoc 6 mcu s capsense touch sensing an eink display can be used to create user interfaces that have always on functionality 2 ce218137 psoc 6 mcu with ble connectivity ble with proximity rtos this code example demonstrates connectivity between the psoc 6 mcu with bluetooth low energy ble and cysmart ble host emulation tool or mobile device running the cysmart mobile application to transfer capsense proximity sensing information 3 ce218138 psoc 6 mcu with ble connectivity ble thermometer rtos this code example demonstrates interfacing psoc 6 mcu with ble connectivity psoc 6 mcu with a thermistor circuit to read temperature information and sending the temperature data as ble hts indications to a mobile device running cysmart mobile application in addition psoc 6 mcu s real time clock rtc generates alarms interrupts at every minute to show temperature information on the e ink display when ble is not connected 4 ce218139 psoc 6 mcu with ble connectivity eddystone beacon rtos this code example demonstrates the ability of psoc 6 mcu with ble connectivity psoc 6 mcu to function as a ble beacon using the broadcaster role which transmits eddystone fames eddystone is an open source ble beacon profile released by google this project broadcasts core eddystone frame types eddystone uid eddystone url and eddystone tlm 5 ce220331 psoc 6 mcu with ble connectivity ble with user interface rtos this code example demonstrates interfacing psoc 6 mcu with an rgb led with color and intensity control and touch buttons based on mutual capacitance csx and touch slider based on self capacitance csd this code example also shows connectivity between the psoc 6 ble acting as a peripheral and gatt server and a pc running the cysmart ble host emulation tool or a mobile device running the cysmart mobile application acting as a central and gatt client custom ble services are used for capsense touch sensing and led control 6 ce222604 psoc 6 mcu with ble connectivity rtc with current time service rtos this code example demonstrates accurate time keeping with the rtc of psoc 6 mcu with ble connectivity psoc 6 mcu which also generates alarms interrupts at every one minute to show time information on an e ink display in addition a ble cts is used to synchronize time and date with a current time server such as an iphone 7 ce222793 psoc 6 mcu motion sensor rtos this example configures and reads data from a bmi160 motion sensor using psoc 6 mcu the example uses the bmi160 motion sensor to detect and count steps from activities such as walking or running emulating the functionality of a pedometer the motion sensor s accelerometer data is also read and converted to indicate the orientation of the sensor with respect to the ground the step count and orientation information is displayed on the e ink display references 1 psoc 6 mcu psoc 6 bridges the gap between expensive power hungry application processors and low performance microcontrollers mcus the ultra low power psoc 6 mcu architecture offers the processing performance needed by iot devices eliminating the tradeoffs between power and performance the psoc 6 mcu contains a dual core architecture with both cores on a single chip it has an arm cortex m4 for high performance tasks and an arm cortex m0 for low power tasks and with security built in your iot system is protected to learn more on the device please visit our psoc 6 mcu http www cypress com products 32 bit arm cortex m4 psoc 6 webpage 2 psoc 6 mcu learning resource list 2 1 psoc 6 mcu datasheets device datasheets list the features and electrical specifications of psoc 6 families of devices psoc 6 mcu datasheets http www cypress com search all f 5b0 5d meta type 3atechnical documents f 5b1 5d resource meta type 3a575 f 5b2 5d field related products 3a114026 2 2 psoc 6 mcu application notes application notes are available on the cypress website to assist you with designing your psoc application a list of psoc 6 mcu ans http www cypress com psoc6an 2 3 psoc 6 mcu component datasheets psoc creator utilizes components as interfaces to functional hardware hw each component in psoc creator has an associated datasheet that describes the functionality apis and electrical specifications for the hw you can access component datasheets in psoc creator by right clicking a component on the schematic page or by going through the component library listing you can also access component datasheets from the cypress website psoc 6 component datasheets http www cypress com documentation component datasheets 2 4 psoc 6 mcu technical reference manuals trm the trm provides detailed descriptions of the internal architecture of psoc 6 devices psoc 6 mcu trms http www cypress com psoc6trm faq technical support need support for your design and development questions check out the cypress developer community 3 0 https community cypress com welcome interact with technical experts in the embedded design community and receive answers verified by cypress very best applications engineers you ll also have access to robust technical documentation active conversation threads and rich multimedia content you can also use the following support resources if you need quick assistance self help technical support http www cypress com support local sales office locations sales office http www cypress com about us sales offices | freertos psoc psoc6 dual-cpu cypress non-mtb-2-x | os |
spaCy | a href https explosion ai img src https explosion ai assets img logo svg width 125 height 125 align right a spacy industrial strength nlp spacy is a library for advanced natural language processing in python and cython it s built on the very latest research and was designed from day one to be used in real products spacy comes with pretrained pipelines https spacy io models and currently supports tokenization and training for 70 languages it features state of the art speed and neural network models for tagging parsing named entity recognition text classification and more multi task learning with pretrained transformers like bert as well as a production ready training system https spacy io usage training and easy model packaging deployment and workflow management spacy is commercial open source software released under the mit license https github com explosion spacy blob master license version 3 7 out now check out the release notes here https github com explosion spacy releases tests https github com explosion spacy actions workflows tests yml badge svg https github com explosion spacy actions workflows tests yml current release version https img shields io github release explosion spacy svg style flat square logo github https github com explosion spacy releases pypi version https img shields io pypi v spacy svg style flat square logo pypi logocolor white https pypi org project spacy conda version https img shields io conda vn conda forge spacy svg style flat square logo conda forge logocolor white https anaconda org conda forge spacy python wheels https img shields io badge wheels e2 9c 93 4c1 svg longcache true style flat square logo python logocolor white https github com explosion wheelwright releases code style black https img shields io badge code 20style black 000000 svg style flat square https github com ambv black br pypi downloads https static pepy tech personalized badge spacy period total units international system left color grey right color orange left text pip 20downloads https pypi org project spacy conda downloads https img shields io conda dn conda forge spacy label conda 20downloads https anaconda org conda forge spacy spacy on twitter https img shields io twitter follow spacy io svg style social label follow https twitter com spacy io documentation documentation spacy 101 new to spacy here s everything you need to know usage guides how to use spacy and its features new in v3 0 new features backwards incompatibilities and migration guide project templates end to end workflows you can clone modify and run api reference the detailed reference for spacy s api models download trained pipelines for spacy universe plugins extensions demos and books from the spacy ecosystem spacy vs code extension additional tooling and features for working with spacy s config files online course learn spacy in this free and interactive online course videos our youtube channel with video tutorials talks and more changelog changes and version history contribute how to contribute to the spacy project and code base a href https explosion ai spacy tailored pipelines img src https user images githubusercontent com 13643239 152853098 1c761611 ccb0 4ec6 9066 b234552831fe png width 125 alt spacy tailored pipelines a get a custom spacy pipeline tailor made for your nlp problem by spacy s core developers streamlined production ready predictable and maintainable start by completing our 5 minute questionnaire to tell us what you need and we ll be in touch learn more rarr https explosion ai spacy tailored pipelines a href https explosion ai spacy tailored analysis img src https user images githubusercontent com 1019791 206151300 b00cd189 e503 4797 aa1e 1bb6344062c5 png width 125 alt spacy tailored pipelines a bespoke advice for problem solving strategy and analysis for applied nlp projects services include data strategy code reviews pipeline design and annotation coaching curious fill in our 5 minute questionnaire to tell us what you need and we ll be in touch learn more rarr https explosion ai spacy tailored analysis spacy 101 https spacy io usage spacy 101 new in v3 0 https spacy io usage v3 usage guides https spacy io usage api reference https spacy io api models https spacy io models universe https spacy io universe spacy vs code extension https github com explosion spacy vscode videos https www youtube com c explosionai online course https course spacy io project templates https github com explosion projects changelog https spacy io usage changelog contribute https github com explosion spacy blob master contributing md where to ask questions the spacy project is maintained by the spacy team https explosion ai about please understand that we won t be able to provide individual support via email we also believe that help is much more valuable if it s shared publicly so that more people can benefit from it type platforms bug reports github issue tracker feature requests ideas github discussions usage questions github discussions stack overflow general discussion github discussions github issue tracker https github com explosion spacy issues github discussions https github com explosion spacy discussions stack overflow https stackoverflow com questions tagged spacy features support for 70 languages trained pipelines for different languages and tasks multi task learning with pretrained transformers like bert support for pretrained word vectors and embeddings state of the art speed production ready training system linguistically motivated tokenization components for named entity recognition part of speech tagging dependency parsing sentence segmentation text classification lemmatization morphological analysis entity linking and more easily extensible with custom components and attributes support for custom models in pytorch tensorflow and other frameworks built in visualizers for syntax and ner easy model packaging deployment and workflow management robust rigorously evaluated accuracy for more details see the facts figures and benchmarks https spacy io usage facts figures install spacy for detailed installation instructions see the documentation https spacy io usage operating system macos os x linux windows cygwin mingw visual studio python version python 3 7 only 64 bit package managers pip conda via conda forge pip https pypi org project spacy conda https anaconda org conda forge spacy pip using pip spacy releases are available as source packages and binary wheels before you install spacy and its dependencies make sure that your pip setuptools and wheel are up to date bash pip install u pip setuptools wheel pip install spacy to install additional data tables for lemmatization and normalization you can run pip install spacy lookups or install spacy lookups data https github com explosion spacy lookups data separately the lookups package is needed to create blank models with lemmatization data and to lemmatize in languages that don t yet come with pretrained models and aren t powered by third party libraries when using pip it is generally recommended to install packages in a virtual environment to avoid modifying system state bash python m venv env source env bin activate pip install u pip setuptools wheel pip install spacy conda you can also install spacy from conda via the conda forge channel for the feedstock including the build recipe and configuration check out this repository https github com conda forge spacy feedstock bash conda install c conda forge spacy updating spacy some updates to spacy may require downloading new statistical models if you re running spacy v2 0 or higher you can use the validate command to check if your installed models are compatible and if not print details on how to update them bash pip install u spacy python m spacy validate if you ve trained your own models keep in mind that your training and runtime inputs must match after updating spacy we recommend retraining your models with the new version for details on upgrading from spacy 2 x to spacy 3 x see the migration guide https spacy io usage v3 migrating download model packages trained pipelines for spacy can be installed as python packages this means that they re a component of your application just like any other module models can be installed using spacy s download https spacy io api cli download command or manually by pointing pip to a path or url documentation available pipelines detailed pipeline descriptions accuracy figures and benchmarks models documentation detailed usage and installation instructions training how to train your own pipelines on your data available pipelines https spacy io models models documentation https spacy io usage models training https spacy io usage training bash download best matching version of specific model for your spacy installation python m spacy download en core web sm pip install tar gz archive or whl from path or url pip install users you en core web sm 3 0 0 tar gz pip install users you en core web sm 3 0 0 py3 none any whl pip install https github com explosion spacy models releases download en core web sm 3 0 0 en core web sm 3 0 0 tar gz loading and using models to load a model use spacy load https spacy io api top level spacy load with the model name or a path to the model data directory python import spacy nlp spacy load en core web sm doc nlp this is a sentence you can also import a model directly via its full name and then call its load method with no arguments python import spacy import en core web sm nlp en core web sm load doc nlp this is a sentence for more info and examples check out the models documentation https spacy io docs usage models compile from source the other way to install spacy is to clone its github repository https github com explosion spacy and build it from source that is the common way if you want to make changes to the code base you ll need to make sure that you have a development environment consisting of a python distribution including header files a compiler pip https pip pypa io en latest installing virtualenv https virtualenv pypa io en latest and git https git scm com installed the compiler part is the trickiest how to do that depends on your system platform ubuntu install system level dependencies via apt get sudo apt get install build essential python dev git mac install a recent version of xcode https developer apple com xcode including the so called command line tools macos and os x ship with python and git preinstalled windows install a version of the visual c build tools https visualstudio microsoft com visual cpp build tools or visual studio express https visualstudio microsoft com vs express that matches the version that was used to compile your python interpreter for more details and instructions see the documentation on compiling spacy from source https spacy io usage source and the quickstart widget https spacy io usage section quickstart to get the right commands for your platform and python version bash git clone https github com explosion spacy cd spacy python m venv env source env bin activate make sure you are using the latest pip python m pip install u pip setuptools wheel pip install r requirements txt pip install no build isolation editable to install with extras bash pip install no build isolation editable lookups cuda102 run tests spacy comes with an extensive test suite spacy tests in order to run the tests you ll usually want to clone the repository and build spacy from source this will also install the required development dependencies and test utilities defined in the requirements txt requirements txt alternatively you can run pytest on the tests from within the installed spacy package don t forget to also install the test utilities via spacy s requirements txt requirements txt bash pip install r requirements txt python m pytest pyargs spacy | natural-language-processing data-science machine-learning python cython nlp artificial-intelligence ai spacy nlp-library neural-network neural-networks deep-learning named-entity-recognition entity-linking text-classification tokenization | ai |
Avalonia.HtmlRenderer | avalonia htmlrenderer avalonia port of the https github com arthurhub html renderer project fully managed implementation of html engine note this library doesn t replace webview webbrowser controls as its capabilities are limited many modern html features are not supported for samples see demo project nuget https www nuget org packages avalonia htmlrenderer 11 0 0 xml packagereference include avalonia htmlrenderer version 11 0 0 https user images githubusercontent com 3163374 220894702 ccac91f5 a027 4bae 8dcb cba942fe32fe mp4 | front_end |
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skeletonic | alt text logo logo images skeletonic banner min svg skeletonic banner introducing skeletonic css https skeletonic io skeletonic css https github com sebastienrousseau skeletonic a lightweight intuitive accessible and ultra responsive cascading style sheets framework to streamline your digital and mobile web development needs download skeletonic css v1 2 1 https github com sebastienrousseau skeletonic archive v1 2 1 zip npm https nodei co npm skeletonic png https nodei co npm skeletonic npm version https badge fury io js skeletonic svg https badge fury io js skeletonic build status https travis ci org sebastienrousseau skeletonic svg branch master https travis ci org sebastienrousseau skeletonic packagist https img shields io badge license mit blue svg https skeletonic github io license fossa status https app fossa io api projects git 2bgithub com 2freedia 2fskeletonic svg type shield https app fossa io projects git 2bgithub com 2freedia 2fskeletonic ref badge shield table of contents introducing skeletonic css introducing skeletonic css table of contents table of contents getting started getting started local setup local setup what s included whats included extend extend css responsive grid view css responsive grid view css colours css colours css animations css animations alternate cdn locations alternate cdn locations versioning versioning built with built with contributing contributing code of conduct code of conduct our values our values history history license license acknowledgements acknowledgements getting started skeletonic is constantly in development try it out now local setup several quick start options are available download the latest release https github com sebastienrousseau skeletonic archive v1 2 1 zip install with npm https www npmjs com package skeletonic to get the pre built css and sourcemaps this is recommended when using skeletonic for a typical web project npm install skeletonic install with yarn https yarnpkg com en package skeletonic to get the pre built css and sourcemaps this is recommended when using skeletonic for a typical web project yarn add skeletonic clone the main repository to get all source files including build scripts git clone https github com sebastienrousseau skeletonic git what s included within the download you ll find all the source files compiled and minified css bundles as well as the css sourcemaps https developers google com web tools chrome devtools javascript source maps grouped into the dist folder you ll see something like this skeletonic 1 2 1 skeletonic animations css skeletonic animations css map skeletonic animations min css skeletonic colours css skeletonic colours css map skeletonic colours min css skeletonic fonts css skeletonic fonts css map skeletonic fonts min css skeletonic pattern css skeletonic pattern css map skeletonic pattern min css skeletonic css skeletonic css map skeletonic min css now simply link one of these css in your html document in that case the quickest and easiest way to start using skeletonic is to include a reference to the minified css file link rel stylesheet type text css href skeletonic min css the link consists of just a simple line of html code that you will need to put in the head section of your html document we also provide production ready versions and use unpkg https unpkg com as our preferred cdn to link to the latest production version unpkg https unpkg com is a fast global content delivery network for everything on npm please feel free to grab the latest link rel stylesheet type text css href https unpkg com skeletonic dist skeletonic min css you can also specify a specific version as per below the latest version as of today is 1 2 1 link rel stylesheet type text css href https unpkg com skeletonic 1 2 1 dist skeletonic min css extend we provide a growing library of extensible css module files utilities themes and components ready to use as is to fit your web needs css responsive grid view link rel stylesheet type text css href skeletonic pattern min css css colours link rel stylesheet type text css href skeletonic colours min css css animations link rel stylesheet type text css href skeletonic animations min css alternate cdn locations the following table lists alternate cdn locations where skeletonic is hosted cdn url https combo unpkg https unpkg com https unpkg com skeletonic 1 2 1 dist skeletonic min css yes no jsdelivr https www jsdelivr com https cdn jsdelivr net npm skeletonic dist skeletonic min css yes yes versioning for transparency into our release cycle and in striving to maintain backward compatibility skeletonic is maintained under the semantic versioning https semver org guidelines built with gulp https gulpjs com the streaming build system stylus http stylus lang com expressive robust feature rich css language built for nodejs csscomb http csscomb com css coding style formatter contributing please read carefully through our contributing guidelines https github com sebastienrousseau skeletonic blob master contributing md for further details on the process for submitting pull requests to us code of conduct we are committed to preserving and fostering a diverse welcoming community please read our code of conduct https github com sebastienrousseau skeletonic blob master code of conduct md our values 1 we believe perfection must consider everything 2 we take our passion beyond code into our daily practices 3 we are just obsessed about creating and delivering exceptional solutions history see skeletonic release https github com sebastienrousseau skeletonic releases list license this project is licensed under the mit license see the license https github com sebastienrousseau skeletonic blob master license file for details fossa status https app fossa io api projects git 2bgithub com 2freedia 2fskeletonic svg type large https app fossa io projects git 2bgithub com 2freedia 2fskeletonic ref badge large acknowledgements skeletonic https skeletonic io is beautifully crafted by these people and a bunch of awesome contributors https github com sebastienrousseau skeletonic graphs contributors sebastien rousseau https avatars0 githubusercontent com u 1394998 s 117 https sebastienrousseau co uk sebastien rousseau https github com sebastienrousseau credit also goes to the following source code libraries normalize css http necolas github io normalize css a modern html5 ready alternative to css resets to fix cross browser compatibility issues skeleton http www getskeleton com a dead simple responsive boilerplate wing https kbrsh github io wing a beautiful css framework designed for minimalists | css skeletonic css-framework lightweight responsive responsive-grid responsive-css mobile-web accessibility accessible wai-aria wai accessible-colours css-grid open-source section-508 mit-license accessibility-checker web-development-tools ui-components | front_end |
vision_blender | vision blender github stars https img shields io github stars cartucho vision blender svg style social label stars https github com cartucho vision blender a blender user interface to generate synthetic ground truth data benchmarks for computer vision applications img src https user images githubusercontent com 15831541 94527156 7b944d80 022e 11eb 85bd 0b387fd519fb png width 100 img src https user images githubusercontent com 15831541 94527180 8353f200 022e 11eb 9bf5 5ebd6102bc9f png width 100 visionblender is a synthetic computer vision dataset generator that adds a user interface to blender allowing users to generate monocular stereo video sequences with ground truth maps of depth disparity segmentation masks surface normals optical flow object pose and camera parameters presentation video https user images githubusercontent com 15831541 95021661 388d0c80 066a 11eb 9216 a5deac6372df png https www youtube com watch v lhpogw6ingu tutorial click to watch youtube link https www youtube com watch v lhpogw6ingu installation to install the addon simply go to edit preferences add on tab install an add on then select the file path to vision blender addon ground truth generation py and click install add on finally you have to enable the add on search visionblender and tick the check box img src https media giphy com media 8hi33wfpulqgns0akz giphy gif width 50 you should now be able to find the visionblender ui in the bottom of the output properties img src https media giphy com media yohyefbmecg2zxtt6t giphy gif width 50 how to generate ground truth data 1 select render engine if you want to get ground truth segmentation masks or optical flow you need first to set blender to use the cycles render engine otherwise use eevee it will be faster which is set by default img src https media giphy com media s87yo48jpqittzvnbl giphy gif width 50 how to set up segmentation masks to set up the segmentation masks you need to choose a pass index other than zero 0 for each object object properties relations pass index img src https media giphy com media fll3lqwg1x01efac1e giphy gif width 50 each integer e g pass index 1 represents a class of objects to be segmented how to set up optical flow you will only have optical flow if the camera or the objects are moving during an animation in the following gif i show you an example of how to move an object between frames img src https media giphy com media n77idgospjkxbk5tfd giphy gif width 50 2 set output path set up the output path in output properties output output path this is the path where both your rendered images and ground truth will be saved img src https media giphy com media pkonivp8o8slvsc3nf giphy gif width 50 3 select ground truth maps and render first tick the boxes of what you want to save as ground truth in the visionblender ui then start rendering to start rendering you click render render image or render render animation alternatively you can click f12 for image and ctrl f12 for animation img src https media giphy com media evpnpfjmyzweyaheqg giphy gif width 50 note the ground truth maps are always calculated using meters m as unit of distance how to read the data after generating it you simply have to load the numpy arrays from thr npz files go to vision blender samples https github com cartucho vision blender tree master samples and have a look at the example there paper this work received the best paper award at a miccai 2020 workshop the paper can be found at this link https www tandfonline com doi full 10 1080 21681163 2020 1835546 if you use this tool please consider citing our paper bibtex article cartucho2020visionblender title visionblender a tool to efficiently generate computer vision datasets for robotic surgery author cartucho jo a o and tukra samyakh and li yunpeng and s elson daniel and giannarou stamatia journal computer methods in biomechanics and biomedical engineering imaging visualization pages 1 8 year 2020 publisher taylor francis | ai |
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ml-class | projects binder https mybinder org badge logo svg https mybinder org v2 gh lukas ml class master urlpath 2flab these are specific bite sized projects to learn an aspect of deep learning starting from scratch there is an associated video around 10 minutes long i assume no background in ml but some proficiency in python the projects are in order from beginner to more advanced but feel free to skip around project starter code video build a simple image classifier for apparel projects 1 fashion mnist https github com lukas ml class tree master projects 1 fashion mnist build your first machine learning model https www youtube com watch v cbxj7091owa improve your image classifier projects 2 fashion mnist mlp https github com lukas ml class tree master projects 2 fashion mnist mlp multi layer perceptrons https www youtube com watch v gvkda5hxuze build a convolutional image classifier projects 3 fashion mnist cnn https github com lukas ml class tree master projects 3 fashion mnist cnn convolutional neural networks https www youtube com watch v wzy8ji dueq build a denoising autoencoder projects 4 fashion autoencoder https github com lukas ml class tree master projects 4 fashion autoencoder autoencoders https www youtube com watch v 6mah8lh3pk4 build a text classifier with scikit learn projects 5 sentiment analysis https github com lukas ml class tree master projects 5 sentiment analysis sentiment analysis https www youtube com watch v qoyp8pbtcz0 predict the weather with an rnn projects 6 rnn timeseries https github com lukas ml class tree master projects 6 rnn timeseries recurrent neural networks https www youtube com watch v 8lbgjkhrjoo build a text generator projects 7 text generation https github com lukas ml class tree master projects 7 text generation text generation using lstms and grus https www youtube com watch v 4f69m3krmhw build a sentiment classifier on amazon reviews projects 8 text classification https github com lukas ml class tree master projects 8 text classification text classification using cnns https www youtube com watch v 8yszxtpfro0 hybrid lstm cnns https www youtube com watch v nysy9fn9uac seq2seq models https www youtube com watch v mqugtgd605k transfer learning https www youtube com watch v vbhenebj3jm one shot learning https www youtube com watch v h4mpiwx6fte speech recognition https www youtube com watch v qf4yjchxtcy data augmentation https www youtube com watch v yyqavlkrwuq batch size and learning rate https www youtube com watch v zbvwnovivzk more projects benchmarks if you have done all of the tutorial projects we have a few more that don t have associated lessons yet you can learn by contributing to one of our collaborative benchmarks https www wandb com benchmarks project link japanese handwriting recognition https app wandb ai wandb kmnist benchmark video prediction with cat gifs https app wandb ai wandb catz benchmark drought detection from satellite https app wandb ai wandb droughtwatch benchmark visual game playing https app wandb ai wandb witness benchmark image resolution enhancement https app wandb ai wandb superres benchmark photo colorization https app wandb ai wandb colorizer applied dl benchmark getting started 1 clone this repository 2 get the python libraries run pip install r requirements txt you don t need a fancy computer to run most of the examples but especially to do the later projects you may want to invest in a gpu slides o reilly 9 10 2019 using keras to classify text using lstms https www dropbox com s sdep4yralq4exq3 oreilly 20lstm 20 20sept 2010 pdf dl 0 videos introduction to machine learning https www wandb com classes intro overview examples in my in person classes i typically use a lot of the examples in the directory examples this code is liable to change as i update things reusing the materials please feel free to use these materials for your own classes projects etc if you do that i would love it if you sent me a message and let me know what you re up to windows git install git if you don t have it https git scm com download win anaconda install anaconda https repo continuum io archive anaconda3 4 4 0 windows x86 64 exe try running the following from the command prompt python version you should see something like python 3 6 1 anaconda 4 4 0 64 bit if don t see anaconda in the output search for anaconda prompt from the start menu and enter your command prompt this way it s also best to use a virtual environment to keep your packages silo ed do so with conda create n ml class python 3 6 activate ml class whenever you start a new terminal you will need to call activate ml class clone this github repository git clone https github com lukas ml class git cd ml class libraries pip install wandb conda install c conda forge scikit learn conda install c conda forge tensorflow conda install c conda forge keras linux and mac os x install python you can download python from https www python org downloads there are more detailed instructions for windows installation at https www howtogeek com 197947 how to install python on windows the material should work with python 2 or 3 on windows you need to install the 64 bit version of python 3 5 or 3 6 in order to install tensorflow clone this github repository git clone https github com lukas ml class git cd ml class if you get an error message here most likely you don t have git installed go to https www atlassian com git tutorials install git for instructions on installing git install necessary pip libraries pip install r requirements txt reading material for people who haven t done a lot of programming if you are uncomfortable opening up a terminal i strongly recommend doing a quick tutorial before you take this class setting up your machine can be painful but once you re setup you can get a ton out of the class i recommend getting started ahead of time if you re on windows i recommend checking out http thepythonguru com if you re on a mac check out http www macworld co uk how to mac coding with python on mac 3635912 if you re on linux you re probably already reasonably well setup if you run into trouble the book learn python the hard way has installation steps in great detail https learnpythonthehardway org book ex0 html it also has a refresher on using a terminal in the appendix reading material for people who are comfortable with programming but haven t done a lot of python if you are comfortable opening up a terminal but want a python intro refresher check out https www learnpython org for a really nice introduction to python suggestions for people who have done a lot of programming in python a lot of people like to follow along with ipython or jupyter notebooks and i think that s great it makes data exploration easier i also really appreciate pull requests to make the code clearer if you ve never used pandas or numpy they are great tools and i use them heavily in my work and for this class i assume no knlowedge of pandas and numpy but you may want to do some learning on your own you can get a quick overview of pandas at http pandas pydata org pandas docs stable 10min html there is a great overview of numpy at https docs scipy org doc numpy user quickstart html | machine-learning-tutorials deep-learning-tutorials | ai |
BTEC-Fall-Training | btec fall training training in web development | git github figma css html javascript | front_end |
BlockVotes | blockvotes an e voting system based on blockchain using ring signature img src about logo png width 50 height 50 align center demo video https www youtube com watch v 4c6pqg3q4vc slides blockvotes pdf blockvotes pdf topic e voting systems will be beneficial to all people who are involved in elections for example administrators can improve operation of tasks for elections and voters can vote in an election anytime and anywhere in addition ideal e voting systems have transparency completeness only voters have the right to vote and their votes are correctly counted and verifiability voters can check that their vote is correctly counted and therefore it is better than existing voting system digital voting is the use of electronic devices such as voting machines or an internet browser to cast votes these are sometimes referred to as e voting whenvoting using a machine in a polling station and e voting when using a web browser security of digital voting is always the biggest concern when considering to implement a digitalvoting system with such monumental decisions at stake there can be no doubt about the system s ability to secure data and defend against potential attacks one way the security issues can be potentially solved is through the technology of blockchains blockchain technology originates from the underlying architectural design of the crypto currency bitcoin it is a form of distributed database where records take the form of transactions a block is a collection of these transactions with the use of blockchains a secure and robust system for digital voting can be devised to solve the problem i propose a way to use the ring signature and the blockchain to ensure the features of the e voting the blockchain technology is characterized by decentralization irreversibility distribution of joint accounting asymmetric encryption and data security blockchain the blockchain can be described as an immutable cumulative ledger with consensus protocol working to maintain this ledger of all valid transactions on every node in the network op return op return is a script opcode used to mark a transaction output as invalid since any outputs with op return are provably unspendable op return outputs can be used to burn bitcoins about opreturn png ring signature about ring png 1 a group of entities each have public private key pairs p 1 s 1 p 2 s 2 pn sn 2 a ring signature m si p1 p2 pn 3 anyone can check the validity of a ring signature given m and the public keys involved p 1 pn tools framework about stack png the protocol please see the thesis wu yifan an e voting system based on blockchain and ring signature master university of birmingham 2017 http www dgalindo es mscprojects yifan pdf instruction to run the whole system there are a lot works to do recommended running environment 1 operation system mac os x or linux or windows 2 apache 2 4 27 or nginx 1 12 1 3 mysql 5 7 19 at least 5 4 4 php 7 1 8 at least 7 0 5 php7 0 gmp 6 composer php7 0 gmp php7 0 gmp is a package used for the elliptic curve digital signature algorithm ecdsa if you are using the linux please do the following things to install gmp on php7 on ubuntu run sudo apt get install php7 0 gmp and add the following to php ini extension php gmp so to install gmp on php7 on mac os x run brew tap homebrew dupes brew tap homebrew versions brew tap homebrew homebrew php brew install php71 brew install php71 gmp and add the following to php ini extension php gmp so to nstall gmp on php7 on windows uncomment extension php gmp dll in php ini apache configuration ensure your htaccess and index php files are in the same public accessible directory the htaccess file should contain this code rewriteengine on rewritecond request filename f rewritecond request filename d rewriterule index php qsa l make sure your apache virtual host is configured with the allowoverride option so that the htaccess rewrite rules can be used allowoverride all nginx configuration this is an example nginx virtual host configuration for the domain example com it listens for inbound http connections on port 80 it assumes a php fpm server is running on port 9000 you should update the server name error log access log and root directives with your own values the root directive is the path to your application s public document root directory your slim app s index php front controller file should be in this directory server listen 80 server name example com index index php error log path to example error log access log path to example access log root path to public location try files uri index php is args args location php try files uri 404 fastcgi split path info php include fastcgi params fastcgi param script filename document root fastcgi script name fastcgi param script name fastcgi script name fastcgi index index php fastcgi pass 127 0 0 1 9000 reference https www slimframework com docs start web servers html for the whole project directory public is the entrance of the system if you can not open the page please check it if is configured correctly in the nginx or apache blockvotes configuration to run the system some critical but private information are not uploaded by gitingore file please add the env file into the root of this project directory db driver mysql db host 127 0 0 1 db database blockvotes db username root db password db port 3306 stmp server smtp gmail com stmp port 465 stmp username xxx gmail com stmp password password database the sample sql file is generated and uploaded as the file blockvotes sql https gist github com yfgeek 75c53298d59f335c65a6cc03703ec02e please import it to mysql composer composer is a dependency management tool for php please ensure all dependencies has been installed before running the system php composer phar install screenshot about 1 png about 2 png about 3 png about 4 png about 5 png about 6 png about 7 png about 8 png about 10 png about 11 png about 14 png about 15 png | blockchain ring-signature e-voting bitcoin testnet vote transaction election ledger opreturn | blockchain |
Knowledge-Base | knowledge base https www slowmist com https www slowmist io knowledge base knowledge base fire false top up blockchain ecological security research include bitcoin monero ethereum eos and other top blockchains fire cryptocurrency security solution https github com slowmist cryptocurrency security fire blockchain dark forest selfguard handbook https github com slowmist blockchain dark forest selfguard handbook fire collection of security research in english security research readme md https mp weixin qq com mp appmsgalbum biz mzu4odq3ntm2oa action getalbum album id 1378653641065857025 aml https mp weixin qq com mp appmsgalbum biz mzu4odq3ntm2oa action getalbum album id 1983440310995156993 https mp weixin qq com mp appmsgalbum biz mzu4odq3ntm2oa action getalbum album id 1378673890158936067 papers of slowmist https github com slowmist papers public topic of slowmist hackingtime https github com slowmist hackingtime public ontology triones service node security checklist https github com slowmist ontology triones service node security checklist vechain core nodes security checklist https github com slowmist vechain core nodes security checklist eos bp nodes security checklist https github com slowmist eos bp nodes security checklist eos bp nodes security audit https github com slowmist eos bp nodes security checklist blob master audit md eos smart contract security best practices https github com slowmist eos smart contract security best practices eos eos monkit https eos slowmist io firewall x eos https firewallx io firewall x github https github com firewall x open of slowmist https github com slowmist fire false top up usdt https mp weixin qq com s ctaklne0mokdyufaod4 hw eos https mp weixin qq com s fkinfzlw65lyad4qo 21na xrp https developers ripple com partial payments html https mp weixin qq com s 3cmbe6p 4qcdvla4fna5 a rbf https mp weixin qq com s oyi2jdbaoledg8vdouqbig xmr https mp weixin qq com s kt g bybuumibsgsnyyxla https t zsxq com ynbmfia iost https www slowmist com lang zh products filecoin https www slowmist com lang zh products nem https www slowmist com lang zh products solana https www slowmist com lang zh products https www slowmist com lang zh products terra https www slowmist com lang zh products btc dogcoin ltc https www slowmist com lang zh products some translated blockchain security documents dasp top10 translations dasp top10 chinese pdf solidity translations solidity security comprehensive list of known attack vectors and common anti patterns zh cn md translations exploring the attack surface of blockchain a systematic overview exploring the attack surface of blockchain a systematic overview zh cn md some open security audit reports of slowmist open security audit report open report v2 readme md blockchain security audit report open report v2 blockchain blockchain application security audit report open report v2 blockchain application smart contract security audit report open report v2 smart contract some mind maps of blockchain security dapp attack defense mindmaps dapp attack defense png exchange or wallet attack defense mindmaps exchange wallet attack defense png evil blockchain how to be evil mindmaps evil blockchain png other awesome collections hacked https hacked slowmist io awesome blockchain bug bounty https github com slowmist awesome blockchain bug bounty blockchain security study notes readme md | blockchain security hacking knowledge-base | blockchain |
mach-ecs | a href https machengine org pkg mach ecs picture source media prefers color scheme dark srcset https machengine org assets mach ecs full dark svg img alt mach ecs src https machengine org assets mach ecs full light svg height 150px picture a the mach entity component system written from first principles and designed for deep tooling capabilities features initially a 100 clean room implementation working from first principles later informed by research into how other open source ecs work enable deep tooling to provide tracing editors visualizers profilers etc fast optimal for cpu caches multi threaded leverage comptime for type safety dynamic allow for very flexible runtime capabilities experimental this is an experimental project according to our stability guarantees https machengine org about stability when a project has an experimental warning it means all bets are off you should carefully read the warning to understand why the project is experimental and assume the worst tracking issue https github com hexops mach issues 968 documentation machengine org pkg mach ecs https machengine org pkg mach ecs join the community join the mach community on discord https discord gg xng3nzgcqp to discuss this project ask questions get help etc issues issues are tracked in the main mach repository https github com hexops mach issues q is 3aissue is 3aopen label 3aecs | os |
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Transformers-for-Natural-Language-Processing | transformers for natural language processing this is the code repository for transformers for natural language processing https www packtpub com product transformers for natural language processing 9781800565791 published by packt https www packtpub com utm source github it contains all the supporting project files necessary to work through the book from start to finish paperback 384 pages isbn 13 9781800565791 date of publication january 2021 img src other cover png width 248 https www amazon com transformers natural language processing architectures ebook dp b08s977x8k links amazon https www amazon com transformers natural language processing architectures ebook dp b08s977x8k packt publishing https www packtpub com product transformers for natural language processing 9781800565791 about the book transformers for natural language processing investigates in vast detail the deep learning for machine translations speech to text text to speech language modeling question answering and many more nlp domains in context with the transformers the book takes you through natural language processing with python and examines various eminent models and datasets in the transformer technology created by pioneers such as google facebook microsoft openai hugging face and other contributors the book trains you in three stages the first stage introduces you to transformer architectures starting with the original transformer before moving on to roberta bert and distilbert models you will discover training methods for smaller transformers that can outperform gpt 3 in some cases in the second stage you will apply transformers for natural language understanding nlu and generation finally the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification by the end of this nlp book you will understand transformers from a cognitive science perspective and be proficient in applying pre trained transformer models by tech giants to various datasets things you will learn use the latest pretrained transformer models grasp the workings of the original transformer gpt 2 bert t5 and other transformer models create language understanding python programs using concepts that outperform classical deep learning models use a variety of nlp platforms including hugging face trax and allennlp apply python tensorflow and keras programs to sentiment analysis text summarization speech recognition machine translations and more measure the productivity of key transformers to define their scope potential and limits in production instructions and navigation all of the code is organized into folders that are named chapter wise for example chapter02 the code will look like the following python title activating the gpu main menu runtime change runtime type import tensorflow as tf device name tf test gpu device name if device name device gpu 0 raise systemerror gpu device not found print found gpu at format device name software requirements check this file for the hardware and software requirements technical requirements md other technical requirements md related products python machine learning third edition https www packtpub com product python machine learning third edition 9781789955750 hands on explainable ai xai with python second edition https www packtpub com product hands on explainable ai xai with python 9781800208131 download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781800565791 https packt link free ebook 9781800565791 a p | ai |
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CE_records | week 5 personal records system create a simple personal record management system that allows you to keep track of your personal information such as usernames passwords and email addresses use your knowledge of linux user account management to create a new user account for your personal record management system use linux package management to install any necessary software for the project use your preferred text editor to create a set of files to store your personal records using linux file permissions restrict access to your personal record files use github to store and version your personal record files | cloud |
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ml-cpp | machine learning for the elastic stack https www elastic co what is elasticsearch machine learning the ml cpp repo is a part of machine learning for the elastic stack which is available with either a trial or platinum license for the elastic stack https www elastic co products this repo only contains the c code that implements the core analytics for machine learning code for integrating into elasticsearch and source for its documentation can be found in the main elasticsearch repo https github com elastic elasticsearch elastic license functionality usage in production requires that you have a license key that permits use of machine learning features see license txt license txt for full information getting started to get started with machine learning please have a look at https www elastic co guide en machine learning current ml getting started html full documentation of machine learning can be found at https www elastic co guide en machine learning current index html questions bug reports help we are happy to help and to make sure your questions can be answered by the right people please follow the guidelines below if you have a general question about functionality please use our discuss https discuss elastic co tag elastic stack machine learning forums if you have a support contract please use your dedicated support channel for questions regarding subscriptions please contact https www elastic co contact us for bug reports pull requests and feature requests specifically for machine learning analytics please use this github repository contributing please have a look at our contributor guidelines contributing md setting up a build environment you don t need to specifically build the c components for machine learning as by default the elasticsearch build will download pre compiled c artifacts setting up a build environment for ml cpp native code is complex if you are specifically interested in working with the ml cpp code then information regarding setting up a build environment can be found in the build setup build setup directory to use clion with the project please refer to the using clion build setup clion using clion md tutorial building if you do choose to build the project from the command line yourself for all platforms the following instructions apply from the top level of the project source the file set env sh e g set env sh when building on windows from the native command shell that command becomes set env bat run cmake b cmake build relwithdebinfo to generate the build system under the cmake build relwithdebinfo directory the config relwithdebinfo option may be omitted on linux and mac run cmake build cmake build relwithdebinfo config relwithdebinfo to build the libraries and the executables for the project the config relwithdebinfo option may be omitted on linux and mac this may take some time to speed up the build you can tell cmake to perform a parallel build using the j jobs option e g cmake build cmake build relwithdebinfo j 7 to build and run the unit tests run cmake build cmake build relwithdebinfo t test again this can be sped up somewhat by using the j option e g cmake build cmake build relwithdebinfo t test j 7 running although the executables are designed to be run from elasticsearch it is possible to run them from the command line this is particularly useful when attempting to debug issues and you have an input data set sufficient to replicate the error the location of the executables differs depending on the platform macos build distribution platform darwin x86 64 controller app contents macos linux build distribution platform linux x86 64 bin windows build distribution platform windows x86 64 bin the command line arguments will of course differ depending on which executable is being run but each has the help option e g build distribution platform linux x86 64 bin autodetect help usage autodetect options fieldname by fieldname options help display this information and exit version display version information and exit limitconfig arg optional limit config file modelconfig arg optional model config file fieldconfig arg optional field config file modelplotconfig arg optional model plot config file jobid arg id of the job this process is associated with logproperties arg optional logger properties file logpipe arg optional log to named pipe bucketspan arg optional aggregation bucket span in seconds default is 300 latency arg optional maximum delay for out of order records in seconds default is 0 summarycountfield arg optional field to that contains counts for pre summarized input default is none delimiter arg optional delimiter character for delimited data formats default is tab separated lengthencodedinput take input in length encoded binary format default is delimited timefield arg optional name of the field containing the timestamp default is time timeformat arg optional format of the date in the time field in strptime code default is the epoch time in seconds quantilesstate arg optional file to quantiles for normalization deletestatefiles if the quantilesstate option is used and this flag is set then delete the model state files once they have been read input arg optional file to read input from not present means read from stdin inputispipe specified input file is a named pipe output arg optional file to write output to not present means write to stdout outputispipe specified output file is a named pipe restore arg optional file to restore state from not present means no state restoration restoreispipe specified restore file is a named pipe persist arg optional file to persist state to not present means no state persistence persistispipe specified persist file is a named pipe persistinterval arg optional time interval at which to periodically persist model state mutually exclusive with bucketpersistinterval persistinforeground persistence occurs in the foreground defaults to background persistence bucketpersistinterval arg optional number of buckets after which to periodically persist model state mutually exclusive with persistinterval maxquantileinterval arg optional interval at which to periodically output quantiles if they have not been output due to an anomaly if not specified then quantiles will only be output following a big anomaly maxanomalyrecords arg the maximum number of records to be outputted for each bucket defaults to 100 a value 0 removes the limit memoryusage log the model memory usage at the end of the job multivariatebyfields optional flag to enable multi variate analysis of correlated by fields other executables exist under the devbin directory these are not built by default to build these you need to explicitly specify a target cmake build cmake build relwithdebinfo j 7 t model extractor the executable is created under the cmake build relwithdebinfo hierarchy so to run do cmake build relwithdebinfo devbin model extractor model extractor help | ai |
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tensorflow-object-detection-tutorial | objectherkenning met de computer vision library tensorflow deze blogpost verscheen op qdraw nl https qdraw nl blog design objectherkenning met de computer vision library tensorflow met computer vision wordt het mogelijk om foto s en video s op intelligente wijze te analyseren om onze steden slimmer en veiliger te maken nieuwe soorten robots te ondersteunen die productie optimaliseren in dit artikel ligt een onderdeel van computer vision uit namelijk object detection tensorflow object detection api een belangrijke functionaliteit van tensorflow is het image recognition tensorflow is een open source library dat door google in 2015 voor het grote publiek beschikbaar is gemaakt het wordt gebruikt om deep learningmodels te bouwen ontwerpen en te trainen met tensorflow is het mogelijk met de object detection api wat het toegankelijk maakt voor onderzoekers en softwareontwikkelaars om objecten te identificeren in een 2d beeld het doel van google van de object detection api is om een evenwicht te hebben tussen simplicity en performance er zijn een aantal voorgetrainde modellen welke door wetenschappers worden gebruikt om algoritmes te trainen en nu gaan we het gewoon zelf gebruiken tensorflow is een library die beschikbaar is vanuit python al het zware werk wordt buiten python gedaan python is een programmeertaal die veel wordt gebruikt voor machine learning en data analyse in deze tutorial ga ik ervan uit dat je een ubuntu virtuele machine tot je beschikking hebt buiten scope is het inrichten van nvidia grafische kaarten en met cuda ik heb deze code werkend op ubuntu 16 04 en mac os siera de tekst gaat verder na de onderstaande afbeelding objectherkenning met de computer vision library tensorflow uitzicht met object detection object detection uitzicht https media qdraw nl log objectherkenning met de computer vision library tensorflow 500 20170725 160045 tensorflow image kl jpg objectherkenning met de computer vision library tensorflow uitzicht met object detection foto 1 https media qdraw nl log objectherkenning met de computer vision library tensorflow 1000 20170725 160045 tensorflow image kl1k jpg objectherkenning met de computer vision library tensorflow uitzicht met object detection foto 1 pip een python package manager is pip voor het gemak maak ik gebruik van python 2 7 de code werkt ook met python 3 6 voor deze tutorial gebruiken we deze en met het onderstaande commando installeer je deze op je systeem het dollarteken geeft aan dat een bash commando is die als normale gebruiker moet worden uitgevoerd dit dollarteken hoeft niet mee te worden gekopieerd shell ubuntu sudo apt get install python pip git y shell mac os brew install python ik clone de github responsories naar mijn home folder shell cd tensorflow models clone the tensorflow models van de offici le repository in deze repository staan machine leaning modelen die getraind zijn met tensorflow voor deze tutorial gebruiken we alleen de slim module en object detection met dit commando kopieer je de inhoud van de repository de model map is 167 4 mb groot shell git clone https github com tensorflow models git het het pwd commando wordt het absolute path van de huidige map getoond shell pwd shell home dion voeg onderaan toe waar home dion de naam van je gebruiker is dit is om alle bestanden die in de map staan in python in het path laden shell nano profile shell export pythonpath pythonpath home dion models home dion models slim laad de inhoud van profile of herstart alle terminal vensters om de inhoud van het bestand te laden shell source profile protobuf google heeft een manier ontwikkeld om frozen models op te slaan een frozen model is neural network dat is opgeslagen en in het geheugen kan worden ingeladen dit protocol moet eerst worden ge nstalleerd shell ubuntu sudo apt get install protobuf compiler y shell mac os brew install protobuf de protobuf libraries moeten eerst worden gecompiled dit moet je doen vanuit de model map shell cd models shell protoc object detection protos proto python out en ik ga terug naar mijn home folder shell cd opencv met het onderstaande commando laad ik een bash script van een externe website waarbij opencv automatisch wordt gecompileerd en ge nstalleerd in usr local dit script installeert opencv 3 2 en werkt met ubuntu 16 04 shell curl l https raw githubusercontent com qdraw tensorflow object detection tutorial master install opencv ubuntu sh https raw githubusercontent com qdraw tensorflow object detection tutorial master install opencv ubuntu sh bash op mijn macbook maak ik gebruik van opencv 2 4 ik heb hier opencv 3 2 proberen te installeren allen dit werkt goed in combinatie met mac os siera en python 3 6 shell brew install homebrew science opencv de tekst gaat verder na de onderstaande afbeelding objectherkenning met de computer vision library tensorflow compiling opencv ubuntu 16 04 opencv ubuntu https media qdraw nl log objectherkenning met de computer vision library tensorflow 500 20170725 205133 compiling opencv kl jpg objectherkenning met de computer vision library tensorflow compiling opencv ubuntu 16 04 foto 2 https media qdraw nl log objectherkenning met de computer vision library tensorflow 1000 20170725 205133 compiling opencv kl1k jpg objectherkenning met de computer vision library tensorflow compiling opencv ubuntu 16 04 foto 2 tensorflow object detection tutorial repository in deze repository heb ik alle inhoud verzameld er zijn twee voorbeelden het eerste voorbeeld wordt een afbeelding geanalyseerd en het tweede voorbeeld laat een livebeeld zien van de webcam met de onderstaande opdracht kopieer je de map van github shell git clone https github com qdraw tensorflow object detection tutorial git het volgende deel van de uitleg voeren we uit vanuit de onderstaande map shell cd tensorflow object detection tutorial de benodigdheden van de demo moeten nog worden ge nstalleerd shell pip install r requirements txt het analyseren van een afbeelding voor deze demonstratie analyseren we een foto die gemaakt is bij colours op het kantoor in den bosch we zoeken naar alle objecten in deze foto het algoritme kan een aantal auto s al vinden shell python image object detection py druk ctrl c binnen het terminalvenster om het programma af te sluiten de tekst gaat verder na de onderstaande afbeelding objectherkenning met de computer vision library tensorflow herkennen van een appel en banaan appel banaan object detection https media qdraw nl log objectherkenning met de computer vision library tensorflow 500 20170725 220836 detect appel banaan kl jpg objectherkenning met de computer vision library tensorflow herkennen van een appel en banaan foto 3 https media qdraw nl log objectherkenning met de computer vision library tensorflow 1000 20170725 220836 detect appel banaan kl1k jpg objectherkenning met de computer vision library tensorflow herkennen van een appel en banaan foto 3 het analyseren van het beeld van je webcam met opencv wordt het mogelijk om webcambeelden in python in te laden in dit script draaien meerdere processen tegelijk waardoor het afsluiten lastig is shell python webcam object detection py de snelste en makkelijkste manier om af te sluiten is in een ander terminalvenster het proces te killen shell ubuntu pkill python shell mac os pkill python objectherkenning met de computer vision library tensorflow demo uitzicht https raw githubusercontent com qdraw tensorflow object detection tutorial master test images example webcam 640px gif objectherkenning met de computer vision library tensorflow demo het analyseren van het beeld van je webcam mocht de wereld van computer vision je interesse hebben gewekt maar weet je nog niet hoe je dit kunt toepassen en heb je de nodige vragen stuur mij dan een mailtje http qdraw nl contact html dan kunnen we een kopje koffie drinken deze blogpost verscheen op qdraw nl https qdraw nl blog design objectherkenning met de computer vision library tensorflow | tensorflow tensorflow-tutorials | ai |
design-systems-repo | design systems repo welcome to the design systems repo a frequently updated and growing collection of design system examples articles tools and talks the goal of the project is to put together a comprehensive and curated list of design systems style guides and pattern libraries that you can use for inspiration how to contribute detailed contribution guidelines are a work in progress you can just submit a pr for now with the markdown file and image of what you want to add or submit it here https jad8 typeform com to xnidfz contributors all contributors https img shields io badge all contributors 8 orange svg style flat square contributors a big thanks to these wonderful people emoji key https allcontributors org docs en emoji key all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tr td align center a href https github com tsiq swyx img src https avatars0 githubusercontent com u 35976578 v 4 width 100px alt br sub b tsiq swyx b sub a br a href content tsiq swyx title content a td td align center a href https github com alan oliv img src https avatars3 githubusercontent com u 4368481 v 4 width 100px alt br sub b alan oliveira b sub a br a href content alan oliv title content a td td align center a href https www konrad design img src https avatars1 githubusercontent com u 5304116 v 4 width 100px alt br sub b kevin henriquez b sub a br a href content kkhenriquez title content a td td align center a href http www aaronshekey com img src https avatars3 githubusercontent com u 1369864 v 4 width 100px alt br sub b aaron shekey b sub a br a href content aaronshekey title content a td td align center a href https boltdesignsystem com img src https avatars2 githubusercontent com u 1617209 v 4 width 100px alt br sub b salem ghoweri b sub a br a href content sghoweri title content a td td align center a href https github com chrisconnors ibm img src https avatars3 githubusercontent com u 35537391 v 4 width 100px alt br sub b chris connors b sub a br a href content chrisconnors ibm title content a td td align center a href http dbanks design img src https avatars0 githubusercontent com u 321279 v 4 width 100px alt br sub b danny banks b sub a br a href content dbanksdesign title content a td tr tr td align center a href http mattfelten com img src https avatars0 githubusercontent com u 488744 v 4 width 100px alt br sub b matt felten b sub a br a href content mattfelten title content a td td align center a href http twitter com crswll img src https avatars2 githubusercontent com u 182222 v 4 width 100px alt br sub b bill criswell b sub a br a href content crswll title content a td td align center a href https ttalbot fr img src https avatars1 githubusercontent com u 371041 v 4 width 100px alt br sub b thomas talbot b sub a br a href content ttalbot title content a td td align center a href https github com gstvribs img src https avatars1 githubusercontent com u 7907966 v 4 width 100px alt br sub b gustavo ribeiro b sub a br a href content gstvribs title content a td tr table markdownlint enable prettier ignore end all contributors list end | design-systems design-patterns style-guide pattern-library | os |
Graph-LLM | graphllm boosting graph reasoning ability of large language model this is the implementation for the paper graphllm boosting graph reasoning ability of large language model https arxiv org abs 2310 05845 setup you may need a single 80g gpu to run the experiment we experiment on cuda 11 8 and torch 2 0 1 setup up a new conda env and install necessary packages conda create n graph llm python 3 10 y pip install r requirements txt to run the code you need the checkpoint and tokenizer of llama 2 7b which you can access at meta https ai meta com resources models and libraries llama downloads after downloading llama 2 7b soft link the checkpoint folder and the tokenizer folder to the folder of this repository markdown ln s folder of llama 2 7b checkpoint llama 7b 2 ln s folder of llama 2 7b tokenizer llama 2 7b hf remember to replace the directory folder of llama 2 7b checkpoint and folder of llama 2 7b tokenizer with actual directories the four graph reasoning datasets are available https drive google com file d 1frxdcmhpkb1 kuzcxgzpkkilewbsbw4m you may download it and place the zip file in the directory of this repository and then run the command markdown unzip dataset zip d dataset get start train and evaluate the model with default settings on graph reasoning datasets on gpu 0 1 substructure counting markdown scripts sc sh 2 maximum triplet sum markdown scripts mts sh 3 shortest path markdown scripts sp sh 4 bipartite graph matching markdown scripts bgm sh more hyperparameter settings are at config py hyperparameter explanation n encoder layers number of transformer layers of textual encoder n decoder layers number of transformer layers of textual decoder n mp layers number of graph transformer layers adapter dim hidden dimention adapter len number of prefix tokens per llm layer rrwp positional encoding dimention batch size batch size in memory during training grad steps grad step times batch size batch size for optimization lr the learning rate num epochs number of training epochs warmup epochs number of linear warmup epochs wd weight decay | ai |
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machine-learning | machine learning something about machine learning and all implement with tensorflow | ai |
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TAG | tag this is the repository for my ios mobile application tag which is my individual project for software engineering ii and iii the final two class of the senior sequence of the computer science program at wou it uses the elimination framework api which is part of my team project https bitbucket org mgeorgebrown89 sneakysoftware vision statement for people who want to create and play the elimination based live action game like assassins or gotcha tag is a mobile application that allows for a host to make a game set rules and invite other users to join the game tag is played by assigning each player another play as a target until a circle is made the player only knows their target not who has them as a target then over the course of days weeks the player attempts to tag their target whether manually by using mock projectiles or by using the mobile app to take a picture which will use the elimination framework api and photo recognition or get in range of their targets phone and pressing a button to tag them unlike current methods for playing elimination based live action games this app will remove the subjectivity that comes from a human moderator and human players determining a successful elimination and also make adding more rules and features like player skill modifiers a player inventory and methods of elimination to games easier and more fun | os |
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node-express-reddit-clone | let s build a reddit clone last week we set out to build a reddit clone from the database s perspective we ended up with a redditapi with createpost getallposts createuser and other data related methods in this next part of the project we are going to take the functionality that we already built and make a website out of it to do this we are going to use many of the technologies we have already learned about but we will also be adding a bit more on top at the end of this project we should have at a bare minimum a reddit clone with the following functionality non logged in users will be able to view a list of posts ordered by one of hot top newest non logged in users will be able to signup and or login to the site logged in users will be able to cast either an up or down vote for each post they are looking at logged in users will be able to add new posts to the site a post is a url and a title and it belongs to a subreddit and a user getting started clone this project to your cloud9 workspace under your home directory then copy the reddit js file from the reddit nodejs api directory to the current project description of the pages of the site before starting to write any code let s figure out the different pages we will need homepage the homepage lists 25 posts as provided by the getallposts method of the redditapi signup page the signup page will be a simple page with an html form the form will have username and password fields as well as a signup submit button more on that later login page the login page will be a simple page with an html form the form will have username and password fields as well as a login submit button more on that later create post page the create post page will also be a simple page with an html form the form will have title and url text fields a dropdown for choosing the subreddit and a create submit button subreddits pages they will work similarly to the homepage except that the posts will be filtered for one subreddit only in your express code you will have one app get r subreddit and use the req params subreddit to make a request to your reddit api then you can use the same rendering code as the homepage to print the posts for that subreddit review rendering html with a templating engine pug rendering html by writing code like this javascript var output ul contents foreach function item output output li a href item url item title a output output ul can quickly get out of hand especially as you have a more complex page as seen in class express offers the pug https github com pugjs templating engine https expressjs com en guide using template engines html which was previously known as jade https github com pugjs pug issues 2184 the syntax will look a bit weird at first but it translates to html and allows you to avoid the whole string concatenation headache here s a full example in the server code javascript at the top already added for you app set view engine pug app get function request response this is only an example redditapi getallposts then function posts response render will call the pug template engine with the post list pug file response render post list posts posts in your post list pug pug h1 welcome to reddit clone ul post list each post in posts li post h2 a href post url post title p submitted by post username p score post score upvotes post upvotes downvotes post downvotes which will return this html html h1 welcome to reddit clone h1 ul class post list li class post h2 a href http blabla this is the first post a h2 p submitted by 514forever p p score 100 upvotes 500 downvotes 400 p li li class post h2 a href http blabla123 this is the second post a h2 p submitted by other user p p score 123 upvotes 523 downvotes 400 p li ul in general we want to return a full page of html not only a snippet for this pug gives us template inheritance https pugjs org language inheritance html we can create a layout which has the general structure of our page and have a placeholder for the content here would be an example of layout pug file by using so called blocks we can create placeholders for content pug doctype html html head meta charset utf 8 block title title the default title body block content then in our post list pug file we can extend this layout and provide a block for each block in the layout pug extends layout pug block title title welcome to reddit clone block content h1 welcome to reddit clone ul post list each post in posts li post h2 a href post url post title p submitted by post username p score post score upvotes post upvotes downvotes post downvotes check out the full documentation for pug https pugjs org api getting started html to learn more here are some of the important sections tags https pugjs org language tags html attributes https pugjs org language attributes html conditionals https pugjs org language conditionals html interpolation https pugjs org language interpolation html iteration https pugjs org language iteration html review handling form submissions we will have at least three form submissions to handle login signup and create post each form should be sent using a post request to the server sending a post request is an indication that we want to create new data on the target system therefore it s very important to not submit such data more than once browsers are good at helping with this notice that if you submit a form through post and try to refresh the resulting web page the browser will warn that you are about to re submit a form we can avoid this though a good practice is to always redirect the user after a post https en wikipedia org wiki post redirect get read this wikipedia article if you redirect the user to another page using an http 303 see other status code https en wikipedia org wiki post redirect get then the browser will load that other page with a get request and all will be well the user will not even be able to re submit the same form data for the signup form we could redirect the user to the login page after they re done for the login form we can redirect users to the homepage for the create post form we could also redirect the user to the homepage if we had a page per post with comments for example then we could also redirect the user to the new post page that they created like they do over at reddit to redirect users we can use the express res redirect http expressjs com en api html res redirect function review user signup and login processes one of the most sensitive aspects of a website is its security as we ve seen in the past years even some of the largest sites out there are not immune to hacks as web developers it s our job to make sure that a site we build is as secure as possible this will reduce the chances of compromising our customers personal data and or putting us out of business one field where this is super important is the user signup and login process during this process we are asking the user to provide us with a username and password combination that will be used to identify them some of our users will re use that same password for all their accounts it would be pretty bad if we stored their password in plain text and our database got compromised warning the signup login method described below is not meant to be 100 secure it s only meant to give you a bit of insight into how complicated this process can be in fact many https stormpath com companies https www userapp io make a business out of providing user management functionalities to other businesses this lets us concentrate on what makes our product different hashing passwords for this reason and many others we will never store our customers passwords in plain text in our database when creating a new user we will instead store a hashed version of their password hashing is a function that takes a string as an input like a password for example and uses an irreversible but consistent transformation of that string to generate its output let s imagine your password was a number my hashing function could be 1 take the password number and divide it by 100 using integer division 2 return the remainder of the division as the hash so if your password is 1234 i would store it as 34 1234 100 34 while i cannot recover your password if you give me an input password i can check that it has the same hash this would work well as long as there are no collisions if you tell me your password is 134 or 2234 they will all hash to 34 and you will be able to login for these reasons n the real world we will be using hashing functions that have little chance for collision an example of such a hashing function is sha 1 if i pass the string hunter2 through the sha 1 function i will get back a8a00adebf1411b8baf07bdc688ce3889e8f7cb2 simply changing the string to hunter2 note the lack of capital h then the sha 1 will be f3bbbd66a63d4bf1747940578ec3d0103530e21d while this is not a demonstration of any security feature you can see that even a slight change in the input string will result in a completely different hash we can compute the number of possible combinations of sha 1 outputs if we see the output as a set of 40 hexadecimal digits then the number of combinations would be 16 40 which is a huuuuge number however big that number may be the number of possible password strings is infinite this means that our hashing function will definitely have collisions meaning that two passwords will hash to the same string however up until 2016 there has still not been a practical way to create a collision with this hashing function moreover we will not simply be storing the password has a hash of the input string that would still be too easy to crack for example the hunter2 password above is a popular string it comes from an old internet joke that you may lookup in your own time there exists a few websites out there that can reverse sha 1 outputs of popular strings https isc sans edu tools reversehash html there s no magic involved they simply have a large database of sha 1 input output combinations for all these reasons we will be using a library called bcrypt https github com ncb000gt node bcrypt js to take care of our password hashing when signing up a new user we will use bcrypt s hash function https github com ncb000gt node bcrypt js usage to generate a hashed version of the password if you look at the reddit api we built last week the createuser function uses bcrypt to hash a password in this case the output will look like this 2a 10 26ofmwevtb4 6nwuyopg6ojylyl uh7barqo5wfkri9j9wjozfiei verifying passwords eventually we ll have to build a login function in there we will receive again a username and a password this time we will go to our database to find a user with the same username if we don t find a user then we can respond with username or password incorrect this will prevent attackers from knowing whether or not the username exists if we do find a user we can use bcrypt s compare function to compare the found user s hashed password with the password we received from the login process it would go a bit like this 1 user loads the login page 2 browser displays an html form with a username field and a password field 3 user fills in both fields and clicks on the login button 4 browser constructs a query string like username john password hunter2 5 browser looks at the action and method of the form and sends an http request usually a post 6 server receives the request and parses it into appropriate object under request body 7 in our web server we use the request body username and request body password to call the redditapi checkuserlogin function 8 the redditapi checkuserlogin function 1 takes a username and password 2 does an sql query to our database select from users where username 3 after retrieving the sql result uses the bcrypt compare https github com iceddev bcrypt as promised basic usage function to check if the hashed password matches the input password 4 if the passwords match the promise resolves with the full user object minus the hashed password 5 if the passwords don t match the promise throws an error username or password invalid 9 if we have a password match from the redditapi checkuserlogin function 1 create a session by calling redditapi createsession passing it the full user object from previous step 2 when the promise resolves retrieve the sessionid 3 use response cookie to set a cookie with the name session and the value being the sessionid from the promise 4 use response redirect to send the user back to the homepage but now they ll be logged in time to eat that cookie cool we now have set a random unguessable session token in the user s browser cookies next time they do an http request to our server their browser will send the session token we can then check in our database if it exists and what userid it s linked to a middleware was already created for you that does this its code is in lib check login token js and will be explained in detail later here s the gist of what it does 1 check the request cookies for a cookie called session 2 if it does not exist move on 3 if the cookie exists do a database query to see if the session token belongs to a user 1 if it doesn t exist move on 2 if it does exist then we update the request to contain the logged in user s info explanation of initial file structure this project already contains many files and directories in this section we go over what each one does in detail warning attention even though these explanations are given to you you should go over each file line by line before starting the project if there is something you don t understand make sure you get an explanation before moving on index js this file sits at the root of your project and is the main file that will execute your web server it contains a bit more logic than is normally desirable but we tried to split up some of that logic in other modules where possible here s what this file does 1 load express and create a new web server 2 load all the express middlewares we will use and adds them to the pipeline with app use 3 loads the redditapi created a database connection and sets up the api 4 delegates anything under static to the static middleware https expressjs com en starter static files html 5 delegates anything under auth to a custom express router https expressjs com en guide routing html express router 6 sets up a few other request handlers for the homepage subreddits creating posts and voting these functionalities could be further split up in their own modules 7 finally makes the web server listen on process env port which is set to 8080 on cloud9 database tables sql contains the create table sql statements for the whole database database data sql contains a data dump to give your reddit clone some initial data public this directory contains static files like css and logo images the files are served by express static middleware controllers auth js this file contains a custom express router https expressjs com en guide routing html express router here we export a function that receives the redditapi instance and returns the router an express router is like a tiny sub application that takes care of its own paths notice that the get s and post s in there will say login and signup but in the index js file we mount the router under auth this means that the final urls will be auth login and auth signup lib reddit js this file contains the redditapi class it s a correct version of the last project you worked on lib check login token js this file exports a function that takes a redditapi instance and returns an express middleware that will check if the current request has a session token if it does the middleware will try to find the user that corresponds to that session and add the user object under the request loggedinuser property this same user object will also be added under request locals loggedinuser request locals is an object and its properties will be made available to the html template engine lib only logged in js this file exports a simple middleware that will force a user to be logged in if a request comes from a non logged in user the middleware will not call next and instead return a 401 unauthorized response this middleware is not meant to be used on every request look in index js for how this middleware is used views layout pug this file contains the main layout for the website it outputs the main html structure and uses pug s inheritance system https pugjs org language inheritance html the part that says block content will be replaced with the content output by any template that extends layout pug check homepage pug for an example of extending the layout views post list pug this file creates a pug mixin https pugjs org language mixins html which is the equivalent of a function in that it can take arguments and be re used this mixin is used in views homepage pug and will be useful for you to build other views views homepage pug this file extends the layout pug file and defines a block called content this block in turn uses the postlist mixin to output a list of posts after outputting a generic title views error pug this file can be used anytime you have access to an error object it is useful to output the error in a nice way to the browser your work this section details the work that you have to do on this project as well as suggestions to improve it further warning attention even though the qa team s job is to thouroughly test the website application it is still your duty as a developer to make sure you hand an app that has been tested to the best of your knowledge this will enable the qa team to concentrate on the really hard to find bugs or features and everyone will benefit signup and login the first thing you ll have to do is complete the signup and login features of the site 1 signup in controllers auth js make the app get signup render an html signup form to do this add a file views signup form pug and make it output the following form html h1 signup h1 form action auth signup method post p username input type text name username p p password input type password name password p p button type submit signup button p form make sure that your pug file extends the layout pug so that your signup form gets output with all the surrounding html then implement the code of authcontroller post signup this code will receive the form data under request body there you have to call myreddit createuser and pass it the necessary info once the createuser promise is resolved use response redirect to send the user to auth login 2 login in controllers auth js make the app get login render an html login form to do this add a file views login form pug and make it output the following form html h1 login h1 form action auth login method post p username input type text name username p p password input type password name password p p button type submit login button p form this form is super similar to the signup form except for the action make sure that your pug file extends the layout pug so that your signup form gets output with all the surrounding html then implement the code of authcontroller post login to do this you ll need to complete some code in lib reddit js 1 in lib reddit js complete the code of the checkuserlogin function following the instructions in comments 2 in lib reddit js complete the code of the createusersession function following the instructions in comments when these two functions are done start working on the post handler for login 1 use the checkuserlogin function passing it the request body username and password 2 if the login check is unsuccessful send a 401 unauthorized status to the browser else move to step 3 3 since login is successful use the checkuserlogin response to find the user s id and pass it to the createusersession function 4 when that function is done you ll get back a random session id use express response cookie to set a cookie with name session and value being that session id 5 use response redirect to send the user back to the home page 3 checking if user is actually logged in the code in lib check login token js gets executed on every request to check if the request contains a session cookie even though the code was written for you it relies on a function called getuserfromsession in the redditapi implement that function by doing a join query between the sessions and users tables and return the full user object for the given session id once you do that refresh the home page and you should see a message at the top saying welcome your user that s it you have fully implemented the signup login and cookie consumption process your pug templates have access to a variable called loggedinuser it will be false if there is no user and will contain a user object otherwise check the code of views layout pug to see an example of using that variable subreddit pages in index js there is an app get r subreddit that is currently not returning anything we d like to make it output a list of posts just like on the front page but only for the requested subreddit to do this you ll have to make a few changes 1 first we have to go from subreddit name to subreddit id create a redditapi function called getsubredditbyname name this should make a query to the database and return a subreddit object that matches the given name if no subreddit was found the promise should resolve with null 2 call getsubredditbyname from the app get handler and pass it the request params subreddit if you get back null send a 404 response otherwise move to the next step 3 modify the redditapi getallposts function to accept a subredditid optional parameter if this parameter is passed the select query should be changed to add a where p subredditid and return only posts for that subreddit 4 call getallposts from your app get handler passing it the subreddit id from step 2 then render the resulting list of posts using the post list pug template since this is a subreddit the rendering should include the name of the subreddit as well as its description before the post list you can use pug conditionals in post list pug to make this happen sorted pages in index js there is an app get sort method that is currently not returning anything we d like to make it output a list of posts just like on the front page but sorted by something other than createdat desc to do this you ll first have to make some changes to the redditapi getallposts function make it accept an optional sortingmethod parameter that can be hot or top if the sorting method is set to top then the posts should be ordered by votescore desc if the sorting method is set to hot then the posts should be ordered by votescore now p createdat desc this formula will take the score but divide it by the number of seconds the post has been online this will make newer posts appear higher if they have the same number of votes as an older post in the app get handler check if request params method is either hot or top if not then return a 404 error if it is call the getallposts and then render a list of posts just like on the home page finally make sure that you have a href sort hot and a href sort top links somewhere on the page so that the user can change sorting methods by clicking creating new posts in index js there is a get and post handlers for createpost let s implement them make the get handler return an html form like the following one by creating a create post form pug file html h1 share a new link h1 form method post action createpost p subreddit select name subredditid option value 1 firstsubreddit option one option tag for each subreddit select p p url input type text name url p p title input type text name title p form the select element is a dropdown list the names between option tags will be shown to the user but the value xx part will be sent to the server to output this select box you ll have to use the redditapi getallsubreddits function before rendering the template then implement the post handler notice that the code uses the onlyloggedin middleware to make sure that this will only be called when there is a logged in user here you will call redditapi createpost and you ll need to pass it the information from the form you also need to provide a userid but that will be coming from request user instead of request body once the post is created successfully the only thing you can do is redirect the user use the newly created posts id to redirect them to post postid which you will implement next single post view in index js there is a get handler for post postid this should use the redditapi getsinglepost function to get the post by its id if the post does not exist return a 404 if it does then create a new pug template that will output that post as well as its comments to do this you ll not only need to call getsinglepost but also getcommentsforpost make sure to use promise all to do this since the two requests are independent votes and voting on content how will the user cast a vote for a post eventually their browser will have to make a post request to vote next to each post when outputing the li for that post you have to add the following form html form action vote method post input type hidden name postid value the id of the current post button type submit name vote value 1 upvote this button button type submit name vote value 1 downvote this button form this code looks weird because we have two submit buttons the way it works is that the submit buttons are each tied to a 1 and 1 value for the vote property clicking on one of the buttons will submit its value as the vote value in the form then you have to implement the post handler for vote in index js make it call redditapi createvote and pass the necessary information the postid will come from the hidden input hidden inputs are useful because they allow us to pass information to the server without any user input done done this concludes the minimal part of the project the following section gives you some suggestions on features you can add to make your reddit clone more unique warning attention even though the qa team s job is to thouroughly test the website application it is still your duty as a developer to make sure you hand an app that has been tested to the best of your knowledge this will enable the qa team to concentrate on the really hard to find bugs or features and everyone will benefit and yes we did write this twice on purpose thanks for reading this far extra features the following are suggestions for features you can add to your reddit clone if you have an idea for a feature that s not listed here don t hesitate to ask us what we think about it each feature is rated from one star up to three star depending on its difficulty level it s up to you and your group to decide which features you d like to implement star add a thumbnail for image posts in all post listings post list pug if the post url looks like it leads to an image ends in gif png or jpg then include a 40x40 image thumbnail along with the rest of the information for that post warning attention normally it s not recommended to embed img tags with images from other domains and sometimes those domains will block you from doing so if we wanted to implement this feature in a real application we would have to produce the thumbnails on our own server star user posts page when listing posts the user who created the post is linked as u username in index js add a get handler for a new u username endpoint this endpoint should serve list of all the posts made by that user create a new redditapi method getallpostsforusername to retrieve all the posts made by a given username re use the post list mixin to render the post list for that user star emojis in post title and comments text make post titles and comments text emojifiable so that if a word like rocket or metal appears in the text they will be replaced with rocket or metal look at the node emoji https github com omnidan node emoji package on npm and try to incorporate it in your project the best place to do this is in the redditapi functions concerned by this change getallposts getsinglepost and getcommentsforpost star allow markdown in posts markdown is a text format that can be automatically converted to html but is easier to write and read for humans learn more about markdown https github com adam p markdown here wiki markdown cheatsheet it s a great format for writing technical documentation because it allows for fixed width text as well as code blocks with syntax highlighting for example this readme md is written with markdown for this feature you can use the marked https www npmjs com package marked package to transform a string of markdown to html when outputting that string of html with pug you ll have a surprise pug will do the safe thing and escape your html https pugjs org language interpolation html effectively replacing characters like with their html entity counterparts like lt read the pug interpolation https pugjs org language interpolation html documentation and find out how to tell pug to not escape this bit of html star add a comment form to the single post page earlier we created a single post view for the endpoint post postid extend the pug template of this page to add a comment form which will post its data to a new endpoint createcomment then add a post handler for createcomment and make use of the redditapi createcomment function to add a comment when the comment is created redirect the user back to the post page from the post handler star add voting on comments currently comments are being displayed by createdat date we will build this feature the same way as the post votes feature the steps are roughly 1 create a commentvotes table similar to the votes table for posts 2 add a createcommentvote method to the redditapi 3 add an app post commentvote handler similar to the post vote handler 4 add an html form to each comment output similar to the post vote form 5 test everything star css make it look nice next week we will look at css together working on this feature will allow you to get a head start and make your reddit clone more unique add basic style to the main elements of your reddit clone style the header the main navigation the main content the sidebar and the footer try to make it look nice if you need help to pick a colorway you can try adobe color cc https color adobe com explore filter newest for inspiration star star add self posts feature in addition to sharing links give users the ability to share their thoughts through self posts here is an example of self post on reddit https www reddit com r showerthoughts comments 6650fj i watched my dog chase his tail for 10 minutes to accomplish this feature you ll have to implement the following steps 1 add column posttext text to the posts table and set appropriate values for the already existing posts 2 update the createpost function so that it accepts a posttext in the post object a post should have one and only one of url or posttext 3 update the create a post form to accept self posts you ll have to add a textarea name posttext textarea element where the user will be able to type their self post 4 update the app post createpost handler to accept and pass through the value of the text area 5 optional next week we will look at how to make a web page dynamic with browser side javascript if you want to take a head start make the form dynamic by allowing the user to toggle between a self post form and a link sharing form star star subreddit moderator add a feature that will designate a moderator for a subreddit a moderator is someone who will have admin power that will allow him or her to delete the posts in this subreddit in order to achieve this you will need to 1 add a new column called moderatorid in your subreddits table when creating a new subreddit insert the userid of the creator as the moderatorid 2 when the moderator of a subreddit visits the subreddit he should have a new button on every post that allows him or her to delete a post attention you will have to make sure only the moderator of this subreddit can delete a post 3 clicking the button should submit a form that makes a post to deletepost with the id of the post make sure to only allow the moderator to delete a post 4 bonus you can also add this delete button on the single post page star star star theme by subreddit with custom style css this feature depends on the subreddit moderator feature allow the moderator of a subreddit to change the appearance of it in order to do this you will need to add a new page to allow the style customization at r subreddit admin on this page the moderator should be presented with a list of styles they can modify here is an example of what it could look like imgur http i imgur com xyx2s3q png to do this you will need to 1 create a new table in your database called subredditstyle this table should have the following columns id subredditid stylename stylevalue there should be a unique key constraint on the subredditid stylevalue pair 2 when saving the custom style page it should insert any modified entry in your subredditstyle table every style element has its own row use the on duplicate key update in your insert query like for the votes table 3 when on a subreddit page grab all the custom styles and inject them into the page using a style style tag in the head of the output star star star add a forgot password feature this feature only makes sense if users provide an email address to implement the feature you ll need to cover the following points 1 add an email varchar 100 column to the users table and make sure there is a unique constraint on that column emails should be optional 2 at signup allow the user to provide an email address and make it optional modify the signup form and the post handler as well as the createuser function accordingly 3 add a auth recover page through the controllers auth js router with a form that asks for the email address make it post to auth createresettoken 4 add a post handler for auth createresettoken in the controllers auth js router it will receive a request body email if the email address is found in the database we will let the user reset their password by using a random token similar to the session id token 1 create a new table passwordresettokens with columns userid int and token varchar 100 making sure that the token is unique 2 add a createpasswordresettoken userid method to the redditapi in this method generate a random string and insert it along with the user id in the passwordresettokens table 3 send an email to the user with a link to your website at auth resetpassword token xxxx replacing xxxx with the random string that is in the database 1 signup for an account at mailgun https app mailgun com new signup a web service for sending emails 2 install the npm package mailgun js https www npmjs com package mailgun js and read its documentation 3 go to https app mailgun com app domains and click on the sandbox domain to find your domain name and api key 4 use the mailgun js module to send an email to your user with the link to reset their password auth resetpassword token xxxx 5 add a get handler for auth resetpassword that will output a form with a new password field when the form should also have a hidden input that will be whatever is in the token param of the query string the form will post to resetpassword with the token and the newpassword 6 add a resetpassword token newpassword method to the redditapi in it find if the token corresponds to a real token and which userid it corresponds to then reset their password by hashing the newpassword with bcrypt and making an update to the database make sure to delete the password reset token from the database so that it cannot be reused 7 add a post handler for auth resetpassword that will call redditapi resetpassword and pass it the necessary info once the password is updated redirect the user to auth login so they can re login with their new password 8 test everything warning attention in a production ready system we will usually avoid sending an email from a request handler to make the web server response more snappy we will prefer to queue an email task that will be handled by another process after the web server has returned | reddit workshop reddit-api hashing-passwords cookie user-signup middleware hash-functions express nodejs mysql bcrypt promises | front_end |
wxECGAnalyzer | wxecganalyzer detection of abnormal rhythm morphologies is more difficult than normal beat therefore we need to collect abnormal rhythm signals in clinical practice to improve the detection of qrs complex br this project is for electrocardiogram ecg signal algorithms design and validation include preprocessing qrs complex detection embedded system validation ecg segmentation label your machine learning dataset and clinical trial etc br br for algorithm performance in ansi aami ec38 it is required that the detected qrs shall in the 150ms range of the signed point from annotation by human exper br br mit bih to do qrs complex br p align center img src https github com gcy wxecganalyzer blob master res demo gif p use dependence win10 wxwidgets 3 1 2 vs2017 msvc 10 0 17763 sdk mac high sierra wxwidgets 3 x g build win10 1 open wxecganalyzer sln 2 rebuild alt text https github com gcy wxecganalyzer blob master res win10 png raw true mac high sierra 1 make alt text https github com gcy wxecganalyzer blob master res high 20sierra png raw true operation manual clinical trial 1 setup your ecg device to clinical trial 2 connect vcp to wxecganalyzer tools vcp select cu or com devices bouadrate is uart only 3 monitor target 4 segmentation and save target morphology of the ecg you can modify the windows size tihs project is 700ms 5 select ecg codes https github com gcy wxecganalyzer blob master src mac define h to labeling snapshot csv https github com gcy wxecganalyzer blob master res snapshot csv example file alt text https github com gcy wxecganalyzer blob master res snapshot png raw true validation of algorithm for real time embedded systems holter 1 add cpp and h file and create new wxradiobox item to apply 2 design algorithm with c c 3 clinical trial and validation 4 port code file to your embedded project alt text https github com gcy wxecganalyzer blob master res single 20mode gif raw true features x filter x finite impulse response fir x qrs complex detect algorithm x adaptive threshold algorithm x hc chen algorithm x enhanced so chen x pan tompkins deep ecg 1d cnn x heart rate variability x heart rate x sdnn x nn50 x pnn50 x labeling x segmentation automatic segmentation x mit bih ecg codes automatic labeling svm cnn feature mit bih database operate wfdb quantitative sensitivity specificity accuracy confusion matrix x other operate x record raw data 60s x save four plot png x fast furious transform fft amplitude spectrum x connect serial port the point of qrs complex detection algorithm finite impulse response this project with fir to filter ecg signal coefficients generate parameter is 360hz 32taps band pass 0 51hz 8 9hz and kaiser window br coefficients generator https github com gcy finite impulse response fir filter adaptive threshold algorithm this algorithm purpose for this project involving two parts first is adaptive threshold update and second find the local maxima and minima br define threshold update period sampling rate target low frequency for example target is ecg hr normal people heart rate is 45 150 bpm that is equally 0 75hz 2 5hz 360sr 0 75hz 480 signal point decrease update period the algorithm be sensitive br determinate the local maxima and minima we need to know gradient calculate below equation to find the gradient threshold factor for 12bit adc is 3 0f increase threshold factor the local maximum and minimum are determinated by the algorithm which need more gradient br gradient rms cv threshold factor br design more rules for the local maximum and minimum your project will be able to recognize ecg pqrst br hc chen this project modified lpf and hpf window size hp buffer 150ms qrs complex size forgetting factor alpha is used to avoid threshold keeping high level br enhanced so chen the threshold parameter and filter parameter are 4 and 16 in the implementation enhanced point is 123 350ms for 360hz sampling rate if last qrs complex point is over triple sampling rate we will decrease threshold until zero slope square process could detect abnormal heart rhythms br pan tompkins the pan tompkins filter of classical version is design for 200hz and band pass 5hz 15hz if ecg signal is over 200hz it needs to downsample to 200hz this project with fir to clean 360hz ecg signal and modified search back period must be more than sampling rate 1 66 600 point but this project is 550 point br real time complexity running on stm32f407 clock 168mhz and enable fpu y axix time uint is nanoseconds x axix is signal point br charts below show runtime environment time complexity adative threshold algorithm complexity is follow gradient threshold step edge the qrs complex detection of the classical pan tompkins algorithm mainly complexity is search back hc chen and so chen relatively stable br adaptive threshold algorithm average 8 100692259ns alt text https github com gcy wxecganalyzer blob master res ata 20time png raw true hc chen average 2 060941828ns alt text https github com gcy wxecganalyzer blob master res hc 20chen 20time png raw true enhanced so chen average 2 074ns alt text https github com gcy wxecganalyzer blob master res so 20and 20chen 20time png raw true pan tompkins average 548 0295567ns alt text https github com gcy wxecganalyzer blob master res pt 20time png raw true heart rate variability heart rate and hrv are move average in the implementation n beat size 16 br experiment device devices arm cortex m4 stm32f407 discovery ad8232 cnibp https github com gcy continuous non invasive blood pressure research platform ecg and ppg pulse arrival time based git use the ecg part without right leg drive setup connect ecg signal output to stm32f4 pc0 pin next load elf https github com gcy wxecganalyzer tree master embedded and run the setup adc sampling rate is 360hz with adc dma timer trigger same as mit bit arrhythmia database record br br for vcp mode just define br cpp define vcp mode for holter br cpp define single mode and define qrs complex detect algorithm flag br cpp define ata define hc define so define pt video br algorithm test audi r8 http img youtube com vi gphpex1oun4 0 jpg https youtu be gphpex1oun4 motion artifact running audi r8 http img youtube com vi kjehhs2av2s 0 jpg https youtu be kjehhs2av2s disturbance audi r8 http img youtube com vi atonvdifkyu 0 jpg https youtu be atonvdifkyu br reference find the local maxima and minima http billauer co il peakdet html heart rate variability https en wikipedia org wiki heart rate variability hc chen algorithm hc chen sw chen a moving average based filtering system with its application to real time qrs detection computers in cardiology 2003 https github com blakemilner real time qrs detection modified enhanced so chen algorithm so h h chan k l 1997 october development of qrs detection method for real time ambulatory cardiac monitor in proceedings of the 19th annual international conference of the ieee engineering in medicine and biology society magnificent milestones and emerging opportunities in medical engineering cat no 97ch36136 vol 1 pp 289 292 ieee lee r g chou i c lai c c liu m h chiu m j 2005 a novel qrs detection algorithm applied to the analysis for heart rate variability of patients with sleep apnea biomedical engineering applications basis and communications 17 05 258 262 wang c y chang r c h lin c h su s h 2018 may fatigue detection system using enhanced so and chan method in 2018 ieee international conference on consumer electronics taiwan icce tw pp 1 2 ieee pan tompkins algorithm pan j tompkins w j 1985 a real time qrs detection algorithm ieee transactions on biomedical engineering 3 230 236 afonso v x 1993 ecg qrs detection biomedical digital signal processing 237 264 https github com rafaelmmoreira pantompkinsqrs modified license mit license copyright c 2019 tony guo permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | qrs-detection heart-rate-variability ecg tompkins labeling pan-tompkins electrocardiogram signal chen-algorithm stm32f4 adaptive-thresholding qrs-complex biomedical ecg-signal ecg-segmentation adaptive-threshold-algorithm biomedical-signal-processing biomedical-engineering | os |
mlr | mlr img src man figures logo png align right package website release https mlr mlr org com dev https mlr mlr org com dev machine learning in r badges start tic https github com mlr org mlr workflows tic badge svg branch main https github com mlr org mlr actions cran status badge https www r pkg org badges version ago mlr https cran r project org package mlr cran checks https badges cranchecks info worst mlr svg https cran r project org web checks check results mlr html cran downloads https cranlogs r pkg org badges mlr https cran r project org package mlr stackoverflow https img shields io badge stackoverflow mlr blue svg https stackoverflow com questions tagged mlr lifecycle https img shields io badge lifecycle retired orange svg https lifecycle r lib org articles stages html codecov https codecov io gh mlr org mlr branch main graph badge svg https app codecov io gh mlr org mlr badges end cran release site https cran r project org package mlr online tutorial https mlr mlr org com index html changelog https mlr mlr org com news index html stackoverflow https stackoverflow com questions tagged mlr mlr mattermost https lmmisld lmu stats slds srv mwn de mlr invite blog https mlr org com deprecated mlr is considered retired from the mlr org team we won t add new features anymore and will only fix severe bugs we suggest to use the new mlr3 https mlr3 mlr org com framework from now on and for future projects not all features of mlr are already implemented in mlr3 if you are missing a crucial feature please open an issue in the respective mlr3 extension package https github com mlr org mlr3 wiki extension packages and do not hesitate to follow up on it installation release r install packages mlr development r remotes install github mlr org mlr citing mlr in publications please cite our jmlr paper https jmlr org papers v17 15 066 html bibtex https www jmlr org papers v17 15 066 bib some parts of the package were created as part of other publications if you use these parts please cite the relevant work appropriately an overview of all mlr related publications can be found here https mlr mlr org com articles tutorial mlr publications html introduction r does not define a standardized interface for its machine learning algorithms therefore for any non trivial experiments you need to write lengthy tedious and error prone wrappers to call the different algorithms and unify their respective output additionally you need to implement infrastructure to resample your models optimize hyperparameters select features cope with pre and post processing of data and compare models in a statistically meaningful way as this becomes computationally expensive you might want to parallelize your experiments as well this often forces users to make crummy trade offs in their experiments due to time constraints or lacking expert programming skills mlr provides this infrastructure so that you can focus on your experiments the framework provides supervised methods like classification regression and survival analysis along with their corresponding evaluation and optimization methods as well as unsupervised methods like clustering it is written in a way that you can extend it yourself or deviate from the implemented convenience methods and construct your own complex experiments or algorithms furthermore the package is nicely connected to the openml https github com openml openml r r package and its online platform https www openml org which aims at supporting collaborative machine learning online and allows to easily share datasets as well as machine learning tasks algorithms and experiments in order to support reproducible research features clear s3 interface to r classification regression clustering and survival analysis methods abstract description of learners and tasks by properties convenience methods and generic building blocks for your machine learning experiments resampling methods like bootstrapping cross validation and subsampling extensive visualizations e g roc curves predictions and partial predictions simplified benchmarking across data sets and learners easy hyperparameter tuning using different optimization strategies including potent configurators like iterated f racing irace sequential model based optimization variable selection with filters and wrappers nested resampling of models with tuning and feature selection cost sensitive learning threshold tuning and imbalance correction wrapper mechanism to extend learner functionality in complex ways possibility to combine different processing steps to a complex data mining chain that can be jointly optimized openml connector for the open machine learning server built in parallelization detailed tutorial miscellaneous simple usage questions are better suited at stackoverflow using the mlr https stackoverflow com questions tagged mlr tag please note that all of us work in academia and put a lot of work into this project simply because we like it not because we are paid for it new development efforts should go into mlr3 https github com mlr org mlr3 we have a own style guide which can easily applied by using the mlr style from the styler https github com r lib styler package see our wiki https github com mlr org mlr3 wiki style guide styler mlr style for more information talks workshops etc mlr outreach https github com mlr org mlr outreach holds all outreach activities related to mlr and mlr3 | machine-learning data-science tuning cran r-package predictive-modeling classification regression statistics r survival-analysis imbalance-correction tutorial mlr learners hyperparameters-optimization feature-selection multilabel-classification clustering stacking | ai |
deepnlp-OXFORD-CS-2017 | oxford s cs deep natural language processing 2017 course lectures amp practicals lectures and practicals for the course oxford deep nlp 2017 course lectures lectures for the course https github com red sackz lectures oxford deep nlp 2017 course practicals practicals for the course practical 1 word2vec https github com red sackz practical1 practical 2 text classification https github com red sackz practical2 practical 3 text classification with rnns https github com red sackz practical3 make sure to check back for more updates new stuff https github com oxford cs deepnlp 2017 | ai |
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bevel | hyperledger bevel join the chat chat image chat url all contributors badge start do not remove or modify this section all contributors https img shields io badge all contributors 6 orange svg style flat square contributors all contributors badge end chat url https discord gg hyperledger chat image https img shields io discord 905194001349627914 logo hyperledger style plastic svg license https img shields io badge license apache 202 0 blue svg license documentation status https readthedocs org projects hyperledger bevel badge version latest https hyperledger bevel readthedocs io en latest badge latest cii best practices https bestpractices coreinfrastructure org projects 3548 badge https bestpractices coreinfrastructure org projects 3548 dci lint status https github com hyperledger bevel actions workflows dci lint yml badge svg https github com hyperledger bevel actions workflows dci lint yml short description short description scope of project scope of project getting started getting started hyperledger fabric hyperledger fabric corda enterprise corda enterprise corda opensource corda opensource hyperledger indy hyperledger indy quorum quorum hyperledger besu hyperledger besu contact contact contributing contributing initial committers initial committers sponsor sponsor short description an automation framework for rapidly and consistently deploying production ready distributed ledger technology dlt platforms scope of project hyperledger bevel delivers an automation framework for rapidly and consistently deploying production ready dlt platforms to cloud infrastructure what is hyperledger bevel docs images hyperledger bevel overview png what is hyperledger bevel hyperledger bevel makes use of ansible helm and kubernetes to deploy production dlt networks specifically it makes use of ansible for configuration of the network by devops engineers it then uses helm charts as instructions for deploying the necessary components to kubernetes kubernetes was chosen to allow for hyperledger bevel to deploy the dlt networks to any cloud that supports kubernetes hyperledger bevel currently supports corda hyperledger fabric hyperledger indy and quorum it is the intention to add support for hyperledger besu and corda enterprise in the near future other dlt platforms can easily be added getting started to get started with the framework quickly follow our getting started guidelines https hyperledger bevel readthedocs io en latest gettingstarted html detailed operator and developer documentation is available on our readthedocs site https hyperledger bevel readthedocs io en latest index html the documentation can also be built locally be following instructions in the docs folder hyperledger fabric for hyperledger fabric we use the official docker containers provided by that project a number of different ansible scripts will allow you to either create a new network across clouds or join an existing network hyperledger bevel fabric docs images hyperledger bevel fabric png hyperledger bevel for hyperledger fabric corda enterprise for corda enterprise we build docker containers from the corda source with licensed jars a number of different ansible scripts will allow you to either create a new network across clouds or join an existing network hyperledger bevel corda enterprise docs images hyperledger bevel corda ent png hyperledger bevel for corda enterprise corda opensource for corda opensource we build docker containers from the corda source a number of different ansible scripts will allow you to either create a new network across clouds or join an existing network hyperledger bevel corda docs images hyperledger bevel corda png hyperledger bevel for corda hyperledger indy for hyperledger indy we build docker containers from our source code a number of different ansible scripts will allow you to create a new network across clouds hyperledger bevel indy docs images hyperledger bevel indy png hyperledger bevel for hyperledger indy quorum for quorum we use the official docker containers provided by quorum a number of different ansible scripts will allow you to either create a new network across clouds with choice of consensus between ibft and raft and a choice of transaction manager between tessera and constellation hyperledger bevel quorum docs images hyperledger bevel quorum png hyperledger bevel for quorum hyperledger besu for hyperledger besu we use the official docker containers provided by that project a number of different ansible scripts will allow you to create a new network across clouds hyperledger bevel besu docs images hyperledger bevel besu png hyperledger bevel for hyperledger besu contact we welcome your questions feedback on our discord channel https discord com channels 905194001349627914 941739691336679454 please join our discord first https discord gg hyperledger contributing we welcome contributions to hyperledger bevel in many forms and there s always plenty to do please review contributing contributing md guidelines to get started build if you are not using the provided jenkins automation scripts you can run the provisioning scripts within a docker runtime independent from your target kubernetes cluster build provisioning image docker build t hyperledgerlabs baf build run the provisioning scripts docker run it v pwd home bevel hyperledgerlabs baf build initial committers tkuhrt https github com tkuhrt jonathan m hamilton https github com jonathan m hamilton sownak https github com sownak sponsor mark wagner github n1zyz https github com n1zyz email mwagner redhat com mailto mwagner redhat com tsc member contributors thanks goes to these wonderful people emoji key https allcontributors org docs en emoji key all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tr td align center a href https github com alvaropicazo img src https avatars githubusercontent com u 76157062 v 4 s 100 width 100px alt br sub b alvaro picazo b sub a br a href maintenance alvaropicazo title maintenance a td td align center a href https github com suvajit sarkar img src https avatars githubusercontent com u 55580532 v 4 s 100 width 100px alt br sub b suvajit sarkar b sub a br a href https github com hyperledger bevel commits author suvajit sarkar title code a a href https github com hyperledger bevel commits author suvajit sarkar title documentation a td td align center a href https github com deepakkumardbd img src https avatars githubusercontent com u 57094817 v 4 s 100 width 100px alt br sub b deepak kumar b sub a br a href https github com hyperledger bevel commits author deepakkumardbd title code a td td align center a href https github com jagpreetsinghsasan img src https avatars githubusercontent com u 56861721 v 4 s 100 width 100px alt br sub b jagpreet singh sasan b sub a br a href https github com hyperledger bevel commits author jagpreetsinghsasan title code a a href maintenance jagpreetsinghsasan title maintenance a td td align center a href https github com angelaalagbe img src https avatars githubusercontent com u 54588164 v 4 s 100 width 100px alt br sub b angela alagbe b sub a br a href https github com hyperledger bevel commits author angelaalagbe title documentation a a href content angelaalagbe title content a td td align center a href https github com mgcepeda img src https avatars githubusercontent com u 83813093 v 4 s 100 width 100px alt br sub b marina g mez cepeda b sub a br a href https github com hyperledger bevel commits author mgcepeda title code a td tr table markdownlint restore prettier ignore end all contributors list end this project follows the all contributors https github com all contributors all contributors specification contributions of any kind welcome | blockchain hyperledger kubernetes | blockchain |
Awesome-Multimodal-LLM | awesome multimodal llm awesome https awesome re badge svg https awesome re a curated list of papers related to multi modal machine learning especially multi modal large language models llms table of contents tutorials tutorials datasets datasets research papers research papers survey papers survey papers core areas core areas multimodal understanding multimodal understanding vision centric understanding vision centric understanding embodied centric understanding embodied centric understanding domain specific models domain specific models multimodal evaluation multimodal evaluation tutorials recent advances in vision foundation models https vlp tutorial github io cvpr 2023 workshop pdf https datarelease blob core windows net tutorial vision foundation models 2023 slides chunyuan cvpr2023 tutorial lmm pdf datasets m3it a large scale dataset towards multi modal multilingual instruction tuning https arxiv org abs 2306 04387 arxiv 2023 data https huggingface co datasets mminstruction m3it llava instruction 150k https llava vl github io arxiv 2023 data https huggingface co datasets liuhaotian llava instruct 150k youku mplug 10m https arxiv org abs 2306 04362 arxiv 2023 data https github com x plug youku mplug multiinstruct improving multi modal zero shot learning via instruction tuning https arxiv org abs 2212 10773 acl 2023 data https github com vt nlp multiinstruct research papers survey papers a survey on multimodal large language models https arxiv org abs 2306 13549 arxiv 2023 project page https github com bradyfu awesome multimodal large language models vision language models for vision tasks a survey https arxiv org abs 2304 00685 arxiv 2023 core areas multimodal understanding shikra unleashing multimodal llm s referential dialogue magic https arxiv org abs 2306 15195 arxiv 2023 code https github com shikras shikra pandagpt one model to instruction follow them all http arxiv org abs 2305 16355 arxiv 2023 code https github com yxuansu pandagpt shikra unleashing multimodal llm s referential dialogue magic https arxiv org abs 2306 15195 arxiv 2023 code https github com shikras shikra mimic it multi modal in context instruction tuning https arxiv org abs 2305 03726 arxiv 2023 code https github com luodian otter llavar enhanced visual instruction tuning for text rich image understanding https arxiv org abs 2306 17107 arxiv 2023 code https github com salt nlp llavar metavl transferring in context learning ability from language models to vision language models https arxiv org abs 2306 01311 arxiv 2023 mplug owl modularization empowers large language models with multimodality https arxiv org abs 2304 14178 arxiv 2023 code https github com x plug mplug owl instructblip towards general purpose vision language models with instruction tuning https arxiv org abs 2305 06500 arxiv 2023 code https github com salesforce lavis tree main projects instructblip blip 2 bootstrapping language image pre training with frozen image encoders and large language models https arxiv org abs 2301 12597 icml 2023 code https github com salesforce lavis tree main projects blip2 cheap and quick efficient vision language instruction tuning for large language models https arxiv org abs 2305 15023 arxiv 2023 code https github com luogen1996 lavin multimodal gpt a vision and language model for dialogue with humans https arxiv org abs 2305 04790 arxiv 2023 code https github com open mmlab multimodal gpt llama adapter v2 parameter efficient visual instruction model https arxiv org abs 2304 15010 arxiv 2023 code https github com zrrskywalker llama adapter language is not all you need aligning perception with language models https arxiv org abs 2302 14045v2 arxiv 2023 code https github com microsoft unilm one peace exploring one general representation model toward unlimited modalities http arxiv org abs 2305 11172 arxiv 2023 code https github com ofa sys one peace x llm bootstrapping advanced large language models by treating multi modalities as foreign languages https arxiv org abs 2305 04160 arxiv 2023 code https github com phellonchen x llm visual instruction tuning https arxiv org abs 2304 08485 arxiv 2023 code https github com haotian liu llava visual chatgpt talking drawing and editing with visual foundation models https arxiv org abs 2303 04671 arxiv 2023 code https github com microsoft taskmatrix pali a jointly scaled multilingual language image model http arxiv org abs 2209 06794 iclr 2023 blog https ai googleblog com 2022 09 pali scaling language image learning in html grounding language models to images for multimodal inputs and outputs https arxiv org abs 2301 13823 icml 2023 code https github com kohjingyu fromage ofa unifying architectures tasks and modalities through a simple sequence to sequence learning framework https arxiv org abs 2202 03052 icml 2022 code https github com ofa sys ofa flamingo a visual language model for few shot learning https arxiv org abs 2204 14198 neurips 2022 vision centric understanding lisa reasoning segmentation via large language model https arxiv org abs 2308 00692 arxiv 2023 code https github com dvlab research lisa contextual object detection with multimodal large language models https arxiv org abs 2305 18279 arxiv 2023 code https github com yuhangzang contextdet kosmos 2 grounding multimodal large language models to the world https arxiv org abs 2306 14824 arxiv 2023 code https github com microsoft unilm tree master kosmos 2 contextual object detection with multimodal large language models https arxiv org abs 2305 18279 arxiv 2023 code https github com yuhangzang contextdet fast segment anything https arxiv org abs 2306 12156 arxiv 2023 code https github com casia iva lab fastsam multi modal classifiers for open vocabulary object detection https arxiv org abs 2306 05493 icml 2023 code https github com prannaykaul mm ovod instruction vit multi modal prompts for instruction learning in vit https arxiv org abs 2305 00201 arxiv 2023 images speak in images a generalist painter for in context visual learning https arxiv org abs 2212 02499 arxiv 2023 code https github com baaivision painter video llama an instruction tuned audio visual language model for video understanding https arxiv org abs 2306 02858 arxiv 2023 code https github com damo nlp sg video llama seggpt segmenting everything in context http arxiv org abs 2304 03284 arxiv 2023 code https github com baaivision painter visionllm large language model is also an open ended decoder for vision centric tasks http arxiv org abs 2305 11175 arxiv 2023 code https github com opengvlab visionllm matcher segment anything with one shot using all purpose feature matching https arxiv org abs 2305 13310 arxiv 2023 personalize segment anything model with one shot https arxiv org abs 2305 03048 arxiv 2023 code https github com zrrskywalker personalize sam segment anything https arxiv org abs 2304 02643 arxiv 2023 code https github com facebookresearch segment anything uni perceiver v2 a generalist model for large scale vision and vision language tasks https arxiv org abs 2211 09808 cvpr 2023 code https github com fundamentalvision uni perceiver a generalist framework for panoptic segmentation of images and videos https arxiv org abs 2210 06366 arxiv 2022 a unified sequence interface for vision tasks http arxiv org abs 2206 07669 neurips 2022 code https github com google research pix2seq pix2seq a language modeling framework for object detection https arxiv org abs 2109 10852 iclr 2022 code https github com google research pix2seq embodied centric understanding scaling up and distilling down language guided robot skill acquisition https arxiv org abs 2307 14535 arxiv 2023 code https github com columbia ai robotics scalingup rt 2 vision language action models transfer web knowledge to robotic control https robotics transformer2 github io assets rt2 pdf preprint project page https www deepmind com blog rt 2 new model translates vision and language into action voxposer composable 3d value maps for robotic manipulation with language models https arxiv org abs 2307 05973 arxiv 2023 project page https voxposer github io motiongpt human motion as a foreign language https arxiv org abs 2306 14795 arxiv 2023 code https github com openmotionlab motiongpt instruct2act mapping multi modality instructions to robotic actions with large language model https arxiv org abs 2305 11176 arxiv 2023 code https github com opengvlab instruct2act palm e an embodied multimodal language model https arxiv org abs 2303 03378 arxiv 2023 blog https palm e github io generative agents interactive simulacra of human behavior https arxiv org abs 2304 03442 arxiv 2023 vision language models as success detectors https arxiv org abs 2303 07280 arxiv 2023 tidybot personalized robot assistance with large language models https arxiv org abs 2305 05658 arxiv 2023 code https github com jimmyyhwu tidybot lm nav robotic navigation with large pre trained models of language vision and action https arxiv org abs 2207 04429 corl 2022 blog https sites google com view lmnav code https github com blazejosinski lm nav domain specific models llava med training a large language and vision assistant for biomedicine in one day http arxiv org abs 2306 00890 arxiv 2023 code https github com microsoft llava med multimodal evaluation lamm language assisted multi modal instruction tuning dataset framework and benchmark https arxiv org abs 2306 06687 arxiv 2023 code https github com openlamm lamm lovm language only vision model selection https arxiv org abs 2306 08893 arxiv 2023 code https github com orrzohar lovm accountable textual visual chat learns to reject human instructions in image re creation https arxiv org abs 2303 05983 arxiv 2023 project page https matrix alpha github io | awesome-list computer-vision large-language-models machine-learning multimodal-machine-learning natural-language-processing paper-list vision-language-model multimodal-large-language-models | ai |
php-ml | php ml machine learning library for php minimum php version https img shields io badge php 3e 3d 207 2 8892bf svg https php net latest stable version https img shields io packagist v php ai php ml svg https packagist org packages php ai php ml build status https travis ci org php ai php ml svg branch master https travis ci org php ai php ml documentation status https readthedocs org projects php ml badge version master http php ml readthedocs org total downloads https poser pugx org php ai php ml downloads svg https packagist org packages php ai php ml license https poser pugx org php ai php ml license svg https packagist org packages php ai php ml coverage status https coveralls io repos github php ai php ml badge svg branch master https coveralls io github php ai php ml branch master scrutinizer code quality https scrutinizer ci com g php ai php ml badges quality score png b master https scrutinizer ci com g php ai php ml branch master p align center img src https github com php ai php ml raw master docs assets php ml logo png p fresh approach to machine learning in php algorithms cross validation neural network preprocessing feature extraction and much more in one library php ml requires php 7 2 simple example of classification php require once dir vendor autoload php use phpml classification knearestneighbors samples 1 3 1 4 2 4 3 1 4 1 4 2 labels a a a b b b classifier new knearestneighbors classifier train samples labels echo classifier predict 3 2 return b awards a href http www yegor256 com 2016 10 23 award 2017 html img src http www yegor256 com images award 2017 winner itcraftsmanpl png width 400 a documentation to find out how to use php ml follow documentation http php ml readthedocs org installation currently this library is in the process of being developed but you can install it with composer composer require php ai php ml examples example scripts are available in a separate repository php ai php ml examples https github com php ai php ml examples datasets public datasets are available in a separate repository php ai php ml datasets https github com php ai php ml datasets features association rule learning apriori http php ml readthedocs io en latest machine learning association apriori classification svc http php ml readthedocs io en latest machine learning classification svc k nearest neighbors http php ml readthedocs io en latest machine learning classification k nearest neighbors naive bayes http php ml readthedocs io en latest machine learning classification naive bayes decision tree cart ensemble algorithms bagging bootstrap aggregating random forest adaboost linear adaline decision stump perceptron logisticregression regression least squares http php ml readthedocs io en latest machine learning regression least squares svr http php ml readthedocs io en latest machine learning regression svr decisiontreeregressor clustering k means http php ml readthedocs io en latest machine learning clustering k means dbscan http php ml readthedocs io en latest machine learning clustering dbscan fuzzy c means metric accuracy http php ml readthedocs io en latest machine learning metric accuracy confusion matrix http php ml readthedocs io en latest machine learning metric confusion matrix classification report http php ml readthedocs io en latest machine learning metric classification report regression workflow pipeline http php ml readthedocs io en latest machine learning workflow pipeline featureunion neural network multilayer perceptron classifier http php ml readthedocs io en latest machine learning neural network multilayer perceptron classifier cross validation random split http php ml readthedocs io en latest machine learning cross validation random split stratified random split http php ml readthedocs io en latest machine learning cross validation stratified random split feature selection variance threshold http php ml readthedocs io en latest machine learning feature selection variance threshold selectkbest http php ml readthedocs io en latest machine learning feature selection selectkbest preprocessing normalization http php ml readthedocs io en latest machine learning preprocessing normalization imputation missing values http php ml readthedocs io en latest machine learning preprocessing imputation missing values labelencoder lambdatransformer numberconverter columnfilter onehotencoder feature extraction token count vectorizer http php ml readthedocs io en latest machine learning feature extraction token count vectorizer ngramtokenizer whitespacetokenizer wordtokenizer tf idf transformer http php ml readthedocs io en latest machine learning feature extraction tf idf transformer dimensionality reduction pca principal component analysis kernel pca lda linear discriminant analysis datasets array http php ml readthedocs io en latest machine learning datasets array dataset csv http php ml readthedocs io en latest machine learning datasets csv dataset files http php ml readthedocs io en latest machine learning datasets files dataset svm http php ml readthedocs io en latest machine learning datasets svm dataset mnist http php ml readthedocs io en latest machine learning datasets mnist dataset md ready to use iris http php ml readthedocs io en latest machine learning datasets demo iris wine http php ml readthedocs io en latest machine learning datasets demo wine glass http php ml readthedocs io en latest machine learning datasets demo glass models management persistency http php ml readthedocs io en latest machine learning model manager persistency math distance http php ml readthedocs io en latest math distance matrix http php ml readthedocs io en latest math matrix set http php ml readthedocs io en latest math set statistic http php ml readthedocs io en latest math statistic linear algebra contribute guide contributing md https github com php ai php ml blob master contributing md issue tracker github com php ai php ml https github com php ai php ml issues source code github com php ai php ml https github com php ai php ml you can find more about contributing in contributing md contributing md license php ml is released under the mit licence see the bundled license file for details author arkadiusz kondas arkadiuszkondas | ai |
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NLP-Project | documentation https img shields io badge documentation yes brightgreen svg report pdf license mit https img shields io badge license mit yellow svg license nlp project project on abstractive summarization of news for course on natural language processing at iit delhi table of contents installation installation usage usage documentation documentation author author license license installation sh pip install r requirements txt pip install r demo requirements txt usage sh cd src python app py then navigate to http 127 0 0 1 5000 and access the tool documentation refer to a href https github com nilax97 nlp project blob master report pdf target blank report pdf a author nilaksh agarwal website nilax97 github io https nilax97 github io github nilax97 https github com nilax97 linkedin nilaksh97 https linkedin com in nilaksh97 license mit license copyright c 2021 nilaksh agarwal | natural-language-processing news summarization abstractive-summarization nlp iit-delhi | ai |
awesome-refreshing-llms | awesome refreshing llms omit from toc awesome https awesome re badge svg https awesome re license mit https img shields io badge license mit green svg license https img shields io badge prs welcome red github last commit branch https img shields io github last commit hyintell awesome refreshing llms main logo github color blue although large language models llms are impressive in solving various tasks they can quickly be outdated after deployment maintaining their up to date status is a pressing concern in the current era how can we refresh llms to align with the ever changing world knowledge without expensive retraining from scratch p align center img src images llm align world cropped jpg width 60 height 60 alt llm align world example br em an llm after training is static and can be quickly outdated for example a href https openai com blog chatgpt target blank chatgpt a has a knowledge br cutoff date of september 2021 without a href https openai com blog chatgpt plugins target blank web browsing a it does not know the latest information ever since em p news 2023 10 our survey paper is now available on arxiv how do large language models capture the ever changing world knowledge a review of recent advances https arxiv org abs 2310 07343 2023 10 our survey paper how do large language models capture the ever changing world knowledge a review of recent advances has been accepted by emnlp 2023 https 2023 emnlp org we will release the camera ready version soon 2023 10 we create this repository to maintain a paper list on refreshing llms without retraining table of contents news news table of contents table of contents papers papers methods overview methods overview knowledge editing knowledge editing meta learning meta learning hypernetwork editor hypernetwork editor locate and edit locate and edit other other continual learning continual learning continual pre training continual pre training continual knowledge editing continual knowledge editing memory enhanced memory enhanced retrieval enhanced retrieval enhanced internet enhanced internet enhanced resources resources related survey related survey tools tools citation citation acknowledgement contribution acknowledgement contribution papers methods overview to refresh llms to align with the ever changing world knowledge without retraining we roughly categorize existing methods into implicit and explicit approaches implicit means the approaches seek to directly alter the knowledge stored in llms such as parameters or weights while explicit means more often incorporating external resources to override internal knowledge such as augmenting a search engine please see our paper for more details p align center img src images taxonomy png width 75 height 75 alt methods taxonomy br em taxonomy of methods to align llms with the ever changing world knowledge em p p align center img src images compare of methods cropped jpg width 75 height 75 alt methods overview br em a high level comparison of different approaches em p knowledge editing knowledge editing ke is an arising and promising research area that aims to alter the parameters of some specific knowledge stored in pre trained models so that the model can make new predictions on those revised instances while keeping other irrelevant knowledge unchanged we categorize existing methods into meta learning hypernetwork and locate and edit based methods meta learning year venue paper link 2023 arxiv reckoning reasoning through dynamic knowledge encoding static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 06349 2020 iclr editable neural networks static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https openreview net forum id hjedxaetvs static badge https img shields io badge code black logo github https github com editable iclr2020 editable hypernetwork editor year venue paper link 2023 kbs a divide and conquer framework for knowledge editing static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https www sciencedirect com science article pii s0950705123005762 2023 arxiv inspecting and editing knowledge representations in language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2304 00740 static badge https img shields io badge code black logo github https github com evandez remedi 2023 arxiv propagating knowledge updates to lms through distillation static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2306 09306 static badge https img shields io badge code black logo github https github com shankarp8 knowledge distillation 2023 eacl methods for measuring updating and visualizing factual beliefs in language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2023 eacl main 199 static badge https img shields io badge code black logo github https github com peterbhase slag belief updating 2022 iclr fast model editing at scale static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https openreview net forum id 0dczxewfopt static badge https img shields io badge code black logo github https github com eric mitchell mend 2021 emnlp editing factual knowledge in language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2021 emnlp main 522 static badge https img shields io badge code black logo github https github com nicola decao knowledgeeditor locate and edit year venue paper link 2023 arxiv klob a benchmark for assessing knowledge locating methods in language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2309 16535 static badge https img shields io badge code black logo github https github com juyiming klob 2023 arxiv editing commonsense knowledge in gpt static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 14956 static badge https img shields io badge code black logo github https github com anshitag memit csk 2023 arxiv pmet precise model editing in a transformer static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2308 08742 static badge https img shields io badge code black logo github https github com xpq tech pmet 2023 arxiv journey to the center of the knowledge neurons discoveries of language independent knowledge neurons and degenerate knowledge neurons static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2308 13198 2023 arxiv dissecting recall of factual associations in auto regressive language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2304 14767 2023 iclr mass editing memory in a transformer static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https openreview net forum id mkbcahiygys static badge https img shields io badge code black logo github https github com kmeng01 memit 2022 acl knowledge neurons in pretrained transformers static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 acl long 581 static badge https img shields io badge code black logo github https github com hunter ddm knowledge neurons 2022 neurips fast model editing at scale static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https proceedings neurips cc paper files paper 2022 hash 6f1d43d5a82a37e89b0665b33bf3a182 abstract conference html static badge https img shields io badge code black logo github https github com kmeng01 rome other year venue paper link 2023 arxiv eva kellm a new benchmark for evaluating knowledge editing of llms static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2308 09954 2023 arxiv evaluating the ripple effects of knowledge editing in language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2307 12976 static badge https img shields io badge code black logo github https github com edenbiran rippleedits 2023 arxiv cross lingual knowledge editing in large language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2309 08952 static badge https img shields io badge code black logo github https github com krystalan bi zsre 2023 arxiv language anisotropic cross lingual model editing static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2205 12677 continual learning continual learning cl aims to enable a model to learn from a continuous data stream across time while reducing catastrophic forgetting of previously acquired knowledge with cl a deployed llm has the potential to adapt to the changing world without costly re training from scratch below papers employ cl for aligning language models with the current world knowledge including continual pre training and continual knowledge editing continual pre training year venue paper link 2023 arxiv kilm knowledge injection into encoder decoder language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2302 09170 static badge https img shields io badge code black logo github https github com alexa kilm 2023 arxiv semiparametric language models are scalable continual learners static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2303 01421 2023 arxiv meta learning online adaptation of language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 15076 2023 arxiv moduleformer modularity emerges from mixture of experts static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2306 04640 static badge https img shields io badge code black logo github https github com ibm moduleformer 2023 arxiv self information update for large language models through mitigating exposure bias static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 18582 2023 arxiv continual pre training of large language models how to re warm your model static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2308 04014 2023 iclr continual pre training of language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https openreview net forum id m gdiitai3o static badge https img shields io badge code black logo github https github com uic liu lab continuallm 2023 icml lifelong language pretraining with distribution specialized experts static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 12281 2022 acl elle efficient lifelong pre training for emerging data static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 findings acl 220 static badge https img shields io badge code black logo github https github com thunlp elle 2022 emnlp fine tuned language models are continual learners static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 emnlp main 410 static badge https img shields io badge code black logo github https github com thomasscialom t0 continual learning 2022 emnlp continual training of language models for few shot learning static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 emnlp main 695 static badge https img shields io badge code black logo github https github com uic liu lab cpt 2022 emnlp temporalwiki a lifelong benchmark for training and evaluating ever evolving language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 emnlp main 418 static badge https img shields io badge code black logo github https github com joeljang temporalwiki 2022 iclr lora low rank adaptation of large language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https openreview net forum id nzevkeefyf9 static badge https img shields io badge code black logo github https github com microsoft lora 2022 iclr towards continual knowledge learning of language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https openreview net forum id vfsrb5mimo9 static badge https img shields io badge code black logo github https github com joeljang continual knowledge learning 2022 naacl demix layers disentangling domains for modular language modeling static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 naacl main 407 static badge https img shields io badge code black logo github https github com kernelmachine demix 2022 naacl lifelong pretraining continually adapting language models to emerging corpora static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 naacl main 351 2022 neurips factuality enhanced language models for open ended text generation static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2206 04624 static badge https img shields io badge code black logo github https github com nayeon7lee factualityprompt 2022 tacl time aware language models as temporal knowledge bases static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https direct mit edu tacl article doi 10 1162 tacl a 00459 110012 time aware language models as temporal knowledge static badge https img shields io badge code black logo github https github com google research language tree master language templama 2021 acl k adapter infusing knowledge into pre trained models with adapters static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2021 findings acl 121 static badge https img shields io badge code black logo github https github com microsoft k adapter 2021 eacl analyzing the forgetting problem in pretrain finetuning of open domain dialogue response models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2021 eacl main 95 2020 emnlp recall and learn fine tuning deep pretrained language models with less forgetting static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2020 emnlp main 634 static badge https img shields io badge code black logo github https github com sanyuan chen recadam continual knowledge editing year venue paper link 2023 arxiv aging with grace lifelong model editing with discrete key value adapters static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2211 11031 static badge https img shields io badge code black logo github https github com thartvigsen grace 2023 iclr transformer patcher one mistake worth one neuron static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https openreview net forum id 4oyugegbpm static badge https img shields io badge code black logo github https github com zeroyuhuang transformer patcher 2022 acl on continual model refinement in out of distribution data streams static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 acl long 223 static badge https img shields io badge code black logo github https github com facebookresearch cmr 2022 acl plug and play adaptation for continuously updated qa static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 findings acl 37 memory enhanced pairing a static llm with a growing non parametric memory enables it to capture information beyond its memorized knowledge during inference the external memory can store a recent corpus or feedback that contains new information to guide the model generation year venue paper link 2023 arxiv adaptation approaches for nearest neighbor language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2211 07828 2023 arxiv semiparametric language models are scalable continual learners static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2303 01421 2023 arxiv mquake assessing knowledge editing in language models via multi hop questions static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 14795 static badge https img shields io badge code black logo github https github com princeton nlp mquake 2022 emnlp you can t pick your neighbors or can you when and how to rely on retrieval in the knn lm static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 findings emnlp 218 2022 emnlp nearest neighbor zero shot inference static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 emnlp main 214 static badge https img shields io badge code black logo github https github com swj0419 knn prompt 2022 emnlp memory assisted prompt editing to improve gpt 3 after deployment static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 emnlp main 183 static badge https img shields io badge code black logo github https github com madaan memprompt 2022 emnlp towards teachable reasoning systems using a dynamic memory of user feedback for continual system improvement static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 emnlp main 644 static badge https img shields io badge code black logo github https allenai org data teachme 2022 icml neuro symbolic language modeling with automaton augmented retrieval static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2201 12431 static badge https img shields io badge code black logo github https github com neulab retomaton 2022 icml memory based model editing at scale static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2206 06520 static badge https img shields io badge code black logo github https github com eric mitchell serac 2022 naacl learning to repair repairing model output errors after deployment using a dynamic memory of feedback static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2022 findings naacl 26 static badge https img shields io badge code black logo github https github com allenai interscript 2021 emnlp efficient nearest neighbor language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2021 emnlp main 461 static badge https img shields io badge code black logo github https github com jxhe efficient knnlm 2021 emnlp beliefbank adding memory to a pre trained language model for a systematic notion of belief static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https aclanthology org 2021 emnlp main 697 2020 iclr generalization through memorization nearest neighbor language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https openreview net forum id hklbjcekvh static badge https img shields io badge code black logo github https github com urvashik knnlm retrieval enhanced leveraging an off the shelf retriever and the in context learning ability of llms this line of work designs better retrieval strategies to incorporate world knowledge into a fixed llm through prompting which can be divided into single stage and multi stage p align center img src images single multi stage cropped jpg width 50 height 50 alt single and multiple stage retrieval br em single stage left typically retrieves once while multi stage right involves multiple retrievals or revisions to solve complex questions em p year venue paper link 2023 acl augmentation adapted retriever improves generalization of language models as generic plug in static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 17331 static badge https img shields io badge code black logo github https github com openmatch augmentation adapted retriever 2023 acl when not to trust language models investigating effectiveness of parametric and non parametric memories static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2212 10511 static badge https img shields io badge code black logo github https github com alextmallen adaptive retrieval 2023 acl interleaving retrieval with chain of thought reasoning for knowledge intensive multi step questions static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2212 10509 static badge https img shields io badge code black logo github https github com stonybrooknlp ircot 2023 acl rarr researching and revising what language models say using language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2210 08726 static badge https img shields io badge code black logo github https github com anthonywchen rarr 2023 acl multitool cot gpt 3 can use multiple external tools with chain of thought prompting static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 16896 static badge https img shields io badge code black logo github https github com inabatatsuro multitool cot 2023 arxiv can we edit factual knowledge by in context learning static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 12740 static badge https img shields io badge code black logo github https github com zce1112zslx ike 2023 arxiv replug retrieval augmented black box language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2301 12652 2023 arxiv improving language models via plug and play retrieval feedback static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 14002 2023 arxiv measuring and narrowing the compositionality gap in language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2210 03350 static badge https img shields io badge code black logo github https github com ofirpress self ask 2023 arxiv art automatic multi step reasoning and tool use for large language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2303 09014 static badge https img shields io badge code black logo github https github com bhargaviparanjape language programmes 2023 arxiv chatcot tool augmented chain of thought reasoning on chat based large language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 14323 static badge https img shields io badge code black logo github https github com rucaibox chatcot 2023 arxiv check your facts and try again improving large language models with external knowledge and automated feedback static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2302 12813 static badge https img shields io badge code black logo github https github com pengbaolin llm augmenter 2023 arxiv question answering as programming for solving time sensitive questions static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 14221 static badge https img shields io badge code black logo github https github com microsoft contextualsp tree master qaap 2023 arxiv active retrieval augmented generation static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 06983 static badge https img shields io badge code black logo github https github com jzbjyb flare 2023 arxiv demonstrate search predict composing retrieval and language models for knowledge intensive nlp static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2212 14024 static badge https img shields io badge code black logo github https github com stanfordnlp dspy 2023 arxiv enhancing retrieval augmented large language models with iterative retrieval generation synergy static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 15294 2023 arxiv verify and edit a knowledge enhanced chain of thought framework static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 03268 2023 arxiv critic large language models can self correct with tool interactive critiquing static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 11738 static badge https img shields io badge code black logo github https github com microsoft prophetnet tree master critic 2023 arxiv wikichat a few shot llm based chatbot grounded with wikipedia static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 14292 2023 arxiv query rewriting for retrieval augmented large language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 14283 2023 arxiv knowledge solver teaching llms to search for domain knowledge from knowledge graphs static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2309 03118 2023 iclr prompting gpt 3 to be reliable static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https openreview net forum id 98p5x51l5af static badge https img shields io badge code black logo github https github com noviscl gpt3 reliability 2023 iclr decomposed prompting a modular approach for solving complex tasks static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https openreview net forum id nggzqjzary static badge https img shields io badge code black logo github https github com allenai decomp 2023 iclr react synergizing reasoning and acting in language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https openreview net forum id we vluyul x static badge https img shields io badge code black logo github https github com ysymyth react 2023 tacl in context retrieval augmented language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2302 00083 static badge https img shields io badge code black logo github https github com ai21labs in context ralm 2022 arxiv rethinking with retrieval faithful large language model inference static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2301 00303 static badge https img shields io badge code black logo github https github com hornhehhf rr internet enhanced a recent trend uses the whole web as the knowledge source and equips llms with the internet to support real time information seeking year venue paper link 2023 acl large language models are built in autoregressive search engines static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 09612 static badge https img shields io badge code black logo github https github com ziems llm url 2023 acl rarr researching and revising what language models say using language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2210 08726 static badge https img shields io badge code black logo github https github com anthonywchen rarr 2023 arxiv measuring and narrowing the compositionality gap in language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2210 03350 static badge https img shields io badge code black logo github https github com ofirpress self ask 2023 arxiv art automatic multi step reasoning and tool use for large language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2303 09014 static badge https img shields io badge code black logo github https github com bhargaviparanjape language programmes 2023 arxiv taskmatrix ai completing tasks by connecting foundation models with millions of apis static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2303 16434 static badge https img shields io badge code black logo github https github com microsoft taskmatrix tree main taskmatrix ai 2023 arxiv mm react prompting chatgpt for multimodal reasoning and action static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2303 11381 static badge https img shields io badge code black logo github https github com microsoft mm react 2023 arxiv active retrieval augmented generation static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 06983 static badge https img shields io badge code black logo github https github com jzbjyb flare 2023 arxiv chameleon plug and play compositional reasoning with large language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2304 09842 static badge https img shields io badge code black logo github https github com lupantech chameleon llm 2023 arxiv critic large language models can self correct with tool interactive critiquing static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 11738 static badge https img shields io badge code black logo github https github com microsoft prophetnet tree master critic 2023 arxiv query rewriting for retrieval augmented large language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2305 14283 2023 iclr react synergizing reasoning and acting in language models static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https openreview net forum id we vluyul x static badge https img shields io badge code black logo github https github com ysymyth react 2022 arxiv internet augmented language models through few shot prompting for open domain question answering static badge https img shields io badge paper 23b31b1b logo arxiv labelcolor grey https arxiv org abs 2203 05115 resources related survey augmented language models a survey https arxiv org abs 2302 07842 2023 the life cycle of knowledge in big language models a survey https arxiv org abs 2303 07616 2023 interactive natural language processing https arxiv org abs 2305 13246 2023 editing large language models problems methods and opportunities https arxiv org abs 2305 13172 2023 tool learning with foundation models https arxiv org abs 2304 08354 2023 unifying large language models and knowledge graphs a roadmap https arxiv org abs 2306 08302 2023 a comprehensive survey of forgetting in deep learning beyond continual learning https arxiv org abs 2307 09218 2023 large language models for information retrieval a survey https arxiv org abs 2308 07107 a review on language models as knowledge bases https arxiv org abs 2204 06031 2022 a survey of knowledge enhanced text generation https dl acm org doi 10 1145 3512467 2022 a survey of knowledge intensive nlp with pre trained language models https arxiv org abs 2202 08772 2022 a survey on knowledge enhanced pre trained language models https arxiv org abs 2212 13428 2022 retrieving and reading a comprehensive survey on open domain question answering https arxiv org abs 2101 00774 2021 knowledge enhanced pretrained language models a compreshensive survey https arxiv org abs 2110 08455 2021 tools langchain https github com langchain ai langchain a framework for developing applications powered by language models chatgpt plugins https openai com blog chatgpt plugins designed specifically for language models with safety as a core principle and help chatgpt access up to date information run computations or use third party services easyedit https github com zjunlp easyedit an easy to use knowledge editing framework for llms fastedit https github com hiyouga fastedit injecting fresh and customized knowledge into large language models efficiently using one single command pycontinual https github com zixuanke pycontinual an easy and extendible framework for continual learning avalanche https github com continualai avalanche an end to end library for continual learning based on pytorch citation if our research helps you please kindly cite our paper bibtex article zhang2023large title how do large language models capture the ever changing world knowledge a review of recent advances author zhang zihan and fang meng and chen ling and namazi rad mohammad reza and wang jun journal arxiv preprint arxiv 2310 07343 year 2023 acknowledgement contribution this field is evolving very fast and we may miss important works please don t hesitate to share your work pull requests are always welcome if you spot anything wrong e g broken links typos etc or share new papers we thank all contributors for their valuable efforts | awesome-list continual-learning knowledge-editing large-language-models llm llms natural-language-processing nlp paper pretrained-language-model refreshing retrieval-augmented-generation review survey update-llm | ai |
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