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Multiplatform-Mobile-App-Development-with-React-Native | multiplatform mobile app development with react native coursera multiplatform mobile app development with react native | react-native react-native-navigation redux redux-thunk react-native-vector-icons react-native-elements expo-mail-composer expo-image-picker expo-permissions expo-notification | front_end |
simple-opencv-kalman-tracker | simple opencv kalman tracker a simple ball tracker made using opencv to demonstrate the use of the kalman filter in computer vision you can find the full tutorial on robot home website http www robot home it blog en software ball tracker con filtro di kalman watch the demo working on youtube https www youtube com watch v sg h5onsj9s | ai |
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Dan-Jurafsky--Chris-Manning--NLP | dan jurafsky chris manning natural language processing 2012 this is my solution of the natural language processing coursera course made by dan jurafsky chris manning in winter 2012 this course was deleted from coursera database but you can get the videos from this youtube channel https www youtube com playlist list ploromvodv4rofzndyrlw3 ni7tmltmijz and you can get the slides from stanford website https web stanford edu jurafsky nlpcourseraslides html or you can download the whole course from this torrent link http academictorrents com details d2c8f8f1651740520b7dfab23438d89bc8c0c0ab assignments this course has 8 different assignments 1 spamlord 2 autocorrect 3 sentiment analysis 4 ner 5 pcfg 6 cky 7 ir 8 question answer each folder of these contains a compressed folder this folder contains the original assignment in case you wanted to re solve it | autocorrector autocorrection regular-expression regular-expressions sentiment-analysis sentiment-classification ner named-entity-recognition parsing parser cky-parser cky-algorithm cky information-retrieval information-extraction question-answering nlp nlp-machine-learning text-classification text-processing | ai |
Falcon | http i imgur com erzwdly png overview this is a responsive web framework i made it because i was bored feel free to help out components buttons http i imgur com 1rjcpaw png blockquotes http i imgur com 9y4s28x png pagination http i imgur com mgrnyhe png tables http i imgur com 0z1deqy png pricing tables http i imgur com iuk1nes png tabs http i imgur com k5ys2dm png panels http i imgur com xdfm09k png notifications http i imgur com bujmfzb png form elements http i imgur com vylt9zs png charts http i imgur com iulck38 png progress bars http i imgur com cfkktwm png links http i imgur com ihzgqdd png lists http i imgur com hrg4ud7 png accordions http i imgur com 3lenwi1 png breadcrumbs http i imgur com vd8tmhc png responsive images http i imgur com uqjwqz4 png headings http i imgur com cqbp2km png dividers http i imgur com mzcnptf png text colors http i imgur com yy6ckm3 png media objects http i imgur com 6d8x1sk png syntax highlighting http i imgur com bytrsos png links support thenewboston https www patreon com thenewboston thenewboston com https thenewboston com facebook https www facebook com thenewboston 464114846956315 twitter https twitter com bucky roberts google https plus google com buckyroberts reddit https www reddit com r thenewboston | front_end |
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DIT165-V16-Exercises | dit165 v16 exercises dit165 v16 design and development of embedded systems exercises all submitted exercises should include a header like the following file name exerc x y c or cpp date 2016 mm dd group number 33 members that contributed andreas aronsson darja linkova pooria balavi demonstration code ass code 1 4 abc important no code no bonus | os |
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seq2seq-anomaly-detection | this is no longer maintained seq2seq anomaly detection a natural language processing based approach to detect malicious http requests originally forked from seq2seq web attack detection https github com positivetechnologies seq2seq web attack detection model parameters batch size 128 the number of samples in a batch embed size 64 the dimension of embedding space should be less than vocabulary size hidden size 64 the number of hidden states in lstm num layers 2 the number of lstm blocks checkpoints path to checkpoint directory std factor 6 the number of stds that is used for defining a model threshold dropout 0 7 the probability that each element is kept vocab the vocabulary object in the parameters setting stage the threshold setting is introduced set threshold calculates the threshold value using mean and std of loss values of valid samples at the testing stage the model receives benign and anomalous samples for each sample the value of loss is calculated if this value is greater than the threshold then the request is considered anomalous sample data benign sample get vulnbank assets fonts pe icon 7 stroke woff d7yf1v http 1 1 host 10 0 212 25 connection keep alive origin http 10 0 212 25 user agent mozilla 5 0 x11 linux x86 64 applewebkit 537 36 khtml like gecko chrome 64 0 3282 186 safari 537 36 accept referer http 10 0 212 25 vulnbank assets css pe icon 7 stroke css accept encoding gzip deflate accept language en us en q 0 9 cookie phpsessid j1pavglp5ue30266c0j88ged30 anomalous sample post vulnbank online api php http 1 1 host 10 0 212 25 user agent mozilla 5 0 x11 ubuntu linux x86 64 rv 59 0 gecko 20100101 firefox 59 0 accept application json text javascript q 0 01 accept language en us en q 0 5 accept encoding gzip deflate referer http 10 0 212 25 vulnbank online login php content type application x www form urlencoded charset utf 8 x requested with xmlhttprequest content length 209 cookie phpsessid mlacs0uiou344i3fa53s7raut6 connection keep alive type user action create username jack and extractvalue 0x0a concat 0x0a select version and 1 1 password passw0rd firstname first lastname last birthdate 30 08 2017 email eee 40mail com phone 747474747 account de44404419569750553340 creditcard 4556 9373 3913 6510 | ai |
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KoPA | making large language models perform better in knowledge graph completion https img shields io badge version 1 0 1 blue license https img shields io github license mashape apistatus svg maxage 2592000 https github com zjukg kopa main license aaai https img shields io badge preprint 23 brightgreen https arxiv org abs 2310 06671 pytorch https img shields io badge pytorch 23ee4c2c svg e logo pytorch logocolor white https pytorch org making large language models perform better in knowledge graph completion https arxiv org abs 2310 06671 large language model llm based knowledge graph completion kgc aims to predict the missing triples in the kgs with llms and enrich the kgs to become better web infrastructure which can benefit a lot of web based automatic services however research about llm based kgc is limited and lacks effective utilization of llm s inference capabilities which ignores the important structural information in kgs and prevents llms from acquiring accurate factual knowledge in this paper we discuss how to incorporate the helpful kg structural information into the llms aiming to achieve structrual aware reasoning in the llms we first transfer the existing llm paradigms to structural aware settings and further propose a knowledge prefix adapter kopa to fulfill this stated goal kopa employs structural embedding pre training to capture the structural information of entities and relations in the kg then kopa informs the llms of the knowledge prefix adapter which projects the structural embeddings into the textual space and obtains virtual knowledge tokens as a prefix of the input prompt we conduct comprehensive experiments on these structural aware llm based kgc methods and provide an in depth analysis comparing how the introduction of structural information would be better for llm s knowledge reasoning ability model architecture model architecture figure model png dependencies our code is developed based on alpaca lora https github com tloen alpaca lora please build the python environment following the instruction in alpaca lora training test run kopa tuning shell export wandb disabled true wandb offline cuda visible devices 0 nohup python finetune kopa py base model your llm path data path data umls train json output dir your save path num epochs 3 lora r 32 learning rate 3e 4 batch size 12 micro batch size 12 num prefix 1 kge model data umls rotate pth lora target modules q proj k proj v proj o proj log txt you may need to fill the llm path and save path before running run inference shell cuda visible devices 0 python inference kopa py cite please condiser citing this paper if you use the code from our work thanks a lot bigquery misc zhang2023making title making large language models perform better in knowledge graph completion author yichi zhang and zhuo chen and wen zhang and huajun chen year 2023 eprint 2310 06671 archiveprefix arxiv primaryclass cs cl | instruction-tuning knowledge-graph knowledge-graph-completion knowledge-graph-embeddings large-language-models multi-modal multimodal-large-language-models | ai |
Devsnest-3.0-Portfolio | devsnest 3 0 portfolio personal portfolio which students can use to showcase their projects and web development skills we will be enhancing this portfolio as we progress in the course concepts learned in the course will be applied over here latest code changes main branch contains the lastest changes made to the portfolio day wise branches you can track the progress of the portfolio by viewing it s branches for example feature day1 represents the changes done on day 1 similarly feature day5 represents changes dones till the 5th day deployment the website can be hosted using netlify hosting portfolio on netlify https youtu be k23f4gj5bea t 6806 drag and drop hosting https docs netlify com site deploys create deploys drag and drop we are using netlify s form service to submit the contact me form steps to submit forms https docs netlify com forms setup | front_end |
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node-blockchain-server | node blockchain server this library is outdated you can find a similar example application as a part of the ethereumjs devp2p https github com ethereumjs ethereumjs devp2p package | blockchain |
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NLP | introduction to natural language processing friday march 2 2017 natural language processing nlp refers to the methods and technologies used to allow computers to process understand and perform tasks using human language common nlp tasks include sentiment analysis part of speech tagging named entity recognition machine translation document classification clustering and topic extraction this course will introduce fundamental concepts in nlp including word and document representation text processing document classification document similarity and clustering and dimensionality reduction the course will be taught using jupyter notebooks in python nlp tools covered will be sci kit learn and ntlk who this course is targeted primarily at graduate students and researchers who have some experience with machine learning and python but are new to nlp requirements participants must bring a laptop with a few specific software packages installed see pre workshop instructions instructions prerequisites a previous course in programming is strongly recommended experience with basic machine learning is recommended contact please mail ggaut uci edu mailto ggaut edu for more information a name schedule a tentative schedule time 8 30 9 00 sign in coffee bagels 9 00 10 30 text processing and document classification 10 30 10 45 break 10 45 1 00 document similarity and clustering a name syllabus a syllabus 1 introduction preparation common nlp tasks word and document representation text processing 2 document classification text processing tfidf evaluation 3 clustering document similartiy 4 dimensionality reduction topic modeling visualization a name instructions a pre workshop instructions you will need the following programs to run the jupyter notebook git python python modules see requirements txt python modules can be installed using either anaconda recommended for beginners or pip git you will need to install git https git scm com book en v2 getting started installing git after installing git run the following command to clone the workshop repository git clone https github com ucidatascienceinitiative nlp git python you will need python https www python org downloads installed if you do not already have python installed we recommend downloading anaconda which will include python and the modules required for this workshop the notebook will run with versions 3 5 and 2 9 anaconda this is the recommended method of installation for users newer to python or those that have not used pip anaconda https www continuum io downloads should have all required modules after installing anaconda run conda update conda to update all modules if the latentdirchletallocation method will not import it may help to update scikit learn by running conda update scikit learn pip to run this script you will need the packages listed in requirements txt to install run pip install r requirments txt in the command line operating systems i was able to get the notebook running using python 3 5 and 2 9 on both mac and windows machines if you are having trouble installing any of the required software please come to the workshop a few minutes early additionally we will have scheduled setup time to address any problems data the data are comments and metadata from two mental health subreddits r suicidewatch and r depression the data were filtered from this dataset https www reddit com r datasets comments 3bxlg7 i have every publicly available reddit comment to unzip the data run gunzip rc 2015 05 json gz slides if you would like to see the presentation in slide form rather than an ipython notebook you can run the following commands jupyter nbconvert to slides intronlp ipynb post serve | ai |
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design_pattern_for_embedded_system | design pattern for embedded system in c implement of all problem in book design patterns for embedded system in c | design-patterns embedded | os |
SciMLBook | parallel computing and scientific machine learning sciml methods and applications doi https zenodo org badge 205191601 svg https zenodo org badge latestdoi 205191601 this book is a compilation of lecture notes from the mit course 18 337j 6 338j parallel computing and scientific machine learning links to the old notes https mitmath github io 18337 will redirect here this repository is meant to be a live document updating to continuously add the latest details on methods from the field of scientific machine learning and the latest techniques for high performance computing to view this book go to book sciml ai https book sciml ai editing the sciml book this is a franklin jl site much of the material originated from julia markdown documents jmd each of these documents are weave jl ed with a custom template and the resulting html is inserted into a corresponding markdown file updating the files in weave will automatically update the webpages the theme is adapted from tufte css https edwardtufte github io tufte css | differential-equations scientific-machine-learning neural-networks numerical-methods gpu-computing stiff-equations lecture-notes performance-engineering parallelism scientific-simulators neural-ode neural-sde sciml | ai |
dr-ui | mapbox dr ui build status https travis ci com mapbox dr ui svg branch main https travis ci com mapbox dr ui pronounced doctor ui d ocumentation r eact ui components see mapbox mr ui https github com mapbox mr ui ui components for mapbox documentation projects this project is for internal mapbox usage the code is open source and we appreciate bug reports but we will only consider feature requests and pull requests from mapbox developers installation requirements node 18 npm 9 if you re not sure if your node and npm versions are up to date run nvm use before installing dependencies if you don t have nvm installed you can find installation instructions here https github com nvm sh nvm blob master readme md installing and updating npm install mapbox dr ui on mapbox projects pair these components with version 0 26 0 of mapbox s custom assembly https labs mapbox com assembly build this is not in peerdependencies because you might use link and script tags instead of the npm package the public assembly build should work fine with maybe one or two hiccups usage import individual components all components are exposed at mapbox dr ui component name for example js import card from mapbox dr ui card import backtotopbutton from mapbox dr ui back to top button only the component itself and whatever it depends on will be drawn into your bundle development see contributing md contributing md | docs docs-infrastructure | os |
Huatuo-Llama-Med-Chinese | readme md english readme en md p align center width 100 a href https github com scir hi huatuo llama med chinese target blank img src assets logo logo new png alt scir hi huatuo style width 60 min width 300px display block margin auto a p huatuo bentsao original name huatuo instruction tuning large language models with chinese medical knowledge code license https img shields io badge code 20license apache 2 0 green svg https github com scir hi huatuo llama med chinese blob main license python 3 9 https img shields io badge python 3 9 blue svg https www python org downloads release python 390 instruction tuning llama alpaca chinese bloom chatgpt api news 2023 09 12 arxiv https arxiv org pdf 2309 04198 pdf 2023 09 08 arxiv https arxiv org pdf 2309 04175 pdf 2023 08 07 https github com hit scir huozi 2023 08 05 ccl 2023 demo track poster 2023 08 03 scir https github com hit scir huozi 2023 07 19 bloom https huggingface co bigscience bloom 7b1 2023 05 12 2023 04 28 alpaca https github com ymcui chinese llama alpaca 2023 04 24 llama 2023 03 31 llama a quick start python 3 9 pip install r requirements txt lora 1 0 https github com hit scir huozi bloom 7b bloom 7b https huggingface co bigscience bloomz 7b1 alpaca chinese 7b https github com ymcui chinese llama alpaca llama llama 7b https huggingface co decapoda research llama 7b hf lora lora hugging face 1 lora https pan baidu com s 1bpndnb1wqztwy be6mfcna pwd m21s 2 bloom lora https pan baidu com s 1jpcueohesfgypzj7u52fag pwd scir hugging face https huggingface co lovepon lora bloom med bloom 3 alpaca lora https pan baidu com s 16oxcjzxnxjdpl8skihgnxw pwd scir hugging face https huggingface co lovepon lora alpaca med https pan baidu com s 1hddk84ashmzoflkmypbijw pwd scir hugging face https huggingface co lovepon lora alpaca med alldata 4 llama lora https pan baidu com s 1jih per6jzea6n2u6sumog pwd jjpf hugging face https huggingface co thinksoso lora llama med https pan baidu com s 1jadypclr2blyxituffsjpa pwd odsk hugging face https huggingface co lovepon lora llama literature lora lora folder name adapter config json lora adapter model bin lora chatglm chatglm 6b med https github com scir hi med chatglm infer data infer json infer bash scripts infer sh bash scripts infer literature single sh bash scripts infer literature multi sh infer sh base model lora lora weights instruct dir python infer py base model base model path lora weights lora weights path use lora true instruct dir infer data path prompt template template path bloom llama alpaca templates bloom deploy json templates med template json br templates literature template json scripts test sh cmekg https github com king yyf cmekg tools gpt3 5 prompt 2023 gpt3 5 data literature liver cancer json 1k p align center width 100 a href https github com scir hi huatuo llama med chinese target blank img src assets case png alt scir hi huatuo literature style width 100 min width 300px display block margin auto a p 16 https arxiv org pdf 2309 04198 pdf finetune data llama data json finetune bash scripts finetune sh llama a100 sxm 80gb 10 2h17m batch size 128 40g 3090 4090 24gb batch size wandb https wandb ai thinksoso llama med runs a5wgcnzt overview workspace user thinksoso 2023 3 llama alpaca bentsao 1 q a scir 2 q a llama alpaca 3 q a 4 q a llama alpaca bloom based based 5 q a requirements cuda lora llama based llama lora issue 6 q a https haochun wang https github com dyr1 https github com thinksoso https github com ruibai1999 https github com rootnx https github com imsovegetable https github com 1278882181 https github com jianyuchen01 https github com flowolfzzz http homepage hit edu cn stanzhao lang zh https github com hit scir huozi facebook llama https github com facebookresearch llama stanford alpaca https github com tatsu lab stanford alpaca alpaca lora by tloen https github com tloen alpaca lora cmekg https github com king yyf cmekg tools https yiyan baidu com welcome logo citation huatuo tuning llama model with chinese medical knowledge https arxiv org pdf 2304 06975 misc wang2023huatuo title huatuo tuning llama model with chinese medical knowledge author haochun wang and chi liu and nuwa xi and zewen qiang and sendong zhao and bing qin and ting liu year 2023 eprint 2304 06975 archiveprefix arxiv primaryclass cs cl knowledge tuning large language models with structured medical knowledge bases for reliable response generation in chinese https arxiv org pdf 2309 04175 pdf misc wang2023knowledgetuning title knowledge tuning large language models with structured medical knowledge bases for reliable response generation in chinese author haochun wang and sendong zhao and zewen qiang and zijian li and nuwa xi and yanrui du and muzhen cai and haoqiang guo and yuhan chen and haoming xu and bing qin and ting liu year 2023 eprint 2309 04175 archiveprefix arxiv primaryclass cs cl the calla dataset probing llms interactive knowledge acquisition from chinese medical literature https arxiv org pdf 2309 04198 pdf misc du2023calla title the calla dataset probing llms interactive knowledge acquisition from chinese medical literature author yanrui du and sendong zhao and muzhen cai and jianyu chen and haochun wang and yuhan chen and haoqiang guo and bing qin year 2023 eprint 2309 04198 archiveprefix arxiv primaryclass cs cl | llama llm medical nlp aidoctor medgpt medqa chinese bloom huozi | ai |
Neel.GPT | this is my attempt at creating my very own llm by myself without libraries like langchain | ai |
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embedded-inference | embedded inference snips voice platform an embedded and cloud independent spoken language understanding system for private by design voice interfaces | os |
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itseez-ss-2016-theory | 2016 itseez vk group 1 kornyakov video 1 opencv pisarevsky video 1 sidnev video 1 lysenkov video 1 osokin video 1 spizhevoy video 1 beynenson video 1 nosov video 1 shishkov video links itseez vk group http vk com itseez kornyakov video http vk com video 58356145 456239017 pisarevsky video http vk com video 58356145 456239018 sidnev video http vk com video 58356145 456239019 lysenkov video http vk com video 58356145 456239020 osokin video http vk com video 58356145 456239022 spizhevoy video http vk com video 58356145 456239024 nosov video http vk com video 58356145 456239025 beynenson video http vk com video 58356145 456239026 shishkov video http vk com video 58356145 456239027 | ai |
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MADProj | madproj y2s2 mobile application development module project | front_end |
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FreeRTOS-Demos-CHERI-RISC-V | risc v generic demo this demo is intended to be generic enough to run on different cheri risc v configurations multilibs platforms apps etc it has been tested on fpga f1 spike qemu piccolo and sail this repo demo is not standalone and is expected to be a submodule part of a classical freertos distribution https github com ctsrd cheri freertos tree hmka2 build the demo builds on linux based systems using waf build system there are two ways to build the demo either directly with waf or with cheribuild cheribuild makes it easier to build and install all the required toolchains and dependencies to build freertos with this demo to build cherifreertos freertos with cheri support to provide memory safety cheribuild knows how to build all the toolchain and dependencies with cheri support so it is advised to be used rather than building them yourself llvm the main cheri risc v development is based on llvm unlike while building with gcc newlib libc and compiler rt corresponds to libgcc need to be first built as dependencies for freertos cheribuild can build both for you and it is described below using cheribuild 1 clone cheribuild https github com ctsrd cheri cheribuild and checkout hmka2 branch git clone git github com ctsrd cheri cheribuild git b hmka2 cd cheribuild 2 build all the dependencies cheribuild requires in the readme pre build setup section vanilla risc v no cheri gcc and or llvm can be used to build vanilla risc v freertos 3 to build freertos with gcc make sure riscv64 unknown elf gcc is in your path cheribuild py freertos baremetal riscv64 freertos prog main servers freertos toolchain gcc freertos platform qemu virt d change main servers with your intended demo e g aws ota main blinky etc ignore the next steps if you don t want to build with llvm 4 to build this demo with llvm with all of its dependencies run cheribuild py freertos baremetal riscv64 freertos prog main servers freertos platform qemu virt d change main servers with your intended demo e g aws ota main blinky etc the d flag will build all dependencies this builds and installs llvm native newlib baremetal riscv64 compiler rt builtins baremetal riscv64 freertos baremetal riscv64 otherwise build each dependency separately cheribuild py llvm cheribuild py newlib baremetal riscv64 cheribuild py compiler rt builtins baremetal riscv64 cheribuild py freertos baremetal riscv64 freertos prog main servers freertos platform qemu virt 5 to run a demo on qemu install qemu if you don t have it already cheribuild py qemu run cheribuild py run freertos baremetal riscv64 run freertos prog main servers using waf clone freertos distribution along with its submodules git clone single branch branch hmka2 git github com ctsrd cheri freertos git recurse submodules cd freertos freertos demo risc v generic building with gcc waf configure program path program path program demo name toolchain gcc riscv platform platform build an example how to build main servers demo http cli ftp tftp to run on qemu virt platform waf configure program path demo servers program main servers toolchain gcc riscv platform qemu virt build an example how to build aws ota demo mqtt mbedtls tcp ip ota etc to run on qemu virt platform waf configure program path coremqtt agent program aws ota toolchain gcc riscv platform qemu virt build building with llvm assuming you built and installed both newlib and compiler rt yourself to path to newlib and path to compiler respectively ldflags l path to compilert waf configure program path program path program demo name riscv platform platform sysroot path to newlib build note that toolchain llvm is the default hence it is not passed an example how to build main servers demo http cli ftp tftp to run on qemu virt platform ldflags l path to compilert waf configure program path demo servers program main servers riscv platform qemu virt sysroot path to newlib build an example how to build aws ota demo mqtt mbedtls tcp ip ota etc to run on qemu virt platform ldflags l path to compilert waf configure program path coremqtt agent program aws ota riscv platform qemu virt sysroot path to newlib build running on qemu qemu system riscv64 m virt m 2048 nographic bios build risc v generic main servers elf device virtio net device netdev net0 netdev user id net0 ipv6 off hostfwd tcp 10021 21 hostfwd udp 10069 69 hostfwd tcp 10023 23 hostfwd tcp 8080 80 replace the elf file with the program you built if it is not main servers elf todo list cheribuild s freertos options build with cheri support build with compartmentalization support build and run oh other hw targets e g f1 | os |
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rasp | design and development of a consensus algorithm on embedded systems this repo contains the implementation of my bsc thesis work that is accessible here https kristianbrunn com documents bsc thesis pdf | raft-consensus raft-algorithm esp8266 esp8266-arduino bsc-thesis | os |
are-we-learning-yet | are we learning yet rust is a systems programming language but is it a machine learning language arewelearningyet com http arewelearningyet com inspired by are we web yet http arewewebyet org this project aims to catalog the the rust ml ecosystem contributing feedback issues and pull requests are welcome and appreciated for adding missing crates providing additional resources or improving the content running locally requires cobalt 0 17 5 https cobalt org github io recommend just https github com casey just as a task runner or using the commands in the justfile justfile github oauth token avoids 403 rate limiting errors while generated crate data tip set in env file and just will pick it up automatically export github token your github token scrape crate repo data for sitegen just scrape start a dev server on port 3000 cobalt serve the site should be running on localhost 3000 http localhost 3000 how it works the repo consists of 2 key parts the scraper tool is responsible for reading crates yaml data crates yaml fetching additional metadata about the crate from crates io and the github api and generating a score used for ordering crates the fetched data is cached in a tmp directory to speed up repeated site generation and avoid abusing the apis and the scraper outputs data crates generated yaml which is used by the cobalt site generation to force regeneration of all crate data remove the cached data with just clean for more details see the scraper readme scraper the site content is the rest of the repo which follows the layout established by cobalt rs https cobalt org github io cobalt uses this content to generate a static site into a site directory cobalt build and can also start a local development server that rebuilds the site anytime content changes cobalt serve for more details see cobalt rs https cobalt org github io documentation note cobalt serve does not trigger the scraper to rerun when crates yaml or the scraper changes currently you must rerun just scrape to update crates generated yaml publishing arewelearningyet com arewelearningyet com is served by github pages every merge into master is automatically published by a github actions job github workflows additionally to ensure crate statistics download counts and stars are regularly updated the publishing task is also run as a weekly cron job | machine-learning | ai |
Settle-lah-mobile-application | final year project for diploma in information technology dit2166 it project final report settle lah mobile application java settle lah is a mobile application that are responsive which means that it will be able to support multiple interfaces of device and the name settle lah is a malaysian slang which has a meaning of job is done settle lah is derived from the idea that everyone can hire any services that they want quickly safely reliably and most importantly it is cost free in the app store there is no proper application currently that can handle repair work according to the warranty period this application will only be provided to new house owners with a house warranty as payment does not involve in the entire process image https user images githubusercontent com 56108922 176214402 93bf6c8a a25b 4aae 877b 923d47bcf559 png through this application the house owner that discovered faulty workmanship will be able to report those defects through the app and repair work will be scheduled this app will help with providing new house owners a platform to report their problems into the application according to their housing area settle lah is also a platform for admins management team of development to manage and to have an overview of the repair work that is requested by the house owner settle lah can also enhance the user s aftersales service as they can provide better customer service through the application and the user only needs to report through the app finally through the application the developers can also check that whether the repair work being requested is under warranty or not and if the repair work is not under the criteria of warranty the process won t go through the goal is to provide quality customer service and fulfill a client s request efficiently and effectively poster for settle lah settle lah poster https user images githubusercontent com 56108922 179388176 d4e04fc4 d59a 4bdb ac02 3930b7c7101b png | server |
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Seven-Segment-OCR | optimizer a seven segment digits ocr optimizer is a seven segment digits ocr class project carried out by alex https github com alexmomeni priscille https github com priscilleb charlotte https github com charlottecaucheteux and sacha https github com sachaizadi objective img src img pb png height 200 the aim of the project is to digitize the monitoring of mines activities we focused on the gas and lubricant consumption of vehicles within the mines the idea is to build computer vision model that would enable operators to take a picture of the gas pump with their smartphones and automatically log the value of the gas transaction we were given 850 pictures of varying quality of the gas pump with their associated values img src img product png height 200 approaches img src img approaches png height 150 img src img nn png height 150 we tried 2 different approaches 1 the digit per digit approach 1 image processing identify the screen crop the picture grayscale thresholding localize digits and crop them 2 learning phase learn a mnist model that predicts each digit individually 3 inference phase pass each cropped digit to the mnist model and append the results 2 the end to end approach 1 image processing identify the screen crop the picture grayscale and thresholding 2 learning phase learn a model that predicts all digits at once we based our model on the multi digit number recognition from street view imagery using deep convolutional neural networks https static googleusercontent com media research google com fr pubs archive 42241 pdf paper by goodfellow al the idea is to build a convnet that simultaneously learns i the digits and ii where to look for them results img src img results1 png height 200 img src img results2 png height 200 how to reproduce our work and run our models tbc 1 preprocessing python frame extractor py 2 preprocessing python digits cut py 3 model python main py | machine-learning computer-vision deep-neural-networks deep-learning computer-vision-ai hec polytechnique ocr classification-model data-science-for-business-x-hec | ai |
ubuntu-cloud-images | ubuntu cloud images ubuntu cloud images are pre installed disk images that have been customized by ubuntu engineering to run on cloud platforms such as amazon ec2 openstack windows and lxc hadoop image install package add apt repository ppa webupd8team java apt get install oracle java6 installer oracle java6 set default add apt repository ppa hadoop ubuntu stable apt get install hadoop hbase hive pig | cloud |
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RetNet | retentive network pytorch implementation of retentive network a successor to transformer for large language models https arxiv org pdf 2307 08621 pdf references misc sun2023retentive title retentive network a successor to transformer for large language models author yutao sun and li dong and shaohan huang and shuming ma and yuqing xia and jilong xue and jianyong wang and furu wei year 2023 eprint 2307 08621 archiveprefix arxiv primaryclass cs cl | ai |
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Chatr | lab 4 chatr chatr is a chat app using an open source parse http parseplatform org backend time spent 7 hours spent in total user stories the following required user stories are complete x user can sign up and sign in to the login screen 1pt x user sees alerts for login exceptions i e account already exists wrong credentials etc 1pt x user can compose and send chat messages 2pt x user can view a list of chat messages in chronological order 2pt x automatically adjust cell size to fit text 1pt x username of chat author is displayed in each chat message 2pt x persist logged in user 1pt the following stretch user stories are implemented user sees an activity indicator while waiting for authentication 1pt x user can pull to refresh chat feed 1pt add an adorable avatar for each user by requesting an avatar from the adorable avatars api https github com adorableio avatars api 2pt chat bubble style design 3pt expand or contract the cell layout as needed to show the chat message author user if it exists 2pt the following additional user stories are implemented x added additional error alerts for when user sign s up their account 1 3pts x created color template for the app custom app icon and custom launch screen so the app has a unique and fliud look please list two areas of the assignment you d like to discuss further with your peers during the next class examples include better ways to implement something how to extend your app in certain ways etc 1 how to implement the adorable avatars 2 how to change to bubble design video walkthrough here s a walkthrough of implemented user stories img src https i imgur com yzupod8 gif title video walkthrough width alt video walkthrough if walkthrough does not work in readme file click here to open gif in browser https imgur com yzupod8 notes no errors encountered license copyright 2018 andriana aivazians licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | server |
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Upcharika | p align center img src readme assets banner jpg alt banner width 540 height 360 p a unique flutter application aimed at helping people getting their vitals using photoplethysmography and computer vision current goals use the camera and flash sensors to calculate heart rate use the camera and flash sensors to calcuate sp02 make a dashboard for storing periodic readings x user profile login page sign up page splash screen firebase supabase intergration for authentication generate reports our hard working project maintainers https avatars githubusercontent com u 46641503 v 4 https avatars githubusercontent com u 40017559 v 4 smaranjit ghose https github com smaranjitghose anush bhatia https github com anushbhatia our valuable contributors a href https github com smaranjitghose upcharika graphs contributors img src https contributors img web app image repo smaranjitghose upcharika a documents related to the project license license code of conduct code of conduct md contribution guidelines contributing md changelog changelog md references 1 kanva a k sharma c j and deb s 2014 january determination of spo 2 and heart rate using smartphone camera in proceedings of the 2014 international conference on control instrumentation energy and communication ciec pp 237 241 ieee 2 bolkhovsky j b scully c g and chon k h 2012 august statistical analysis of heart rate and heart rate variability monitoring through the use of smart phone cameras in 2012 annual international conference of the ieee engineering in medicine and biology society pp 1610 1613 ieee 3 liu i ni s and peng k 2020 happiness at your fingertips assessing mental health with smartphone photoplethysmogram based heart rate variability analysis telemedicine and e health 26 12 pp 1483 1491 | photoplethysmography computer-vision flutter ppg heartratemonitor spo2 | ai |
MADLectureNotes | mad lecture notes mobile application development with android course lecture notes and related materials table of contents 01 outline 02 introduction to mobile application development 03 application fundamentals 04 managing lifecycle and state lab assignments you can find lab assignments in our wiki page here https github com accavdar mad lecture notes wiki lab assignments projects ideas you can find project ideas in our wiki page here https github com accavdar mad lecture notes wiki project ideas google group please subscribe our google group from here https groups google com forum forum bbm422 mad | front_end |
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wordfuzz | wordfuzz natural language processing with datamuse and wordnet license mit https img shields io badge license mit yellow svg https opensource org licenses mit codecov https codecov io gh molamk wordfuzz branch master graph badge svg https codecov io gh molamk wordfuzz build status https travis ci com molamk wordfuzz svg branch master https travis ci com molamk wordfuzz maintainability https api codeclimate com v1 badges 29acd35d15206058f8ff maintainability https codeclimate com github molamk wordfuzz maintainability known vulnerabilities https snyk io test github molamk wordfuzz badge svg targetfile package json https snyk io test github molamk wordfuzz targetfile package json npm scoped https img shields io npm v wordfuzz svg https www npmjs com package wordfuzz npm bundle size minified https img shields io bundlephobia min wordfuzz svg https www npmjs com package wordfuzz install dependency bash with npm npm install save wordfuzz with yarn yarn add wordfuzz quickstart javascript const fuzz require wordfuzz fuzz meanslike develop endswith m max 1 ask then console log word transform score 17905 tags v todo complete datamuse integration provide option to communicate with local wordnet generate sentences instead of just words | nlp word rhyme meaning datamuse | ai |
CheckUp | checkup alt text logo logo http files softicons com download system icons android friends icons by rich d png 32 android png android java an anroid app where doctors and patients can interact with each other it enables the patient to book appointments locate nearby doctors edit their profiles as well as submit inquiries to doctors the doctors can not only view and reply to the patient inquiries but also view their upcoming appointments both doctors and patients can view their message history with their associated sender however all messages can be viewed by the admin user accounts are created and edited by the admin as well a cashier s role is to remind patients with an overdue balance features user profile for doctors patients cashier and admin the admin will register the doctors patients and cashiers since health is important the doctors are verified by the admin during registration patients can choose to register online or by the admin they can edit their profile details however the admin can update accounts for all users of the app track nearby doctors maps api patients can track doctors located near them this is done by inserting the postal code or address in the search bar the results will provide a list of doctors located nearby and upon selection display their details track patient records the app will keep a record of the patient s history which can be viewed anytime booking and support the patient will be able to check the availability of the doctor and book an appointment accordingly they can also get online help by messaging the doctor the doctors can post solutions to these problems payments payments are accessible to patient reminders are sent to the overdue accounts payments are determined if the user is covered by msp navigation it allows the user to navigate between different activities such as the home page and logout the apps core functionality allows patients to search for nearby doctors and either submit non emergency inquiries or request appointments based on their availability doctors to view upcoming appointments with their patients read and respond to patient inquiries administrators to create user accounts and handle general account responsibilities such as password resets cashiers to payment process contributers developed by darren singh https github com darrensingh shivangi patel https github com ir0nm9n bokki min https github com ama cantabile tonny ogange https github com t iro | front_end |
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steem-python | official python steem library steem python is the official steem library for python it comes with a bip38 encrypted wallet and a practical cli utility called steempy this library currently works on python 2 7 3 5 and 3 6 python 3 3 and 3 4 support forthcoming installation with pip pip3 install steem pip install steem for 2 7 from source git clone https github com steemit steem python git cd steem python python3 setup py install python setup py install for 2 7 homebrew build prereqs if you re on a mac you may need to do the following first brew install openssl export cflags i brew prefix openssl include cflags export ldflags l brew prefix openssl lib ldflags cli tools bundled the library comes with a few console scripts steempy rudimentary blockchain cli needs some tlc and more tlas steemtail useful for e g steemtail f j jq unbuffered sort keys documentation documentation is available at http steem readthedocs io tests some tests are included they can be run via python setup py test todo more unit tests 100 documentation coverage consistent documentation migrate to click cli library notice this library is under development beware | steem steemit steem-blockchain | blockchain |
data-engineering-miniproject | data engineering miniproject a data engineering pipeline simulating data as source and moving them to a nosql database | server |
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text-generation-webui-colab-- | a colab gradio web ui for running large language models google colab web ui please follow me for new updates https twitter com camenduru br please join our discord server https discord gg k5bwmmvjju br please join my patreon community https patreon com camenduru br wip who to use click opne in colab on left then run it on your own colab opne in colab colab notebook colab colab info model page open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main vicuna 13b gptq 4bit 128g ipynb vicuna 13b gptq 4bit 128g br https vicuna lmsys org open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main vicuna 13b 1 1 gptq 4bit 128g ipynb vicuna 13b 1 1 gptq 4bit 128g br https vicuna lmsys org open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main stable vicuna 13b gptq 4bit 128g ipynb stable vicuna 13b gptq 4bit 128g br https huggingface co carperai stable vicuna 13b delta open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main gpt4 x alpaca 13b native 4bit 128g ipynb gpt4 x alpaca 13b native 4bit 128g br https huggingface co chavinlo gpt4 x alpaca open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main pyg 7b gptq 4bit 128g ipynb pyg 7b gptq 4bit 128g br https huggingface co neko institute of science pygmalion 7b open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main koala 13b gptq 4bit 128g ipynb koala 13b gptq 4bit 128g br https bair berkeley edu blog 2023 04 03 koala open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main oasst llama13b gptq 4bit 128g ipynb oasst llama13b gptq 4bit 128g br https open assistant io open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main wizard lm uncensored 7b gptq 4bit 128g ipynb wizard lm uncensored 7b gptq 4bit 128g br https github com nlpxucan wizardlm open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main mpt storywriter 7b gptq 4bit 128g ipynb mpt storywriter 7b gptq 4bit 128g br https www mosaicml com open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main wizard lm uncensored 13b gptq 4bit 128g ipynb wizard lm uncensored 13b gptq 4bit 128g br https github com nlpxucan wizardlm open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main pyg 13b gptq 4bit 128g ipynb pyg 13b gptq 4bit 128g br https huggingface co pygmalionai pygmalion 13b open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main falcon 7b instruct gptq 4bit ipynb falcon 7b instruct gptq 4bit br https falconllm tii ae open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main wizard lm 13b 1 1 gptq 4bit 128g ipynb wizard lm 13b 1 1 gptq 4bit 128g br https github com nlpxucan wizardlm open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main llama 2 7b chat gptq 4bit ipynb llama 2 7b chat gptq 4bit 4bit br https ai meta com llama open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main llama 2 13b chat gptq 4bit ipynb llama 2 13b chat gptq 4bit 4bit br https ai meta com llama br wip please try llama 2 13b chat or llama 2 7b chat or llama 2 7b chat gptq 4bit open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main llama 2 7b chat ipynb llama 2 7b chat 16bit br https ai meta com llama open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main llama 2 13b chat ipynb llama 2 13b chat 8bit br https ai meta com llama open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main redmond puffin 13b gptq 4bit ipynb redmond puffin 13b gptq 4bit 4bit br https huggingface co nousresearch redmond puffin 13b open in colab https colab research google com assets colab badge svg https colab research google com github camenduru text generation webui colab blob main nous hermes 13b gptq 4bit ipynb nous hermes 13b gptq 4bit 4bit br https huggingface co nousresearch nous hermes llama2 13b colab pro according to the facebook research llama license non commercial bespoke license maybe we cannot use this model with a colab pro account but yann lecun said gpl v3 https twitter com ylecun status 1629189925089296386 i am a little confused is it possible to use this with a non free colab pro account tutorial https www youtube com watch v kga7eku1xua text generation web ui https github com oobabooga text generation webui https github com oobabooga text generation webui thanks to oobabooga models license model license vicuna 13b gptq 4bit 128g from https vicuna lmsys org the online demo is a research preview intended for non commercial use only subject to the model license https github com facebookresearch llama blob main model card md of llama terms of use of the data generated by openai and privacy practices of sharegpt please contact us if you find any potential violation the code is released under the apache license 2 0 gpt4 x alpaca 13b native 4bit 128g https huggingface co chavinlo alpaca native https huggingface co chavinlo alpaca 13b https huggingface co chavinlo gpt4 x alpaca llama 2 https ai meta com llama llama 2 is available for free for research and commercial use special thanks thanks to facebookresearch for https github com facebookresearch llama br thanks to lmsys for https huggingface co lmsys vicuna 13b delta v0 br thanks to anon8231489123 for https huggingface co anon8231489123 vicuna 13b gptq 4bit 128g gptq 4bit quantization of https huggingface co lmsys vicuna 13b delta v0 br thanks to tatsu lab for https github com tatsu lab stanford alpaca br thanks to chavinlo for https huggingface co chavinlo gpt4 x alpaca br thanks to qwopqwop200 for https github com qwopqwop200 gptq for llama br thanks to tsumeone for https huggingface co tsumeone gpt4 x alpaca 13b native 4bit 128g cuda gptq 4bit quantization of https huggingface co chavinlo gpt4 x alpaca br thanks to transformers for https github com huggingface transformers br thanks to gradio app for https github com gradio app gradio br thanks to thebloke for https huggingface co thebloke stable vicuna 13b gptq br thanks to neko institute of science for https huggingface co neko institute of science pygmalion 7b br thanks to gozfarb for https huggingface co gozfarb pygmalion 7b 4bit 128g cuda gptq 4bit quantization of https huggingface co neko institute of science pygmalion 7b br thanks to young geng for https huggingface co young geng koala br thanks to thebloke for https huggingface co thebloke koala 13b gptq 4bit 128g gptq 4bit quantization of https huggingface co young geng koala br thanks to dvruette for https huggingface co dvruette oasst llama 13b 2 epochs br thanks to gozfarb for https huggingface co gozfarb oasst llama13b 4bit 128g gptq 4bit quantization of https huggingface co dvruette oasst llama 13b 2 epochs br thanks to ehartford for https huggingface co ehartford wizardlm 7b uncensored br thanks to thebloke for https huggingface co thebloke wizardlm 7b uncensored gptq gptq 4bit quantization of https huggingface co ehartford wizardlm 7b uncensored br thanks to mosaicml for https huggingface co mosaicml mpt 7b storywriter br thanks to occamrazor for https huggingface co occamrazor mpt 7b storywriter 4bit 128g gptq 4bit quantization of https huggingface co mosaicml mpt 7b storywriter br thanks to ehartford for https huggingface co ehartford wizardlm 13b uncensored br thanks to ausboss for https huggingface co ausboss wizardlm 13b uncensored 4bit 128g gptq 4bit quantization of https huggingface co ehartford wizardlm 13b uncensored br thanks to pygmalionai for https huggingface co pygmalionai pygmalion 13b br thanks to notstoic for https huggingface co notstoic pygmalion 13b 4bit 128g gptq 4bit quantization of https huggingface co pygmalionai pygmalion 13b br thanks to wizardlm for https huggingface co wizardlm wizardlm 13b v1 1 br thanks to thebloke for https huggingface co thebloke wizardlm 13b v1 1 gptq gptq 4bit quantization of https huggingface co wizardlm wizardlm 13b v1 1 br thanks to meta llama for https huggingface co meta llama llama 2 7b chat hf br thanks to thebloke for https huggingface co thebloke llama 2 7b chat gptq gptq 4bit quantization of https huggingface co meta llama llama 2 7b chat hf br thanks to meta llama for https huggingface co meta llama llama 2 13b chat hf br thanks to localmodels for https huggingface co localmodels llama 2 13b chat gptq gptq 4bit quantization of https huggingface co meta llama llama 2 13b chat hf br thanks to nousresearch for https huggingface co nousresearch redmond puffin 13b br thanks to thebloke for https huggingface co thebloke redmond puffin 13b gptq gptq 4bit quantization of https huggingface co nousresearch redmond puffin 13b br thanks to nousresearch for https huggingface co nousresearch nous hermes llama2 13b gptq 4bit quantization https huggingface co nousresearch nous hermes llama2 13b gptq br | ai |
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fand-y007.github.io | fand y007 information technology | server |
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llano | p align center img src assets llano logo jpeg style width 300px p p align center let strong style color 0a93f5 l strong arge strong style color 0a93f5 lan strong guage models serve as data strong style color 0a93f5 anno strong tators p p align center zero shot few shot information extractor p h4 align center a href https github com seanlee97 llano blob main license img src https img shields io badge license apache 2 0 blue svg alt llano is released under the apache 2 0 license a a href https pypi org project llano img src https badge fury io py llano svg alt pypi version a a href http makeapullrequest com img src https img shields io badge prs welcome brightgreen svg style flat square alt http makeapullrequest com a a href https discord gg fqvfncf9k8 img src https img shields io badge discord community orange alt community a h4 installation stable bash python m pip install u llano for chinese users the index url can be specified for a faster installation bash python m pip install i https pypi tuna tsinghua edu cn simple u llano latest bash python m pip install git https github com seanlee97 llano git currently supports python3 8 due to python 3 7 s end of life https endoflife date python on june 27 2023 we no longer support it features converts unstructured data into structured data using powerful llms large language models supports zero shot few shot information extraction provides annotated data that can be used for further training or annotation refinement api is simple to use and out of the box supports a wide range of tasks supports multilingual prompts supporting tasks task name supporting languages status ner english en simplifed chinese zh cn text classification binary multiclass english en simplifed chinese zh cn multilabel classification english en simplifed chinese zh cn data augmentation english en simplifed chinese zh cn relation extraction english en simplifed chinese zh cn summarization text to sql quick tour examples english example python from llano config import tasks languages openaimodels nerformatter from llano import gptmodel gptannotator print all supported tasks tasks list attributes print all supported languages languages list attributes print all supported nerformatter nerformatter list attributes print all supported openaimodels openaimodels list attributes api key your api key model gptmodel api key model openaimodels chatgpt annotator gptannotator model task tasks ner language languages en label mapping people peo location loc company com organization org job job doc elon reeve musk frs i l n ee lon born june 28 1971 is a business magnate and investor he is the founder ceo and chief engineer of spacex angel investor ceo and product architect of tesla inc owner and ceo of twitter inc founder of the boring company co founder of neuralink and openai and president of the philanthropic musk foundation w o hint w o formatted result ret annotator doc w o hint w formatted result ret annotator doc formatter nerformatter bio w hint w formatted result ret annotator doc hint the entity type job is job title such as ceo founder boss formatter nerformatter bio result is the annotation result formatted result is the formatted result tip if you want to train your domain model you can use the formatted result details summary click to show the result summary json request prompt you are a ner named entity recognition system please help me with the ner task ntask extract the entities and corresponding entity types from a given sentence nonly support 5 entity types including people location company organization job n nexplanation and examples the entity type job is job title such as ceo founder boss n noutput format entity entity type n nfollowing is the given sentence elon reeve musk frs i l n ee lon born june 28 1971 is a business magnate and investor he is the founder ceo and chief engineer of spacex angel investor ceo and product architect of tesla inc owner and ceo of twitter inc founder of the boring company co founder of neuralink and openai and president of the philanthropic musk foundation noutput meta role assistant prompt tokens 195 completion tokens 74 total tokens 269 taken time 4 87583 response n n elon reeve musk people frs job spacex company tesla inc company twitter inc company the boring company organization neuralink organization openai organization musk foundation organization result text elon reeve musk frs i l n ee lon born june 28 1971 is a business magnate and investor he is the founder ceo and chief engineer of spacex angel investor ceo and product architect of tesla inc owner and ceo of twitter inc founder of the boring company co founder of neuralink and openai and president of the philanthropic musk foundation entities 0 15 elon reeve musk peo 16 19 frs job 139 145 spacex com 192 203 tesla inc com 222 235 twitter inc com 248 266 the boring company org 282 291 neuralink org 296 302 openai org 339 354 musk foundation org formatted result e tb peo nl ti peo no ti peo nn ti peo n ti peo nr ti peo ne ti peo ne ti peo nv ti peo ne ti peo n ti peo nm ti peo nu ti peo ns ti peo nk ti peo n to nf tb job nr ti job ns ti job n to n to n to n to ni to n to nl to n to nn to n to n to ne to ne to n to nl to no to nn to n to n to nb to no to nr to nn to n to nj to nu to nn to ne to n to n2 to n8 to n to n to n1 to n9 to n7 to n1 to n to n to ni to ns to n to na to n to nb to nu to ns to ni to nn to ne to ns to ns to n to nm to na to ng to nn to na to nt to ne to n to na to nn to nd to n to ni to nn to nv to ne to ns to nt to no to nr to n to n to nh to ne to n to ni to ns to n to nt to nh to ne to n to nf to no to nu to nn to nd to ne to nr to n to n to nc to ne to no to n to na to nn to nd to n to nc to nh to ni to ne to nf to n to ne to nn to ng to ni to nn to ne to ne to nr to n to no to nf to n to ns tb com np ti com na ti com nc ti com ne ti com nx ti com n to n to na to nn to ng to ne to nl to n to ni to nn to nv to ne to ns to nt to no to nr to n to n to nc to ne to no to n to na to nn to nd to n to np to nr to no to nd to nu to nc to nt to n to na to nr to nc to nh to ni to nt to ne to nc to nt to n to no to nf to n to nt tb com ne ti com ns ti com nl ti com na ti com n ti com n ti com ni ti com nn ti com nc ti com n ti com n to n to no to nw to nn to ne to nr to n to na to nn to nd to n to nc to ne to no to n to no to nf to n to nt tb com nw ti com ni ti com nt ti com nt ti com ne ti com nr ti com n ti com n ti com ni ti com nn ti com nc ti com n ti com n to n to nf to no to nu to nn to nd to ne to nr to n to no to nf to n to nt tb org nh ti org ne ti org n ti org nb ti org no ti org nr ti org ni ti org nn ti org ng ti org n ti org nc ti org no ti org nm ti org np ti org na ti org nn ti org ny ti org n to n to nc to no to n to nf to no to nu to nn to nd to ne to nr to n to no to nf to n to nn tb org ne ti org nu ti org nr ti org na ti org nl ti org ni ti org nn ti org nk ti org n to na to nn to nd to n to no tb org np ti org ne ti org nn ti org na ti org ni ti org n to n to na to nn to nd to n to np to nr to ne to ns to ni to nd to ne to nn to nt to n to no to nf to n to nt to nh to ne to n to np to nh to ni to nl to na to nn to nt to nh to nr to no to np to ni to nc to n to nm tb org nu ti org ns ti org nk ti org n ti org nf ti org no ti org nu ti org nn ti org nd ti org na ti org nt ti org ni ti org no ti org nn ti org n to n to details chinese example python from llano config import tasks languages openaimodels nerformatter from llano import gptmodel gptannotator print all supported tasks tasks list attributes print all supported languages languages list attributes print all supported nerformatter nerformatter list attributes print all supported openaimodels openaimodels list attributes api keys your api keys model gptmodel api keys model openaimodels chatgpt annotator gptannotator model task tasks ner language languages zh cn label mapping peo loc com org id doc elon reeve musk 107 1971 6 28 1995 2002 zip2 x com paypal ret annotator doc w o hint w o formatter ret annotator doc formatter nerformatter bio w o hint w formatter ret annotator doc hint formatter nerformatter bio w o hint w formatter details summary click to show the result summary json request prompt ner ner n n 5 n n n n n n elon reeve musk 107 1971 6 28 1995 2002 zip2 x com paypal n meta role assistant prompt tokens 346 completion tokens 103 total tokens 449 taken time 4 54531 response zip2 x com paypal result text elon reeve musk 107 1971 6 28 1995 2002 zip2 x com paypal entities 0 9 peo 48 50 loc 73 82 id 88 90 loc 113 120 org 173 177 zip2 com 184 189 x com com 192 198 paypal com formatted result tb peo n ti peo n ti peo n ti peo n ti peo n ti peo n ti peo n ti peo n ti peo n to ne to nl to no to nn to n to nr to ne to ne to nv to ne to n to nm to nu to ns to nk to n to n to n to n1 to n0 to n7 to n to n to n to n to n1 to n9 to n7 to n1 to n to n6 to n to n2 to n8 to n to n to n to n to n tb loc n ti loc n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n tb id n ti id n ti id n ti id n ti id n ti id n ti id n ti id n ti id n to n to n to n to n to n to n tb loc n ti loc n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n tb org n ti org n ti org n ti org n ti org n ti org n ti org n to n to n to n to n to n to n to n to n to n to n to n to n to n1 to n9 to n9 to n5 to n to n to n2 to n0 to n0 to n2 to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to n to nz tb com ni ti com np ti com n2 ti com n to n to n to n to n to n to n to nx tb com n ti com nc ti com no ti com nm ti com n to n to n to np tb com na ti com ny ti com np ti com na ti com nl ti com n to n to details cli wip wip contribution p align center contributions are always welcome br welcome to join our community p div align center a href https discord gg fqvfncf9k8 img alt join us on discord src https img shields io discord 1081865058306490469 style for the badge a div | annotataion chatgpt large-language-models llm nlp openai prompt-engineering gpt ner prompt classification relation-extraction data-augmentation few-shot information-extraction zero-shot gpt-3 gpt-4 annotator chatie | ai |
web-development-resources | p align center a href https github com milanaryal web development resources img width 64 height 64 alt web development resources src https user images githubusercontent com 9361180 86557412 c37c3f00 bf75 11ea 8503 b42bd67646b2 png a p h1 align center web development resources h1 p align center i a list of open source front end tools and resources for web designers and developers i p p align center br p table of contents wiki wiki frameworks frameworks mobile web apps frameworks mobile web apps frameworks front end components front end components html5 themes html5 themes html preprocessor html preprocessor css preprocessor css preprocessor javascript preprocessor javascript preprocessor sass framework sass frameworks node js feature module and bundler nodejs feature module and bundler javascript library javascript library vanilla javascript plugins vanilla javascript plugins jquery plugins jquery plugins javascript compressor toolkit javascript compressor toolkit css library css library svg png and font icons library svg png and font icons library html forms html forms web tools web tools cdn library cdn library browser compatibility wiki browser compatibility wiki responsiveness testing tools responsiveness testing tools performance testing tools performance testing tools placeholder placeholder mockup placeholder mockup placeholder website icons for browsers website icons for browsers free inspirational snippets and tutorials free inspirational snippets and tutorials css reference css reference css tutorials css tutorials infographics infographics design inspiration design inspiration the type system the type system webmaster tools webmaster tools package manager package manager jamstack jamstack list of lists of lists list of lists of lists wiki name description w3c standards https www w3 org standards w3c standards define an open web platform for application development that has the unprecedented potential to enable developers to build rich interactive experiences powered by vast data stores that are available on any device cssdb https cssdb org a comprehensive list of css features and their positions in the process of becoming implemented web standards mdn web docs https developer mozilla org en us resources for developers by developers google web developers https developers google com web build the next generation of web experiences w3schools https www w3schools com online web tutorials css tricks https css tricks com daily articles about css html javascript and all things related to web design and development smashing magazine https www smashingmagazine com for web designers and developers webglossary info https webglossary info extensive glossary of web development and design terms p align right a href table of contents b back to top b a p frameworks name description html5 boilerplate https html5boilerplate com the web s most popular front end template bootstrap http getbootstrap com bootstrap is the most popular html css and js framework for developing responsive mobile first projects on the web foundation http foundation zurb com the most advanced responsive front end framework in the world base http getbase org a rock solid responsive html css framework basscss http www basscss com low level css toolkit bulma http bulma io a modern css framework based on flexbox concise framework http concisecss com a lightweight front end framework that provides a number of great features without the bloat cardinal http cardinalcss com cardinal is a modular mobile first css framework built with performance and scalability in mind furtive css http furtive co a forward thinking css micro framework juiced http juicedcss com a flexbox css framework material design lite http www getmdl io an implementation of material design components in vanilla css js and html materialize http materializecss com a modern responsive front end framework based on material design photon http photonkit com the fastest way to build beautiful electron apps using simple html and css primer css https primer style css the github design systems team builds and maintains primer pure http purecss io a set of small responsive css modules that you can use in every web project responsive http responsivebp com a powerful accessible developer friendly framework for building responsive websites semantic ui http semantic ui com a development framework that helps create beautiful responsive layouts using human friendly html skel https github com n33 skel a lightweight responsive framework for the www skeleton http getskeleton com a dead simple responsive boilerplate tailwind css https tailwindcss com a utility first css framework for rapidly building custom designs uikit http getuikit com a lightweight and modular front end framework for developing fast and powerful web interfaces p align right a href table of contents b back to top b a p mobile web apps frameworks name description onsen ui https onsen io the most beautiful and efficient way to develop html5 hybrid and mobile web apps p align right a href table of contents b back to top b a p front end components name description formstone https formstone it formstone is a collection of front end components le wagon ui components https lewagon github io ui components p align right a href table of contents b back to top b a p html5 themes name description bootstrap starter https bootstrapstarter com free bootstrap starter themes templates to kickstart your project html5 blank wordpress theme http html5blank com the best html5 wordpress boilerplate theme noted by adobe smashing mag net mag html5 up http html5up net responsive html5 and css3 site templates start bootstrap http startbootstrap com a library of free to download bootstrap themes and templates p align right a href table of contents b back to top b a p html preprocessor name description haml http haml info html abstraction markup language markdown https daringfireball net projects markdown a text to html conversion tool for web writers slim http slim lang com a lightweight templating engine jade http jade lang com node template engine p align right a href table of contents b back to top b a p css preprocessor name description less http lesscss org node css pre processor scss sass http sass lang com the most mature stable and powerful professional grade css extension language in the world stylus http stylus lang com expressive dynamic robust feature rich css preprocessor postcss http postcss org a tool for transforming css with javascript p align right a href table of contents b back to top b a p javascript preprocessor name description coffeescript http coffeescript org a little language that compiles into javascript livescript http livescript net a language which compiles to javascript typescript https www typescriptlang org javascript that scales babel https babeljs io the compiler for writing next generation javascript p align right a href table of contents b back to top b a p sass frameworks name description bourbon http bourbon io a simple and lightweight mixin library for sass compass http compass style org an open source css authoring framework susy http susy oddbird net custom layout engine for sass p align right a href table of contents b back to top b a p node js http nodejs org feature module and bundler name description npm https www npmjs com the package manager for javascript browserify http browserify org browser side require the node way grunt http gruntjs com the javascript task runner gulp http gulpjs com the streaming build system parcel https parceljs org blazing fast zero configuration web application bundler rollup https rollupjs org guide en next generation es module bundler webpack https webpack js org a bundler for javascript and friends eslint https eslint org find and fix problems in your javascript code prettier https prettier io prettier is an opinionated code formatter stylelint https stylelint io a mighty modern linter that helps you avoid errors and enforce conventions in your styles commitlint https commitlint js org lint commit messages linkinator https github com justinbeckwith linkinator a super simple site crawler and broken link checker browsersync https github com browsersync browser sync keep multiple browsers devices in sync when building websites http server https github com http party http server a simple zero configuration command line http server serve https github com zeit serve static file serving and directory listing server js https github com franciscop server simple and powerful server for node js chokidar https github com paulmillr chokidar an efficient wrapper around node js fs watch fs watchfile fsevents nodemon https github com remy nodemon monitor for any changes in your node js application and automatically restart the server perfect for development onchange https github com qard onchange use glob patterns to watch file sets and run a command when anything is added changed or deleted concurrently https github com kimmobrunfeldt concurrently run commands concurrently like npm run watch js npm run watch less but better npm run all https github com mysticatea npm run all a cli tool to run multiple npm scripts in parallel or sequential parallel shell https github com darkguy2008 parallelshell a super simple npm module to run shell commands in parallel concat https github com gko concat concatenate multiple files cpy cli https github com sindresorhus cpy cli copy files for node del https github com sindresorhus del delete files and directories using globs express https github com expressjs express fast unopinionated minimalist web framework for node node sass https github com sass node sass it allows you to natively compile scss files to css at incredible speed and automatically via a connect middleware npm check updates https github com raineorshine npm check updates upgrades your package json dependencies to the latest versions ignoring specified versions purgecss https purgecss com purgecss is a tool to remove unused css rimraf https github com isaacs rimraf the unix command rm rf for node bower https bower io depreciated a package manager for the web p align right a href table of contents b back to top b a p javascript library name description jquery http jquery com the write less do more javascript library modernizr https modernizr com modernizr tells you what html css and javascript features the user s browser has to offer bliss js https blissfuljs com want to use vanilla js but find native apis a bit unwieldy bliss is for you umbrella js https umbrellajs com lightweight javascript library for dom manipulation and events zepto js https zeptojs com zepto is a minimalist javascript library for modern browsers with a largely jquery compatible api cash js https github com fabiospampinato cash an absurdly small jquery alternative for modern browsers chibi js https github com kylebarrow chibi a tiny javascript micro library p align right a href table of contents b back to top b a p vanilla javascript https plainjs com plugins name description anchor js http bryanbraun github io anchorjs add deep anchor links to your docs anime js https animejs com anime js n me is a lightweight javascript animation library with a simple yet powerful api it works with css properties svg dom attributes and javascript objects chart js http www chartjs org simple clean and engaging charts for designers and developers chartist js http gionkunz github io chartist js simple responsive charts a darkmode js https github com sandoche darkmode js br b dark mode switch https github com coliff dark mode switch add a dark mode night mode to your website in a few seconds velocity js http julian com research velocity accelerated javascript animation favico js http lab ejci net favico js make use of your favicon with badges images or videos fluidvids js https github com toddmotto fluidvids fluid width responsive videos module 1kb custom players dynamic elements xhr support highlight js https github com highlightjs highlight js highlight js is a syntax highlighter written in javascript headroom js http wicky nillia ms headroom js hide your header until you need it holder js http holderjs com client side image placeholders lazysizes js https github com afarkas lazysizes high performance and seo friendly lazy loader for images responsive and normal iframes and more littlefoot js https github com goblindegook littlefoot littlefoot is a lightweight javascript library that creates exceptional footnotes lunr js http lunrjs com simple full text search in your browser nprogress js http ricostacruz com nprogress a nanoscopic progress bar pace js https github com hubspot pace an automatic web page progress bar prism js https github com prismjs prism prism is a lightweight robust elegant syntax highlighting library react burger menu https github com negomi react burger menu an off canvas sidebar react component with a collection of effects and styles using css transitions and svg path animations responsive nav http responsive nav com responsive navigation plugin without library dependencies and with fast touch screen support slideout js https slideout js org a touch slideout navigation menu for your mobile web apps smartcrop js https github com jwagner smartcrop js content aware image cropping tabella js http iliketomatoes github io tabellajs responsive table tether http github hubspot com tether a javascript library for efficiently making an absolutely positioned element stay next to another element on the page tippy js https atomiks github io tippyjs a lightweight vanilla javascript tooltip library tooltip js https github com hubspot tooltip tooltip js is a javascript and css library for creating styleable tooltips turbolinks https github com turbolinks turbolinks turbolinks makes navigating your web application faster typeset https blot im typeset a ty po graphic pre proces sor for your html which uses zero client side javascript zeroclipboard js http zeroclipboard org an easy way to copy text to the clipboard p align right a href table of contents b back to top b a p jquery http jquery com plugins name description formstone background https formstone it components background a jquery plugin for full frame image and video backgrounds bigfoot js http www bigfootjs com a jquery plugin for empowering footnotes bigslide js https ascott1 github io bigslide js a tiny slide panel navigation jquery plugin with big dreams fitvids js http fitvidsjs com a lightweight easy to use jquery plugin for fluid width video embeds fullpage js http alvarotrigo com fullpage one page scroll sections site plugin lazy load http www appelsiini net projects lazyload lazy load is delays loading of images in long web pages lity http sorgalla com lity a ultra lightweight accessible and responsive lightbox plugin which supports images iframes and inline content out of the box p align right a href table of contents b back to top b a p javascript compressor toolkit name description terser https github com terser terser javascript parser mangler and compressor toolkit for es6 uglifyjs https github com mishoo uglifyjs2 uglifyjs is a javascript parser minifier compressor and beautifier toolkit p align right a href table of contents b back to top b a p css library name description animate css http daneden github io animate css a cross browser library of css animations cssgram https una im cssgram a tiny 1kb gzipped library for recreating instagram filters with css filters and blend modes css filters playground http bennettfeely com filters play with the new css3 filter effects eqcss http elementqueries com a spec for a container style element query syntax in css flex grid http flexboxgrid com a grid system based on the flex display property mastering the nth child http nthmaster com css3 pseudo classes and nth child ranges eric meyer css reset http meyerweb com eric tools css reset eric meyer css reset graaf http graaf space pure css grid overlays for designing hamburgers https jonsuh com hamburgers tasty css animated hamburgers mueller grid system http muellergridsystem com a modular grid system for responsive adaptive and non responsive layouts based on compass normalize css http necolas github io normalize css a modern html5 ready alternative to css resets rtlcss http rtlcss com framework for converting left to right ltr cascading style sheets css to right to left rtl toast http daneden github io toast insane no nonsense css grid csswand https www csswand dev it s really just a handful of simple pure css based animation library water css https watercss netlify app collection of styles to make simple websites raisin css https github com tretapey raisincss an utility css only library spectrum css https github com adobe spectrum css standard css implementation of the spectrum design language for internal and 3rd party box shadows css https madeas github io box shadows mianly based on box shadows p align right a href table of contents b back to top b a p svg png and font icons library name description how to work with svg icons https fvsch com code svg icons how to align svg icons to text and say goodbye to font icons https blog prototypr io align svg icons to text and say goodbye to font icons d44b3d7b26b4 bootstrap icons https icons getbootstrap com bootstrap icons are svgs so they scale quickly and easily and can be styled with css while they re built for bootstrap they ll work in any project creative commons license icons https creativecommons org about downloads download svg eps and png creative commons license icons to use in your site flaticon http www flaticon com the largest database of free icons available in png svg eps psd and base 64 formats feather https feathericons com feather is a collection of simply beautiful open source icons font awesome http fontawesome io the iconic font and css toolkit font awesome svg png https github com encharm font awesome svg png font awesome split to individual svg and png files of different sizes along with node js based generator fontforge https fontforge github io en us a free and open source font editor brought to you by a community of fellow type lovers fontello http fontello com an easy way to create a custom icon font for your site select images from our large collection and make a webfont pack with one click icomoon https icomoon io icomoon provides a package of vector icons along with a free html5 app for making custom icon fonts or svg sprites browse among thousands of pixel perfect icons or import your own vectors ionicons http ionicons com the premium icon font for ionic framework http ionicframework com material design icons https materialdesignicons com view all the material design icons and more from the community the big list of flat icons icon fonts https css tricks com flat icons icon fonts by css triks there are many such roundups this one is mine noun project icons for everything https thenounproject com nearly a million curated icons created by a global community octicons https octicons github com a scalable icon font handcrafted with lt 3 by github perfect icons http perfecticons com the easiest way to create resolution independent social icons simple icons https simpleicons org free svg icons for popular brands svg icon https leungwensen github io svg icon an ultimate svg icons collection done right svg icons http svgicons sparkk fr ready to use svg icons for the web vector icons roundup https tagliala github io vectoriconsroundup a side by side comparison between popular icon fonts we love icon fonts http weloveiconfonts com a free open source icon fonts hosting service for testing purposes p align right a href table of contents b back to top b a p html forms name description chartspree http chartspree io make charts in seconds formspree https formspree io functional html forms for static sites gridspree http gridspree io display your spreadsheet data however you like right on your static site with google spreadsheet jotform http www jotform com form builder wtf forms http wtfforms com friendlier html form controls with a little css magic designed for ie9 as well as the latest chrome safari and firefox wufoo http www wufoo com online form builder with cloud storage database p align right a href table of contents b back to top b a p web tools name description abstract api s https www abstractapi com a suite of simple yet powerful utility api s for developers such as email validation user avatar generator image compression and more all api s have a free version autodraw https www autodraw com fast drawing for everyone autodraw pairs machine learning with drawings from talented artists to help you draw stuff fast binary translator http binarytranslator com free binary code translator translate binary code to text base16 https github com chriskempson base16 an architecture for building themes based on carefully chosen syntax highlighting using a base of sixteen colors bootstrap 5 cheat sheet https bootstrap cheatsheet themeselection com an interactive list of bootstrap 5 classes variables and mixins colors css https github com mrmrs colors better default colors for the web a collection of skin classes for faster prototyping and nicer looking sites color hex color codes https www color hex com color hex gives information about colors including color models rgb hsl hsv and cmyk triadic colors monochromatic colors and analogous colors calculated in color page color hunt http www colorhunt co curated collection of beautiful colors updated daily css3 generator http css3generator com css3 classics cssmatic http www cssmatic com the ultimate css tools for web designers draw io http draw io draw io http diagrams net is free online diagram software for making flowcharts process diagrams org charts uml er and network diagrams flat colors http flatcolors net browse over 11 000 flat colors in over 2 300 flat color palettes copy them or download the aco file for use in photoshop frontend dogma frontend development tools https frontenddogma com tools curated web based tools for website developers and owners for analysis and exploration gradient backgrounds https cssgradient io gradient backgrounds as a curated list of the best gradient websites across the internet gradient backgrounds allows you to explore try and choose from hundreds of beautiful blended color palettes styling wizard google maps apis https mapstyle withgoogle com customize colors roads labels and more then use your custom style in your google maps platform project snazzy maps https snazzymaps com a repository of different styles for google maps aimed towards web designers and developers solarized precision colors for machines and people https github com altercation solarized solarized is a sixteen color palette eight monotones eight accent colors designed for use with terminal and gui applications sri hash generator https www srihash org use of sri is recommended as a best practice whenever libraries are loaded from a third party source superdomain http superdomain io check domain availability create latex tables online http www tablesgenerator com quickly create even complex latex tables with online generator cells merging is supported together with borders editing uigradients http uigradients com a handpicked collection of beautiful colour gradients for designers and developers unminify http unminify com free online tool to unminify unpack deobfuscate javascript css and html code making it readable and pretty web code tools http webcodetools com css3 html5 microdata open graph and twitter card generators what s my screen resolution http whatsmyscreenresolution com an online tool to check screen resolution of any display device webgradients https webgradients com free collection of background gradients that you can use as content backdrops in any part of your website p align right a href table of contents b back to top b a p cdn library name description bootstrapcdn http www bootstrapcdn com the recommended cdn for bootstrap font awesome and bootswatch cdnjs https cdnjs com the free and open source cdn for web related libraries to speed up your website coralcdn http www coralcdn org a free and open content distribution network based around peer to peer technologies comprised of a world wide network of web proxies and nameservers fontcdn http fontcdn org a search tool for google web fonts google fonts https www google com fonts hundreds of free open source fonts optimized for the web google hosted libraries https developers google com speed libraries a stable reliable high speed globally available content distribution network for the most popular open source javascript libraries jsdelivr http www jsdelivr com a free super fast cdn for developers and webmasters microsoft ajax cdn http www asp net ajax cdn popular third party javascript libraries such as jquery and enables you to easily add them to your web applications open source software cdn http osscdn com open source software cdn by maxcdn yandex cdn https tech yandex ru jslibs service hosts javascript libraries that provides site developers with access to the yandex content delivery cdn and can handle a variety of open source javascript frameworks and libraries from yandex servers p align right a href table of contents b back to top b a p browser compatibility wiki name description can i use http caniuse com check the browser compatibility of css3 and html5 properties should i prefix http shouldiprefix com simply show what prefixes are needed for a newer css property p align right a href table of contents b back to top b a p responsiveness testing tools name description am i responsive http ami responsivedesign is see how your site looks on desktop laptop ipad iphone browserstack https www browserstack com use the browser device tester and download internet explorer images for your vm deviceponsive http deviceponsive com show a quick preview of your website s responsive to different device resolutions like macbook ipad or iphone share this preview with your client responsively app https github com responsively org responsively app a must have devtool for web developers for quicker responsive web development p align right a href table of contents b back to top b a p performance testing tools name description google pagespeed tools https developers google com speed pagespeed the pagespeed tools analyze and optimize your site following web best practices webpagetest http www webpagetest org run a free website speed test from multiple locations around the globe using real browsers and at real consumer connection speeds website speed test http tools pingdom com use this free website speed test to analyze the load speed of your websites and learn how to make them faster p align right a href table of contents b back to top b a p placeholder name description jsonplaceholder https jsonplaceholder typicode com fake online rest api for testing and prototyping lorem ipsum https lipsum com lorem ipsum is simply dummy text of the printing and typesetting industry lorempixel http lorempixel com placeholder images for every case placehold it https placehold it a quick and simple image placeholder service p align right a href table of contents b back to top b a p mockup placeholder name description placeit https placeit net the best one to drop your project in any device context p align right a href table of contents b back to top b a p website icons for browsers name description favicon generator https realfavicongenerator net online favicon generator for real build my pinned site http www buildmypinnedsite com online tool to create a custom windows start screen tile for your site in less than a minute pinned site enhancements on windows https msdn microsoft com en us library bg183312 v vs 85 aspx creating custom tiles for ie11 websites https msdn microsoft com en us library dn455106 v vs 85 aspx setting up live tiles and notifications for windows configuring web applications for apple https developer apple com library ios documentation appleapplications reference safariwebcontent configuringwebapplications configuringwebapplications html conceptual information and techniques on creating effective web content for safari and webkit using html and css installable web apps with the web app manifest in chrome for android https developers google com web updates 2014 11 support for installable web apps with webapp manifest in chrome 38 for android hl en manifest file format https developer chrome com extensions manifest use the web app manifest to control how your web app launches p align right a href table of contents b back to top b a p free inspirational snippets and tutorials name description codemyui http codemyui com handpicked code snippets you can use in your web projects find web design inspiration with code samples codyhouse https codyhouse co a free library of html css js nuggets codrops http tympanus net codrops codrops is a web design and development blog that publishes articles and tutorials about the latest web trends techniques and new possibilities moving to https https movingtohttps com migrate from http to https with ease navnav http navnav co a ton of css jquery and javascript responsive navigation examples demos and tutorials from all over the web papersizes io the best resource for paper sizes http papersizes io a simple free resource for finding the international standards for paper sizes in metric and imperial pdf candy https pdfcandy com edit pdf files with pdf candy a free online pdf editor convert pdf to word pdf to jpg merge pdf split pdf compress pdf etc scotch https scotch io a web development blog discussing all things programming development web and life smashing magazine http www smashingmagazine com an online magazine for professional web designers and developers with a focus on useful techniques best practices and valuable resources w3schools online web tutorials https www w3schools com the world s largest web developer site p align right a href table of contents b back to top b a p css reference name mozilla web docs https developer mozilla org en us docs web css css dev docs https devdocs io css css properties index https meiert com en indices css properties css reference https cssreference io css reference codrops https tympanus net codrops css reference layout flexbox css tricks https css tricks com snippets css a guide to flexbox css grid css tricks https css tricks com snippets css complete guide grid css flexbox playground by gabi https codepen io enxaneta pen adlpwv flexbox froggy https flexboxfroggy com css grid garden https cssgridgarden com css animations using css animation mdn https developer mozilla org en us docs web css css animations using css animations animation css tricks https css tricks com almanac properties a animation p align right a href table of contents b back to top b a p css tutorials name description browser safe fonts http www ampsoft net webdesign l windowsmacfonts html common fonts to all versions of windows mac equivalents css font stack http www cssfontstack com a complete collection of web safe css font stacks css outline property outline none and outline 0 http outlinenone com the css outline property is an accessibility requirement yet often abused by many web designers why do they do it css selector reference https www w3schools com cssref css selectors asp in css selectors are patterns used to select the element s you want to style css specificity http cssspecificity com css specificity css specificity assigns a numerical representation of a selector in order to compare in case there are style conflicts web poster displays css specificity with icons from stanley kubrick s the shining film grid by example https gridbyexample com everything you need to learn css grid layout grid garden https cssgridgarden com a game for learning css grid griddy http griddy io learn the css grid hash tag links that don t headbutt the browser window https css tricks com hash tag links padding css tricks post by chris coyier learn css grid http learncssgrid com a guide to learning css grid learn css grid http jensimmons com post feb 27 2017 learn css grid a lot of fantastic resources out there by jen simmons learn css layout http learnlayout com this site teaches the css fundamentals that are used in any website s layout overriding the default text selection color with css https css tricks com overriding the default text selection color with css css tricks post by chris coyier smarter link underlines for every website https eager io blog smarter link underlines post by adam schwartz what is the browser default background color when selecting text https stackoverflow com questions 16094837 what is the browser default background color when selecting text answer on stack overflow why i switched from less to sass http hugogiraudel com 2012 11 13 less to sass post by hugo giraudel p align right a href table of contents b back to top b a p infographics name description home office posters https github com ukhomeoffice posters home office repository of posters covering different topics research access needs accessibility design p align right a href table of contents b back to top b a p design inspiration name admire the web http www admiretheweb com awwwards http www awwwards com behance https www behance net best website gallery http bestwebsite gallery codepen http codepen io css gallery http www cssdsgn com css winner http www csswinner com design fridge http www designfridge co uk deviantart http www deviantart com dribble https dribbble com favourite website awards fwa http www thefwa com goodui http www goodui org google design https design google com html inspiration http htmlinspiration com inspiration for search ui effects https tympanus net development searchuieffects one page love https onepagelove com nice one i like http www niceoneilike com siiimple http www siiimple com the best designs https www thebestdesigns com very nice sites http www verynicesites com web design inspirations http www webdesign inspiration com webdesign tumblr search https www tumblr com search webdesign p align right a href table of contents b back to top b a p the type system name description exploring responsive type scales https medium com sketch app sources exploring responsive type scales cf1da541be54 finding your appropriate multi device and vertical rhythm font pair https fontpair co font pair helps designers pair google fonts together beautiful google font combinations and pairs generate type scales with stackswell https medium com sketch app sources generate type scales with stackswell 8cdc27a5fed9 save time with stackswell s latest release and create type systems in seconds material design type system https material io design typography the type system html use typography to present your design and content as clearly and efficiently as possible modular scale https www modularscale com calculate visualize and learn about modular scales type connection http www typeconnection com type connection is a game that helps you learn how to pair typefaces typography for developers https css tricks com typography for developers this is intended as a practical guide for developers to learn web typography typewolf https www typewolf com what s trending in type type scale a visual calculator https type scale com preview and choose the right type scale for your project experiment with font size scale and different webfonts whatfont chrome extension https chrome google com webstore detail whatfont jabopobgcpjmedljpbcaablpmlmfcogm hl en the easiest way to identify fonts on web pages p align right a href table of contents b back to top b a p webmaster tools name description schema org https schema org a collaborative community activity with a mission to create maintain and promote schemas for structured data on the internet on web pages in email messages and beyond microformats http microformats org wiki main page the simplest way to markup structured information in html understand how structured data works https developers google com search docs guides intro structured data this documentation describes which properties are required recommended or optional for structured data with special meaning to google search structured data testing tool https search google com structured data testing tool by google google structured data testing tool pagespeed insights https developers google com speed pagespeed insights by google make your web pages fast on all devices json for linking data https json ld org data is messy and disconnected json ld organizes and connects it creating a better web json ld https en wikipedia org wiki json ld wiki latitude and longitude finder https www latlong net latitude and longitude are the units that represent the coordinates at geographic coordinate system to make a search use the name of a place city state or address or click the location on the map to find lat long coordinates p align right a href table of contents b back to top b a p package manager name description list of software package management systems https en wikipedia org wiki list of software package management systems this is a list of software package management systems categorized first by package format binary source code hybrid and then by operating system family apt https en wikipedia org wiki apt software debian based linux os package manager yum https en wikipedia org wiki yum software fedora based linux os package manager homebrew https en wikipedia org wiki homebrew package manager homebrew https brew sh is a package management system that simplifies the installation of software on apple s macos operating system and linux winget https en wikipedia org wiki windows package manager the windows package manager https docs microsoft com en us windows package manager aka winget is a free and open source package manager designed for microsoft windows 10 chocolatey https en wikipedia org wiki nuget chocolatey chocolatey https chocolatey org is a machine level package manager and installer for software packages built for the windows platform goenv goenv https github com syndbg goenv aims to be as simple as possible and follow the already established successful version management model of pyenv and rbenv npm https en wikipedia org wiki npm software npm https www npmjs com originally short for node package manager is a package manager for the javascript programming language nodenv use nodenv https github com nodenv nodenv to pick a node version for your application and guarantee that your development environment matches production pyenv pyenv https github com pyenv pyenv lets you easily switch between multiple versions of python yarn https en wikipedia org wiki npm software alternatives yarn https yarnpkg com is a alternative to npm rvm https en wikipedia org wiki ruby version manager rvm https rvm io is a command line tool which allows you to easily install manage and work with multiple ruby environments from interpreters to sets of gems rbenv use rbenv https github com rbenv rbenv to pick a ruby version for your application and guarantee that your development environment matches production p align right a href table of contents b back to top b a p jamstack name description netlify https www netlify com deploy modern static websites with netlify get cdn continuous deployment 1 click https and all the services you need get started for free staticgen https www staticgen com top open source static site generators list staticman https staticman net staticman is a node js application that receives user generated content and uploads it as data files to a github and or gitlab repository gitalk https gitalk github io a modern comment component based on github issue and preact utterances https utteranc es a lightweight comments widget built on github issues use github issues for blog comments wiki pages and more p align right a href table of contents b back to top b a p list of lists of lists name description 56 unique lorem ipsum generators https mashable com 2013 07 11 lorem ipsum spice up your filler text and design projects with these hilarious lorem ipsum generators abstract guide to http status codes https www abstractapi com http status codes a list and explanation of http status codes awesome https github com sindresorhus awesome a curated list of awesome lists awesome awesomeness https github com bayandin awesome awesomeness a curated list of amazingly awesome awesomeness awesome awesome https github com emijrp awesome awesome a curated list of awesome curated lists of many topics bootstrap resources http startbootstrap com bootstrap resources by start bootstrap a comprehensive list of bootstrap and related resources bootstrap snippets https startbootstrap com snippets by start bootstrap a curated library of bootstrap 4 code snippets perfect for dropping into your project without downloading an entire theme design essentials github collections https github com collections design essentials this collection of design libraries are the best on the web and will complete your toolset for designing stunning products design resources for developers https github com bradtraversy design resources for developers a curated list of free design ui resources for developers including stock photos templates frameworks ui kits online tools and much much more github open source showcases https github com showcases browse popular repositories based on the topic that interests you most gradient backgrounds https cssgradient io gradient backgrounds as a curated list of the best gradient websites across the internet gradient backgrounds allows you to explore try and choose from hundreds of beautiful blended color palettes javascript plugins repository https plainjs com javascript plugins vanilla js tools for writing powerful web applications without jquery javascript territory http jster net jster javascript catalog javascripting http www javascripting com the database of javascript libraries frameworks and plugins libraries io https libraries io the open source discovery service list of http status codes https en wikipedia org wiki list of http status codes a list of hypertext transfer protocol http response status codes social share urls https github com bradvin social share urls readme this project is intended to help you integrate sharing on social media within your code stock photos that don t suck https medium com dustin stock photos that dont suck 62ae4bcbe01b a list of places to find the best free stock photos text editors github collections https github com collections text editors a showcase of some amazingly awesome open source editors unheap http www unheap com a tidy repository of jquery plugins classified in categories p align right a href table of contents b back to top b a p | web-development web-design front-end-development awesome-list awesome resources web-development-tools web-application framework | front_end |
Engineering-in-Cloud-Computing | engineering in cloud computing engineering in cloud computing course | cloud |
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crackcoin | crackcoin crackcoin is a very basic blockchain free cryptocurrency poc in python it s a project for discovering cryptocurrencies note that this is a poc that runs only on local networks and does not provide proper security the code should only be used to get familiar with the building blocks for a cryptocurrency also crackcoin should not be confused with the dead currency crackcoin crack from 2014 this project was created as an exercise after reading mastering bitcoin unlocking digital cryptocurrencies material covered transaction based mining as a poc for a blockchain free cryptocurrency threading in python working with sockets in python udp ecc crypto ecc public key compression decompression base58 encoding like bitcoin having the whole thing work wallet crypto validation networking mining etc project s purpose the purpose of this project is to have people learn about the basic workings of a cryptocurrency i ve tried to create a simple as possible framework to play with the current code allows nodes to exchange coins on a local network in its current state the application does not handle consensus forks also you can perform some double spending attacks and easily out mine other nodes no smart transaction confirmation graphing is implemented to outwit blockchain implementations however this is the whole point these methods are not implemented but because the code is so simple you can easily try out ideas to see how and if they work thus i hope that the project can be an addition to both complete beginners in the field of cryptocurrencies as well as researchers or advanced coders that want to test new ideas blockchain free cryptocurrencies most cryptocurrencies use a blockchain to validate transactions among other things after years of running these networks it s beginning to look like blockchain based currencies naturally evolve into a centralised network because it s in the best interest of the participants to combine computing power to calculate solutions for blocks an interesting framework for a blockchain free protocol is discussed in the paper blockchain free cryptocurrencies a framework for truly decentralised fast transactions which can be found here https eprint iacr org 2016 871 pdf do note that crackcoin doesn t implement nearly as complex a protocol as described in the paper but the transaction based mining method was used as an inspiration for implementing the core for crackcoin component basics wallets a wallet consists of a public private keypair and an address the address is derived from the public key transactions a transaction contains inputs outputs and a unique identifier called hash an output has a unique identifier and is just an amount and a to address an input points to a previous output and uses the coins from that output it must contain a compressed public key and a signature this way nodes can identify that the to address from the previous output which can be generated with the public key is owned by the spender the gui when you create a transaction a confirmation is created and both the transaction and the confirmation are shared on the network udp broadcast when using the broadcast option b your crackcoin node will broadcast a request packet on the network any crackcoin node receiving the request will send all transactions and confirmations to you this is so new nodes can sync network server udp when the server receives a new transaction it checks if the transaction is valid and adds it to the database ledger when a confirmation is received the transaction s confirmation is updated if the received difficulty is higher than the existing difficulty mining and confirmations confirmations are proof of work hashes for a transaction the mining component simply creates confirmations for some transaction mining is done by hardening the transaction confirmation with the least difficulty usage the following steps will allow you to run the code on the local network and spend coins 1 run generategenesis py we re going to need the code git clone https github com dutchgraa crackcoin git cd crackcoin then you python generategenesis py this will show you something like the following private key 17761749377588078293913083910285222277328633594463995997908039960139540655010 compressed public key crackmhmf8qgic2re7yecuetg1147v8fdycvqtc15ce7dqyph address crackcyggs8jajvm7qgix25l1argbhrrfbyldczvdqegubs2dy what you re seeing here is the base for a wallet there is a private key a compressed public key and an address we ll change the code such that the genesis transaction is transferred to your wallet so you can spend the coins in this example i ll use the above values 2 change the database template the first time you run crackcoin py a database is generated from the template crackcoinbase sql this file holds the genesis transaction which is the first transaction for the currency this transaction creates coins from thin air and transfers then to an account go ahead and open crackcoinbase sql there is one line that looks like this insert into transactions outputs id amount address outputhash transactionhash values 1 31337 crackcoint3wmfeujeyrnmrjur3y8wm2lopaqmy3prjakywcen this is the genesis transaction that transfers 31337 coins to address crackcoint3wmfe change this address to the address generated with generategenesis py so in this example i change crackcoint3wmfeujeyrnmrjur3y8wm2lopaqmy3prjakywcen to crackcyggs8jajvm7qgix25l1argbhrrfbyldczvdqegubs2dy if you ran crackcoin py before make sure you delete crackcoin db after doing this edit 3 fix your wallet do a python crackcoin py this will create the database and a random wallet now type i you should see a wallet with no coins now type q to quit and wait for it to quit now with crackcoin shut down open crackcoin db the database with an sqlite database editor you can use a gui tool for this or just sqlite3 on os x i like to use a tool called db browser browse to the table called wallets and change the private key public key and address to the values you generated with generategenesis py save the database close the tool now if everything went well you can python crackcoin py and if you type i you should see that your wallet now contains 31337 coins neat 4 exchange code make a copy of the crackcoin directory and remove the crackcoin db file from the copy we ll use this copied directory on other nodes so let s call this the public directory you can rename it to crackcoinpub if you like now copy this public directory to another node on the network i ll refer to our main node that holds the genesis private key as node a and the other node we just copied the public directory to node b on node b just python crackcoin py from the public directory if you then type i you should see a generated wallet address for b on node a type t to make a transaction at to you enter the wallet address for node b at amount just enter a small number like 137 now if you press i on both nodes you should see that the coins are transferred sometimes you need to type b on node b to sync the two you can now transfer coins from device a to device b and back here is a screenshot alt text https raw githubusercontent com dutchgraa crackcoin master crackcoin usage png transfering coins common problems udp is chosen to send transactions and such because it doesn t take a lot of code these packets have a maximum length of about 500 bytes after that you ll have to fragment packets which kind of sucks this is why zlib compression is used don t forget to wait for the threads to end when you shut down crackcoin q or ctrl c crackcoin only works on the local network because of udp broadcasts but you also need an internet connection or else the method getlocalip will fail | blockchain |
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aws-iot-device-sdk-js-v2 | aws iot device sdk for javascript v2 this document provides information about the aws iot device sdk for javascript v2 this sdk is built on the aws common runtime https docs aws amazon com sdkref latest guide common runtime html jump to installation installation samples samples getting help getting help faq documents faq md api docs https aws github io aws iot device sdk js v2 mqtt5 user guide https github com awslabs aws crt nodejs blob main mqtt5 userguide md impending node version update in the coming months the v2 iot sdk will be updating its node baseline from 10 16 to 14 a discussion thread https github com awslabs aws crt nodejs discussions 468 has been created for any questions or feedback you may have we do not yet have a concrete timeline for when the update will happen installation minimum requirements for use with node the following are required node v10 0 run node v to check node version cmake 3 1 step by step instructions documents prerequisites md build sdk in existing project with npm sh navigate to the javascript project you want to add the javascript v2 sdk to cd your javascript project here install the v2 sdk npm install aws iot device sdk v2 now you can use the javascript v2 sdk in your project build the v2 sdk from source sh create a workspace directory to hold all the sdk files mkdir sdk workspace cd sdk workspace clone the repository to access the samples git clone https github com aws aws iot device sdk js v2 git install the sdk cd aws iot device sdk js v2 npm install then you can run the samples following the instructions in the samples readme samples samples readme samples readme md getting help the best way to interact with our team is through github you can open a discussion https github com aws aws iot device sdk js v2 discussions for guidance questions or an issue https github com aws aws iot device sdk js v2 issues new choose for bug reports or feature requests you may also find help on community resources such as stackoverflow https stackoverflow com questions tagged aws iot with the tag aws iot https stackoverflow com questions tagged aws iot or if you have a support plan with aws support https aws amazon com premiumsupport you can also create a new support case please make sure to check out our resources too before opening an issue faq documents faq md api docs https aws github io aws iot device sdk js v2 iot guide https docs aws amazon com iot latest developerguide what is aws iot html source https github com awsdocs aws iot docs mqtt5 user guide https github com awslabs aws crt nodejs blob main mqtt5 userguide md check for similar issues https github com aws aws iot device sdk js v2 issues aws iot core documentation https docs aws amazon com iot dev blog https aws amazon com blogs awsf blog master iot category internet of things 23amazon freertos 7ccategory internet of things 23aws greengrass 7ccategory internet of things 23aws iot analytics 7ccategory internet of things 23aws iot button 7ccategory internet of things 23aws iot device defender 7ccategory internet of things 23aws iot device management 7ccategory internet of things 23aws iot platform integration with aws iot services such as device shadow https docs aws amazon com iot latest developerguide iot device shadows html and jobs https docs aws amazon com iot latest developerguide iot jobs html is provided by code that been generated from a model of the service contributions guidelines documents contributing md license this library is licensed under the apache 2 0 license documents license latest released version v1 16 1 | hacktoberfest | server |
decorator | decorator is departed this repo will soon be deleted watch or star the new repo modulr css https github com uloga modulr css modulr css https github com uloga modulr css is the new framework | front_end |
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TCC | tcc these codes are related to my senior design project at ufpe a embedded system to process and calculate heart rate from ecg heart signal | os |
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oc-devices-plugin | oc devices plugin manage user devices for mobile app development | front_end |
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webpp | web c web framework web logo assets logo bordered svg this is a web framework written in c that uses multiple underlying protocols it s being developed news will be shared on telegram https t me webpp this project on telegram https t me webpp github https github com the moisrex webpp gitlab https gitlab com webpp webpp look at the core readme file webpp readme md for the core concepts of the project project goals to remove the necessity for dealing with low level networking apis cross platform no need to deal with os specific apis modular to use other people s written apps and modules in your code easily cross protocol remove the necessity for dealing with specific protocols cgi fastcgi cross database api being able to access wide range of databases without dealing with db specific apis and switch between them in the config files instead of changing the code itself parallelization access being able to compute things in parallel easily implement common patterns easily what can you should be able to do with this library the project is still in development but this framework should be able to help you with these kinda projects someday develop a static dynamic website easily in c using other people s already written websites modules components in your app writing long running web apps and store temporary data in variables instead of database not all languages can do that why c the most question that i get while talking to other developers is why c well there are multiple answers c does have the potential specially the newer versions of c having full power of c and your os at your disposal webassembly is getting stronger you will be able to write both back end and front end in the same language with c even though you already can if you choose javascript for example but here s another option has the potential to be faster than other languages c can be easy if the library is easy to use using modern c is fun compile time computation is something that you either can t achieve or it s going to be difficult to achieve in other languages remove the necessity to learn other languages if you re already familiar with c using older codes in your project more easily multi threading access not all languages provide that access easily specially in a web framework example codes a simple cgi application c auto page one return page 1 struct web app enable owner traits default traits et holds the allocator logger dynamic router router et helps with the routing could be used as an app too web app register your routes router router endpath noexcept return main page router router page one endpath page one free functions router router cgi bin cgi hello world return hello world about or cgi bin cgi hello world about router router about router cgi bin cgi hello world about context ctx return ctx view about html auto operator auto req return router req int main cgi protocol webpp http cgi web app cgi application run the app return cgi application there are a lot more features designed to be used in a modular way c could be a sub app of another sub app that has no clue how the server works or what kinda protocol cgi fastcgi self served is being used struct app using namespace webpp using namespace webpp http app tell the view manager where to look for the files view man view roots emplace back public view man view roots emplace back static response index context ctx return main page response api request const req json document doc req you can choose which json lib you want to use in the background doc user username doc token some token return doc response about request const req response res req res headers http status code ok res headers content type text html res body about page return res auto page one return view man view pages page1 mustache we have mustache built in auto hello return view man view pages hello html private enable traits for views view manager view man for demonstration purposes only we could ve done this a lot easier the server calls this class the operator of this class for every request btw the router can totally replace this you can inherit from it even struct app controller using namespace webpp using namespace webpp http private enable traits for dynamic router router app my app public app controller register your app router objects emplace back my app register the routes router router endpath app index router router page one app page one router router api v1 app api router router cgi bin cgi hello world app hello router router about router cgi bin cgi hello world about app about this operator will be called for each request httpresponse auto operator httprequest auto req return router req int main webpp beast app controller server server enable sync call the app in a thread safe manner might be removed in the future address 127 0 0 1 listen on localhost port 8080 on http port start the server and share your website to the world like there s no tomorrow return server development you can help us develop this project if you re familiar with c programming language and web development even if you re not really good at web development but you know c very well specially c 17 and c 20 clang and gcc c 2a is enough then we have lots of things that you can help with which doesn t have anything to do with web development directly i d appreciate any help of any kind even if you re not interested in coding here are some ways you can help out writing tests i dare you to make me mad by writing too much tests writing benchmarks i write lots of benchmarks even outside the scope of this project to make sure i m using the right tool library feature class template make jokes make not funny jokes in our webpp discuss https t me webpp discuss group share ideas i have plenty of ideas and i want more report bugs you can find me in the webpp discuss https t me webpp discuss group report the bugs there or open issues on github gitlab financial support this project is open source we ll need some money for hosting and advertising share on social media i ll appreciate any shout out about this project even if you give a bad feedback learn teach we can teach you and also learn from you so let s talk code suggest someone who can help i d appreciate any help on finding people who can help in any way write documentation writing documentation is so much fun that i don t want to do it alone examples in the examples directory you can find examples that s not much but it ll give you they high level viewpoint take a look at them and you can be sure that we ll try to write tests for all of them so if you read tests you ll learn even more about the project benchmarks benchmarks are done for us developers so we know which tool class implementation library framework solution that we choose is the best currently we are using googles mini benchmark library for this purpose documentation we don t have a documentation tutorial guide at this point you can help us write one we re just too busy writing the code that we don t have much time writing documentations at this point but you can be sure from the point that this project becomes production ready we ll have documentations build bash download git clone https gitlab com webpp webpp git depth 1 cd webpp configure cmake default uses ninja and creates build directory cmake preset default build examples see examples directory for the list of them cmake build preset cgi application cmake build preset beast view cmake build preset cgi hello world cmake build preset beast json build tests see tests directory for the list of tests each file is a test cmake build preset test type traits cmake build preset test dynamic router cmake build preset test context cmake build preset test cookies cmake build preset test response run the tests individually build test response build test type traits run all tests need to build first ctest preset tests install cmake install build prefix usr | cpp web framework | front_end |
Front-End-Fisheye | base de code du projet p6 parcours front end d marrer le projet rien installer ici il suffit d ouvrir le fichier index html | front_end |
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stripes | stripes copyright c 2017 2021 the open library foundation this software is distributed under the terms of the apache license version 2 0 see the file license license for more information introduction stripes is a toolkit for building single page web applications that folio ui modules can run in in addition to being the home of stripes framework doc stripes framework md this repository serves as a hub for shared stripes documentation background stripes framework doc stripes framework md a brief overview of the stripes framework package overview of stripes doc overview md concepts that guided the design of stripes stripes entities doc modules apps etc md terminology of things pertaining to stripes developing with stripes getting started and new environment setup quick start guide doc quick start md new development setup guide doc new development setup md more detailed overview hello world https github com folio org stripes cli blob master doc user guide md app development use stripes cli to create a hello world app for folio guides for development and testing stripes module developer s guide doc dev guide md stripes cli user guide https github com folio org stripes cli blob master doc user guide md migrate to stripes framework doc stripes framework md migrating how to migrate an existing app to stripes framework v1 0 stripes core https github com folio org stripes core blob master readme md details on stripes core features including permissions and settings stripes components https github com folio org stripes components blob master readme md the ui component library for stripes unit testing with jest and rtl doc unit testing with jest and rtl md integration test guide integration tests with nightmare placeholder note the old documentation https github com folio org ui testing blob master readme md is still mostly relevant but tests are now invoked by the cli accessibility a11y in stripes https github com folio org stripes components blob master guides accessibility aboutaccessibility stories mdx internationalization i18n best practices doc i18n md overview and examples release procedure doc release procedure md for all front end ui and stripes modules depending on unreleased features doc depending on unreleased features md troubleshooting guide doc troubleshooting md an evolving troubleshooting guide typescript support typings for the stripes framework may be found in stripes types https github com folio org stripes types implementing stripes guides for dev ops and platform implementors overview of the tenant config aka stripes config js placeholder branding guide doc branding md apply a logo and favicon build a platform https github com folio org stripes cli blob master doc user guide md generating a production build use the cli to create a production build additional information see project stripes https issues folio org browse stripes at the folio issue tracker https dev folio org guidelines issue tracker other folio developer documentation is at dev folio org https dev folio org | front_end |
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book-list | bookstore application this application is inspired by istios bookinfo https istio io latest docs examples bookinfo example app it is intended to demonstrate modern development practices such as microservices and containerization the code accompanies a lecture held at tum heilbronn the main purpose of the application is to expose static information about books via a restful api docs component svg every component is a separate spring boot application which you can find in its dedicated folder getting started github actions the repository comes with a ready to use github workflow which takes care of the continuous integration part for you to use it make sure to set it up correctly 1 create repository secrets https docs github com en actions security guides encrypted secrets for your docker username and your docker password do not store sensitive information such as your username or password in the repository always use an encrypted storage for that 2 select the bookstore component that you want to build this can be done via the component environment variable in the workflow file github workflows build push yml the workflow will be triggered for every change that you do on the main branch as a result of a successful workflow run you will see a new docker image in your personal dockerhub repository the naming schema is as follows secrets docker username bookstore env component github run number every run creates a new tag to prevent cache issues while fetching the image | cloud |
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task-master | img src img tm 1b png alt task master border 10 github issues https img shields io github issues udacityfrontendscholarship task master svg https github com udacityfrontendscholarship task master issues github pull requests https img shields io github issues pr udacityfrontendscholarship task master svg https github com udacityfrontendscholarship task master pulls github forks https img shields io github forks udacityfrontendscholarship task master svg style social label fork https github com udacityfrontendscholarship task master network github stars https img shields io github stars udacityfrontendscholarship task master svg style social label stars https github com udacityfrontendscholarship task master stargazers github watchers https img shields io github watchers udacityfrontendscholarship task master svg style social label watch https github com udacityfrontendscholarship task master watchers github contributors https img shields io github contributors udacityfrontendscholarship task master svg https github com udacityfrontendscholarship task master graphs contributors table of contents a href 1 current live pages a a href 2 back end implementation a a href 3 current status a a href 4 next up a a href 5 what you can do a a href 6 usage a a href 7 background a a href 8 color palette a a href 9 key features a probable front end work probable front end work probable back end work probable back end work a href 10 features in detail a a href 11 new to this maybe this can help a a name 1 href 1 current live pages a a href https udacityfrontendscholarship github io task master b home page b a br a href https udacityfrontendscholarship github io task master login html b login page b a br a href https udacityfrontendscholarship github io task master signup html b signup page b a br a href https udacityfrontendscholarship github io task master profile html b user dashboard b a br a href https udacityfrontendscholarship github io task master thankyou html b thankyou page b a br a name 2 href 2 back end implementation a a href http taskmaster knowledgesaver com index php b taskmaster b a br a name 3 href 3 current status a home page login page signup page profile page thankyou page has been designed thanks to collective effort br also back end is implementated as well as deployed a name 4 href 4 next up a upgrade bug fixes testing minor improvements required a name 5 href 5 what you can do a fork this repo understand the code whatever work has to be done you can find it in the issues tab if you want to work on something by you own raise an issue wait for collaborators to approve it then go ahead with you code a name 6 href 6 usage a this is an online goal setting app where the number of goals and tasks to be done are written down and includes various additional features a name 7 href 7 background a taskmaster is an opensource software created with the help of udacity scholars in front end web development this software is made with a hope and a vision that the traditional way of setting everyday to do s and goals should be replaced with a more advanced version for the modern digitized era a name 8 href 8 color palette a a href https ibb co brdd2s shades of primary colors a a href https coolors co export png 030303 fbf5f3 f5cce8 f61067 2ca58d some funky colors a of the second link the teal both shades of pink will be used for border buttons only a name 9 href 9 key features a probable front end work home page landing page setup activating and linking the different sites from the home page linking the different additional pages to the homepage adding more tabs in the additional navigation bar making the layout of different additional pages beautifying pages in the best way possible and with special emphasis on visual appearance beautifying the user home page after someone has logged in refer to pg 3 of the pdf after backend work has been completed provides a basic structure designing the alarm feature along with popups implementing the notification of upcoming goal deadlines and important goals feed probable back end work getting the contact me form in the landing home page to actually be able to contact someone getting the login with google feature to actually work designing a sync feature with your gmail account attaching pictures with your goals devise a system to prioritize your goals using different colors for different priority of goals a name 10 href 10 features in detail a b homepage b this page contains three sections viz home introduction why taskmaster on the navigation bar and clicking any one of them would trigger a smooth scrolling down the home page these three sections fit into the first page of the web app as stated in the mockup pdf there should be a tick in home which when clicked on starts an animation explaining what taskmaster is all about below this a button entitled get started enables a user to visit the login sign up page lastly on the footer various media links like quora linkedin can be provided other modifications can be open ended and suggestions are welcome for improving the ui ux b login signup page b this page contains the login for the existing users or sign up for the new users option signing in via google is also supported there is also a button to share about this app on facebook it includes a live background with video or canvas elements b user dashboard b this page basically would be divided into three columns with a lot of interactivity the leftmost column will include the list of upcoming goals along with deadlines the rightmost column includes the list of prioritized goals which can be displayed as a pop up to the browser whenever a user opens up his her browser the middle column consists of a section for creating the goal itself with several buttons as follows calendar tool this tool will set the date on which the reminder will come up as a popup alarm popup this tool will set the required alarm if any the time of the alarm goal priority this tool will set the priority of the goal by assigning it a number which automatically updates the list in the rightmost column flash button this tool enables a pop up of prioritizing goal whenever a user opens up his her browser add media this tool enables a user to add any media files like images music videos attached with the goal when creating it create goal this tool will finally create the goal and the page would be updated only the first page would include a contact us section which can be accessed by clicking on it as shown in the mockup pdf a name 11 href 11 new to this maybe this can help a a href https gist github com rajrox97 02e3b2e3c6ef8a356106b65ad02e183a getting the hang of the code a a href https gist github com rajrox97 af2508ec2523d85bc43f1eb080ce5985 must read this before you commit a don t contact moderators if you see an anomaly in the code and if you think some function is not working properly just raise an issue on github that way more and more contributors can see that and the issue can be resolved faster refer to the pdf for a rough idea on the project refer to a href contributing md contributing md a for more information on how to contribute to this project br b the taskmaster team b | front_end |
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myrottenpotatoes | readme this readme would normally document whatever steps are necessary to get the application up and running things you may want to cover ruby version system dependencies configuration database creation database initialization how to run the test suite services job queues cache servers search engines etc deployment instructions | cloud |
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chia-blockchain-gui | chia blockchain gui chia logo https www chia net wp content uploads 2022 09 chia logo svg github contributors https img shields io github contributors chia network chia blockchain gui logo github welcome to the chia gui repo chia gui is written in typescript and uses electron react this monorepo consists of the following packages package name description api js ts library to access the chia blockchain rpc api react react library that wraps api in hooks core common react components and hooks gui the actual gui package it uses our packages like api react and core under the hood icons chia specific icons wallets common react components and hooks do not use this in you project will be merged to core package soon development 1 this repo chia blockchain gui must be under chia blockchain repo please follow the installation steps for the chia blockchain https github com chia network chia blockchain wiki install install from source make sure to install from source code git clone 2 run the sh install gui sh as instructed in the previous step this will clone the chia blockchain gui under chia blockchain repo 3 run npm run dev gui when developing please only edit the code with the vscode editor always have chia blockchain gui opened as a root folder in the vscode and not chia blockchain or chia blockchain gui packages failing to do so will result in incorrect auto linting and auto formatting which would not go through the ci quality checks when you open the repo in the vscode click on install recommended plugins pop up to develop in testnet please follow these steps https github com chia network chia blockchain wiki how to connect to the testnet please write tests for your code when disabling an eslint rule please provide a reason after two dashes example eslint disable next line react hooks exhaustive deps some dependencies intentionally left out installing npm packages to install an npm package please navigate to the root directory of this repo to install lodash for all packages npx lerna add lodash to install lodash for single package npx lerna add lodash scope chia network icons to install as a dev dependency add dev after adding a new npm package please pin down the package version this is done to lower the possibility of supply chain attacks common eslint issues react no array index key only use index as a key when all of the following conditions are met 1 the list and items are static hardcoded 2 the list is never reordered or filtered in all other cases you have to figure out what unique string you will use as a key or create a dedicated id import no extraneous dependencies packages that are used only in development should not be present on the production build you have 3 options 1 if it s a whole directory add it to the eslintrc json file 2 if it s a single file rename it by adding dev in the extension example file ts file dev ts 3 if it s a file that is run on the production use this if process env node env development eslint disable next line global require we cannot use import since it should be only loaded in development const package require package git workflow git branch from main for chia employees prefix your branch with your name like this yourname what is the code about this helps when cleaning up old branches all commits must be signed https docs github com en authentication managing commit signature verification signing commits git commit messages 1 separate subject from body with a blank line a single subject line is usually sufficient but if you need to include additional details add an empty line after the subject and enter the detailed message example capitalized short 70 chars or less summary more detailed explanatory text if necessary wrap it to about 72 characters or so in some contexts the first line is treated as the subject of an email and the rest of the text as the body the blank line separating the summary from the body is critical unless you omit the body entirely tools like rebase can get confused if you run the two together 2 commit subject line should always be able to complete the following sentence if applied this commit will your subject line here fixed bug with y fix bug in the contact form adding new field of x add new field discount code in the order form more fixes for broken stuff fix broken responsive layout localization do not edit files directly in the repo but instead please head over to our crowdin project https chia crowdin com chia blockchain gui and add edit translations there troubleshooting npm install in the root directory does not install packages correctly or other lerna issues please run npx lerna clean y rm rf node modules npm install npx lerna bootstrap npm run dev gui fails to start the app without providing a reason 1 in your command line please go to the chia blockchain directory one level up 2 run activate 3 run cd chia blockchain gui 4 run npm run dev gui to start the app 5 if still does not work please open you process manager and kill all chia python related processes why does my component keep rerendering we have why did you render https github com welldone software why did you render installed you will see the reasons in the electron console after adding this to your functional component yourcomponent whydidyourender logondifferentvalues true debugging 1 in the chia electron app click view developer developer tools 2 in the console tab of the developer tools change the default console events to include verbose events these are the events emitted from the debug package simulator simnet 1 please follow the install and configure the simulator https docs chia net guides simulator user guide do this step only once 2 in the chia blockchain directory run this to setup the env variables use these instead of the ones mentioned in the above guide export chia root chia simulator main export chia simulator root chia simulator export chia keys root chia keys simulator 3 activate 4 chia start simulator 5 cd chia blockchain gui packages gui 6 npm run dev skiplocales 7 you should see your simulator wallets you should not see your testnet mainnet wallets 8 run chia dev sim farm as many times you want to farm some coins chia faq wiki please check out the wiki https github com chia network chia blockchain wiki and faq https github com chia network chia blockchain wiki faq for information on this project | chia chia-blockchain | blockchain |
NBomber | p align center img src https github com pragmaticflow nbomber blob dev assets nbomber logo png alt nbomber logo width 600px p nuget https img shields io nuget v nbomber svg https www nuget org packages nbomber nbomber is a modern and flexible load testing framework for pull and push scenarios designed to test any system regardless of a protocol http websockets amqp etc or a semantic model pull push nbomber is free for personal use developer centric and extensible using nbomber you can test the reliability and performance of your systems and catch performance regressions and problems earlier nbomber will help you to build resilient and performant applications that scale nbomber 5 https cdn jsdelivr net gh pragmaticflow nbomber assets v5 0 assets nbomber 5 youtube png https youtu be z51pyzvznf8 links website https nbomber com documentation https nbomber com docs getting started overview why we build nbomber and what you can do with it the main reason behind nbomber is to provide a lightweight framework for writing load tests which you can use to test literally any system and simulate any production workload we wanted to provide only a few abstractions so that we could describe any type of load and still have a simple intuitive api another goal is to provide building blocks to validate your poc proof of concept projects by applying any complex load distribution with nbomber you can test any pull or push system http websockets graphql grpc sql databse mongodb redis etc with nbomber you can convert some of your integration tests to load tests easily nbomber as a modern framework provides zero dependencies on protocol http websockets amqp sql zero dependencies on semantic model pull push very flexible configuration and dead simple api distributed cluster support https nbomber com docs cluster overview real time reporting ci cd integration xunit and nunit runners are supported plugins extensions support add your own plugins or data sinks data feed support inject real or fake data into your tests debuggability of your load test debug your tests using your favorite ide what makes it very simple one of the design goals of nbomber is to keep api as minimal as possible because of this nbomber focuses on fully utilizing programming language c f constructs instead of reinventing a new dsl that should be learned in other words if you want to write a for loop you don t need to learn a dsl for this csharp var scenario scenario create hello world scenario async context you can define and execute any logic here for example send http request sql query etc nbomber will measure how much time it takes to execute your logic await task delay 1 000 return response ok withloadsimulations simulation inject rate 10 interval timespan fromseconds 1 during timespan fromseconds 30 nbomberrunner registerscenarios scenario run examples type language nbomber demo https github com pragmaticflow nbomber tree dev examples demo c | load-testing performance-testing integration-testing fsharp | os |
Shoe-Store | shoe store project overview the purpose of our project is to design a relational database that will provide backend engineering and support for an e commerce web application that sells a diverse selection of shoes as products the application will provide a means for users customers to create a profile browse and shop for shoes from the e commerce inventory browse and shop for shoes according to their customized profile and then complete a purchase order of one or more pairs of shoes a unique aspect of our e commerce design is the ability for customers to specify a certain preference for shoes sorted by shoe type size color etc in order to enhance user experience on the web application our e commerce web application design includes certain business functions that allow the customer to easily navigate the application review their purchase and order history participate in discounts and specials as well as manage their profile alt text https github com aditya viswanadha shoe store blob master shoe store master shoeissuebanner png | server |
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textflint | p align center img src images logo png alt textflint logo height 100 p h3 align center unified multilingual robustness evaluation toolkit for natural language processing h3 p align center a img src https github com textflint textflint actions workflows python package yml badge svg alt github runner covergae status a a href https www textflint io textflint img alt website src https img shields io website up message online url https 3a 2f 2fwww textflint io 2f a a img alt license src https img shields io badge license gpl 20v3 brightgreen a a href https badge fury io py textflint img alt github release latest by date src https img shields io github v release textflint textflint label release a p textflint is a multilingual robustness evaluation platform for natural language processing which unifies text transformation sub population adversarial attack and their combinations to provide a comprehensive robustness analysis so far textflint supports 13 nlp tasks if you re looking for robustness evaluation results of sota models you might want the textflint io https www textflint io textflint page features full coverage of transformation types including 20 general transformations 8 subpopulations and 60 task specific transformations as well as thousands of their combinations subpopulation which is to identify the specific part of dataset on which the target model performs poorly adversarial attack aims to find a perturbation of an input text that is able to fool the given model complete analytical report to accurately explain where your model s shortcomings are such as the problems in lexical rules or syntactic rules online demo you can test most of transformations directly on our online demo https www textflint io tutorials table of contents setup setup usage usage architecture architecture learn more learn more contributing contributing citation citation setup require python version 3 7 recommend install with pip shell pip install textflint once textflint is installed you can run it via command line textflint or integrate it inside another nlp project usage workflow img src images workflow png style zoom 50 the general workflow of textflint is displayed above evaluation of target models could be divided into three steps 1 for input preparation the original dataset for testing which is to be loaded by dataset should be firstly formatted as a series of json objects you can use the built in dataset following this instruction docs user components 4 sample dataset ipynb textflint configuration is specified by config target model is also loaded as flintmodel 2 in adversarial sample generation multi perspective transformations i e 80 transformation docs user components transformation md subpopulation docs user components subpopulation md and attackrecipe https github com qdata textattack are performed on dataset to generate transformed samples besides to ensure semantic and grammatical correctness of transformed samples validator docs user components validator md calculates confidence of each sample to filter out unacceptable samples 3 lastly analyzer collects evaluation results and reportgenerator automatically generates a comprehensive report of model robustness for example on the sentiment analysis sa task this is a statistical chart of the performance of xlnet with different types of transformation subpopulation attackrecipe on the imdb dataset img src images report png alt style zoom 100 we release tutorials of performing the whole pipeline of textflint on various tasks including machine reading comprehension docs user tutorials 9 mrc ipynb part of speech tagging docs user tutorials 7 bert 20for 20pos 20tagging ipynb named entity recognition docs user tutorials 11 ner ipynb chinese word segmentation docs user tutorials 10 cws ipynb quick start using textflint to verify the robustness of a specific model is as simple as running the following command shell textflint dataset input file config config json where input file is the input file of csv or json format config json is a configuration file with generation and target model options transformed datasets would save to your out dir according to your config json based on the design of decoupling sample generation and model verification textflint can be used inside another nlp project with just a few lines of code python from textflint import engine data path input json config config json engine engine engine run data path config for more input and output instructions of textflint please refer to the io format document docs user components ioformat md architecture img src images architecture png style zoom 50 input layer receives textual datasets and models as input represented as dataset and flintmodel separately dataset a container provides efficient and handy operation interfaces for sample dataset supports loading verification and saving data in json or csv format for various nlp tasks flintmodel a target model used in an adversarial attack generation layer there are mainly four parts in generation layer subpopulation generates a subset of a dataset transformation transforms each sample of dataset if it can be transformed attackrecipe attacks the flintmodel and generates a dataset of adversarial examples validator verifies the quality of samples generated by transformation and attackrecipe textflint provides an interface to integrate the easy to use adversarial attack recipes implemented based on textattack users can refer to textattack https github com qdata textattack for more information about the supported attackrecipe report layer analyzes model testing results and provides robustness report for users learn more section description documentation https textflint readthedocs io full api documentation and tutorials tutorial https github com textflint textflint tree master docs user the tutorial of textflint components and pipeline website https www textflint io textflint provides evaluation results of sota models and transformed data download online demo https www textflint io tutorials interactive demo to try single text transformations paper https aclanthology org 2021 acl demo 41 pdf our system paper which was received by acl2021 contributing we welcome community contributions to textflint in the form of bugfixes and new features if you want to contribute please first read our contribution guideline contributing md citation if you are using textflint for your work please kindly cite our acl2021 textflint demo paper https aclanthology org 2021 acl demo 41 pdf latex inproceedings wang etal 2021 textflint title textflint unified multilingual robustness evaluation toolkit for natural language processing author wang xiao and liu qin and gui tao and zhang qi and others booktitle proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing system demonstrations month aug year 2021 address online publisher association for computational linguistics url https aclanthology org 2021 acl demo 41 doi 10 18653 v1 2021 acl demo 41 pages 347 355 | model-robustness adversarial-samples text-transformations text-augmentation robustness-analysis data-augmentation transformation subpopulation attack | ai |
jdchain | logo http storage jd com jd block chain jdt jdchain png license https img shields io badge license apache 202 4eb1ba svg https www apache org licenses license 2 0 html maven central https maven badges herokuapp com maven central com jd blockchain sdk badge svg https maven badges herokuapp com maven central com jd blockchain sdk build status https travis ci com blockchain jd com jdchain svg branch master https travis ci org blockchain jd com jdchain jd chain git git submodule bash git clone https github com blockchain jd com jdchain git jdchain cd jdchain master master develop submodule git checkout master chmod x build sh skiptests update commit build build sh update build build sh skiptests build build sh update skiptests deploy deploy gateway deploy peer target jdchain gateway zip jdchain peer zip https blockchain jd com github io download https blockchain jd com github io install testnet id e5 91 bd e4 bb a4 e8 a1 8c e6 96 b9 e5 bc 8f e3 80 90 e6 8e a8 e8 8d 90 e3 80 91 jd chain 1 jd chain jdchain cli https blockchain jd com github io cli tx 2 sdk jd chain java go sdk java https github com blockchain jd com jdchain samples go sdk framework go https github com blockchain jd com framework go blob master sdk test tx test go jd chain issue jd chain docs https ledger jd com doc jd chain https ledger jd com https blockchain jd com jdchain support jd com | blockchain |
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Mastering-Blockchain | mastering blockchain this is the code repository for mastering blockchain https www packtpub com big data and business intelligence mastering blockchain utm source github utm medium repository utm content 9781787125445 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 about the book blockchain is a distributed database that enables permanent transparent and secure storage of data the blockchain technology is the backbone of cryptocurrency in fact it s the shared public ledger upon which the entire bitcoin network relies and it s gaining popularity with people who work in finance government and the arts blockhchain technology uses cryptography to keep data secure blockchain is a distributed database that enables permanent transparent and secure storage of data the blockchain technology is the backbone of cryptocurrency in fact it s the shared public ledger upon which the entire bitcoin network relies and it s gaining popularity with people who work in finance government and the arts blockhchain technology uses cryptography to keep data secure blockchain is a distributed database that enables permanent transparent and secure storage of data the blockchain technology is the backbone of cryptocurrency in fact it s the shared public ledger upon which the entire bitcoin network relies and it s gaining popularity with people who work in finance government and the arts blockhchain technology uses cryptography to keep data secure this book gives a detailed description of this leading technology and its implementation in the real world this book begins with the technical foundations of blockchain teaching you the fundamentals of cryptography and how it keeps data secure you will learn about the mechanisms behind cryptocurrencies and how to develop applications using ethereum a decentralized virtual machine you will explore different blockchain solutions and get an exclusive preview into hyperledger an upcoming blockchain solution from ibm and the linux foundation you will also be shown how to implement blockchain beyond currencies scability with blockchain and the future scope of this fascinating and powerful technology instructions and navigation all of the code is organized into folders each folder starts with a number followed by the application name for example chapter03 the code will look like the following sudo apt get install build essential pkg config libc6 dev m4 g multilib autoconf libtool ncurses dev unzip git python zlib1g dev wget bsdmainutils automake all examples in this book have been developed on ubuntu 16 04 1 lts xenial as such it is recommended to use ubuntu however any appropriate operating system either windows or linux can be used but examples especially those related to installation may need to be changed accordingly examples related to cryptography have been developed using the openssl 1 0 2g 1 mar 2016 command line tool ethereum solidity examples have been developed using browser solidity available online at https ethereum github io browser solidity ethereum s homestead release is used to develop ethereum related examples at the time of writing this is the latest version available and can be downloaded from https www ethereum org examples related to iot have been developed using a raspberry pi kit by vilros but any latest model or kit can be used specifically raspberry pi 3 model b v 1 2 has been used to build a hardware example of iot node js v7 2 1 and npm v3 10 10 have been used to download related packages and run node js server for iot examples the truffle framework has been used in some examples of smart contract deployment and is available at http truffleframework com any latest version available via npm should be appropriate related products practical blockchain development https www packtpub com application development practical blockchain development utm source github utm medium repository utm content 9781786465603 building blockchain projects https www packtpub com big data and business intelligence building blockchain projects utm source github utm medium repository utm content 9781787122147 ethereum programming https www packtpub com application development ethereum programming utm source github utm medium repository utm campaign 9781786463715 suggestions and feedback click here https docs google com forms d e 1faipqlse5qwunkgf6puvzpirpdtuy1du5rlzew23ubp2s p3wb gcwq viewform if you have any feedback or suggestions 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 9781788839044 https packt link free ebook 9781788839044 a p | blockchain |
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ghg-emissions | greenhouse gas emissions data comparison this is an end to end data engineering and analytical project which aims to provide comparison of two different sources with greenhouse gas emissions data and visualize the greenhouse gas contributions of different sectors and agrifood systems in particular disclaimer in order to focus on the data engineering and analytical tasks i disregard some technical details about the datasets and make some intentional assumptions about them that might not be correct please use the results with caution overview the whole project was built using google cloud platform services and dbt schema of services integration assets services schema png schema of services integration schema of services integration data sources climate watch data accessed using api call with optional parameters from the https www climatewatchdata org api v1 data historical emissions https www climatewatchdata org api v1 data historical emissions url address faostat data had to be downloaded manually from the https www fao org faostat en data gt https www fao org faostat en data gt url address data ingestion data were ingested using google cloud function etl scripts written in python transformed data was stored in google bigquery data warehouse in a dataset called ghg the scripts allow for incremental data refresh if the source data either requeted via api call or downloaded from the faostat website was already in target tables it got updated no data is deleted in this process the transformation scripts were slightly different for each data source climate watch cw data load scripts cw data load data fetched in the script and pushed to cw data stg table afterwards data was merged with target table cw data faostat fao data load scripts fao data load the data was uploaded to a google cloud storage bucket that triggered the etl script the etl script transformed and enriched the data finally the data was uploaded to fao data stg table and merged with fao data table additional dimension tables were added to the ghg dataset tables of the ghg dataset assets bigquery ghg png tables of the ghg dataset tables of the ghg dataset data modeling data models were created using dbt developed in dbt cloud sql techniques used ctes window functions for calculating running total moving average delta values yoy rank various joins unions intermediate models were stored in stg dbt models stg subfolder and deployed in analytics stg dataset of the bigquery project there are models focusing on evaluating data quality stored in data quality dbt models data quality subfolder and deployed in analytics data quality dataset the main analytical models are in core dbt models core subfolder and deployed in analytics dataset tables and views of the analytical models assets bigquery analytics png tables and views of the analytical models tables and views of the analytical models analysis and visualizations the outputs of the data modeling step were analyzed and visualized in google looker studio the full datasets included greenhouse gas emissions data from 1990 until 2020 the development dataset was just a fraction of that further described in the following text the full google looker studio report can be found here https lookerstudio google com reporting d80d2d2d 9fb4 44df 8e47 020009182925 datasets comparison overview assets looker analysis png datasets comparison overview datasets comparison overview detailed description of the development process 1 context even though this is just a test project let s build some real foundations for its justification the scientists have already discovered in several studies that if the trend of global warming continues there will be pretty bad consequences for human kind since we haven t invented any tools or mechanisms to cool down the planet the only possible action we can take is to lower the amount of emitted greenhouse gases that contribute towards the warming of the planet one of the areas that might be ideal for optimisation is the food consumption usually it s almost no problem to get the products we want at a certain moment even though they were produced on the other side of the planet the food availability causes some unnecessary greenhouse gases emission that are either related to the transportation of the goods or by over production 2 the project goal now the fun part begins as mentioned in the disclaimer there are some assumptions about the datasets and due to simplification some information in this section is made up before making any changes like prohibiting international trade we first want to see the composition of emissions related to different sectors in order to understand the significance of the food production there is also suspicion that countries do not report their emissions correctly in order to look better compared to other countries but luckily there is one organisation that have more precise numbers of emitted greenhouse gases so our goal is also to see how off the countries reports are the goals 1 visualize the proportion of co sub 2 sub equivalent emissions related to agriculture against other sectors 2 compare the data from two different sources and provide some key metrics about the differences there are several gases that are generally considered as greenhouse each with different magnitude of contribution towards global warming the common method to normalize the numbers is to multiply the values with a constant that is related to that specific gas resulting in an equivalent of emitted co sub 2 sub the multiplication factors that are used in both datasets were taken from the fifth assessment report by intergovernmental panel on climate change further abbreviated as ipcc 3 exploring the data first i had to understand what is the structure of the data and how the comparison of the two different datasets would look like the data about emissions related to agriculture and food systems are maintained by the food and agriculture organization of the united nations and can be downloaded from their website https www fao org faostat en data gt further abbreviated as fao the fao dataset contains not only agrifood systems data but the emissions data of other sectors that some countries have to report to the ipcc as well the second organisation that gathers data about greenhouse gas emissions is climate watch in my simplified scenario this is the organisation with more precise numbers about emissions the data can be downloaded either directly from their website https www climatewatchdata org data explorer or accessed via api https www climatewatchdata org api v1 data historical emissions in order to get a better understanding of the data and how both datasets fit together i downloaded sample data for canada s 2020 emissions from both portals and put them together in a google sheets file https docs google com spreadsheets d 1zcka8kzinzwqkovcgzbc2xnqtrsqdnlt5au4ie9xvla edit gid 0 the results of my findings are in the following two subsections in both cases i was interested only in downloading data for co sub 2 sub equivalents of emitted gases fao data the data in raw format was not very user friendly there was no indication about what are the aggregated numbers and what are individual categories following along with a table that is attached to the methodological note of faostat s emissions totals domain https fenixservices fao org faostat static documents gt gt e pdf i was able to map individual lower level categories to higher level ipcc categories i have therefore introduced the grading system and assigned appropriate level to each item in a dimension table i have made from selecting distinct values of item and item code mapping of fao categories to ipcc sectors assets fao categories mapping to ipcc png mapping of fao categories to ipcc sectors mapping of fao categories to ipcc sectors source faostat key findings 1 some categories might be missing there was no entry for rice cultivation emissions probably because of canada s lack of rice fields 2 emissions for food retail category was present only in aggregated form all gases together but according to the mapping table some elements were part of the energy ipcc sector co sub 2 sub ch sub 4 sub n sub 2 sub o and some were part of industrial processes ipcc sector f gases therefore additional download of the food retail category with all the elements was necessary additional modifications were to be made in the following etl process 3 level 2 items summed up only for agriculture and lulucf ipcc categories in order to allow the drill down feature while still displaying the totals i had to add three new items energy emissions not related to agriculture ippu emissions not related to agriculture waste emissions not related to agriculture that were to be calculated in the following etl process climate watch data the climate watch data contained only the values for similar categories as ipcc categories in fao dataset and more detailed values for energy sector therefore it was ready to be used without any additional changes 4 ingesting data etl python scripts for ingesting both fao and climate watch datasets were deployed as google functions i had some issues with running the scripts with the basic 256 mib memory allocation so i had to increase the allocation to 512 mib probably becasue of the pandas library overview of the google functions for etl process assets google functions png overview of the google functions for etl process overview of the google functions for etl process fao data unfortunately there is no api to access the data automatically and therefore it is necessary to download the data manually but the rest of the etl process was automated i have created a google cloud function that is triggered by file uploaded to a specific google cloud storage bucket climate watch data google cloud function with parameters that are used in api calls to climate watch portal start year end year regions the function transforms the data and uploads them to the google bigquery dataset 5 creating models data models were created using dbt cloud there are references to the mapping google sheets file https docs google com spreadsheets d 1zcka8kzinzwqkovcgzbc2xnqtrsqdnlt5au4ie9xvla edit gid 0 items mapping sheet in this section in order to save the compute resources the initial testing of the models was done on a small amount of data the testing dataset consisted of three countries canada can nepal npl and guinea gin the period covered was 2016 to 2020 data quality models first set of models were designed to test the quality of the data and some asumptions made in earlier part of the project the idea behind testing the data quality is to verify that the values of higher granularity items i call these level 2 items in this project match the items of lower granularity and that the dataset is complete and both levels can be used interchangebly i e some calculations can be based on level 1 items while others on level 2 items discovery of any methodological or process errors made by any of the data collectors faostat and climate watch is not the purpose of this testing therefore if some higher granularity items were not included in calculating the lower granularity items by accident it won t be discovered data from the fao dataset are possible to view from two different angles first one is the ipcc point of view where sum of the detailed level 2 items columns j k of the mapping sheets file should match the related level 1 items second one is the fao point of view which has different level 1 categories columns n o of the mapping sheets file that are composed of only some of the level 2 items climate watch dataset has entries that can be linked only to the fao s ipcc categories columns d e of the mapping sheets file therefore the data quality models covers only one dimension results of all the data quality tests were combined in one master table only one of the tests was showing errors results of all data quality tests assets data quality master result png table that includes results of all data quality tests results of all data quality tests however the errors were pretty insignificant and i didn t catch any calculation error i tested one random example and the numbers were correct errors of fao fao test 1 model assets fao fao test 1 result png errors of fao fao test 1 model errors of fao fao test 1 model core models the key analytical data models that aim to compare the fao data and climate watch data they were built on top of raw data in the ghg bigquery dataset as well as intermediate models materialized as tables and stored in analytics stg dataset the main data model datasets comparison per country and year basis has several calculated columns cw value aggregated value of level 1 items of all ipcc sectors from the climate watch dataset fao value aggregated value of level 1 items of all ipcc sectors from the fao dataset values difference difference between cw value and fao value negative value implies that values reported in faostat were lower than those in climate watch relative difference the values difference value divided by the fao value running total difference sum of values difference values for the year on the current row and all of its preceding years ma5 values difference moving average for 5 values current row value 2 preceding and 2 following cw yoy difference yoy difference of cw value fao yoy difference yoy difference of fao value 6 analysis and visualization the data models outputs were finally used as a data source for google looker studio report datasets comparison one of the project s goals was to compare the datasets for that i have selected relative difference as a comparison metric from the metric and the line chart it is clear that the emissions reported in climate watch were in general higher than in faostat therefore my hypothesis about contries not reporting the emissions corectly was not confirmed however there were some countries that had positive relative difference top 5 are displayed at the lower half of the page datasets comparison page assets looker datasets comparison full png datasets comparison page datasets comparison page agrifood systems emissions the second goal was to display the share of emissions related to agrifood systems agrifood systems emissions and its subcategories assets looker agrifood systems png agrifood systems emissions and its subcategories agrifood systems emissions and its subcategories | cloud |
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easyrobust | easyrobust div align center license https img shields io github license alibaba easyrobust svg https github com alibaba easyrobust blob main license open issues https isitmaintained com badge open alibaba easyrobust svg https github com alibaba easyrobust issues github pull requests https img shields io github issues pr alibaba easyrobust svg https github com alibaba easyrobust pull github latest release https badgen net github release alibaba easyrobust https github com alibaba easyrobust releases div what s new jul 2023 coco o a benchmark for object detectors under natural distribution shifts https arxiv org abs 2307 12730 was accepted by iccv 2023 dataset will be avaliable at benchmarks coco o benchmarks coco o jul 2023 robust automatic speech recognition via wavaugment guided phoneme adversarial training https arxiv org abs 2307 12498 was accepted by interspeech 2023 codes will be avaliable at examples asr wapat examples asr wapat feb 2023 imagenet e benchmarking neural network robustness against attribute editing https arxiv org abs 2303 17096 was accepted by cvpr 2023 codes will be avaliable at benchmarks imagenet e benchmarks imagenet e feb 2023 transaudio towards the transferable adversarial audio attack via learning contextualized perturbations https arxiv org abs 2303 15940 was accepted by icassp 2023 codes will be avaliable at examples attacks transaudio examples attacks transaudio jan 2023 inequality phenomenon in l infty adversarial training and its unrealized threats https openreview net pdf id 4t9q35bxgr was accepted by iclr 2023 as notable top 25 codes will be avaliable at examples attacks inequality examples attacks inequality oct 2022 towards understanding and boosting adversarial transferability from a distribution perspective https arxiv org abs 2210 04213 was accepted by tip 2022 codes will be avaliable at examples attacks dra examples attacks dra sep 2022 boosting out of distribution detection with typical features https arxiv org abs 2210 04200 was accepted by neurips 2022 codes avaliable at examples ood detection bats examples ood detection bats sep 2022 enhance the visual representation via discrete adversarial training https arxiv org abs 2209 07735 was accepted by neurips 2022 codes avaliable at examples imageclassification imagenet dat examples imageclassification imagenet dat sep 2022 updating 5 methods for analysing your robust models under tools tools sep 2022 updating 13 reproducing examples of robust training methods under examples imageclassification imagenet examples imageclassification imagenet sep 2022 releasing 16 adversarial training models including a swin b which achieves sota adversairal robustness with 47 42 on autoattack sep 2022 easyrobust v0 2 0 released our research project iccv 2023 coco o a benchmark for object detectors under natural distribution shifts paper https arxiv org abs 2307 12730 coco o dataset benchmarks coco o interspeech 2023 robust automatic speech recognition via wavaugment guided phoneme adversarial training paper https arxiv org abs 2307 12498 code examples asr wapat cvpr 2023 imagenet e benchmarking neural network robustness via attribute editing paper https arxiv org abs 2303 17096 image editing toolkit benchmarks imagenet e imagenet editing imagenet e dataset https drive google com file d 19m1fqb8c mir6ermrsuktqrei ifxet0 view usp sharing iclr 2023 inequality phenomenon in l infty adversarial training and its unrealized threats paper https openreview net pdf id 4t9q35bxgr code examples attacks inequality icassp 2023 transaudio towards the transferable adversarial audio attack via learning contextualized perturbations paper https arxiv org abs 2303 15940 code examples attacks transaudio tip 2022 towards understanding and boosting adversarial transferability from a distribution perspective paper https arxiv org abs 2210 04213 code examples attacks dra neurips 2022 boosting out of distribution detection with typical features paper https arxiv org abs 2210 04200 code examples ood detection bats neurips 2022 enhance the visual representation via discrete adversarial training paper https arxiv org abs 2209 07735 code examples imageclassification imagenet dat cvpr 2022 towards robust vision transformer paper https arxiv org abs 2105 07926 code examples imageclassification imagenet rvt introduction easyrobust is an easy to use library for state of the art robust computer vision research with pytorch https pytorch org easyrobust aims to accelerate research cycle in robust vision by collecting comprehensive robust training techniques and benchmarking them with various robustness metrics the key features includes reproducible implementation of sota in robust image classification most existing sota in robust image classification are implemented adversarial training https arxiv org abs 1706 06083 advprop https arxiv org abs 1911 09665 sin https arxiv org abs 1811 12231 augmix https arxiv org abs 1912 02781 deepaugment https arxiv org abs 2006 16241 drvit https arxiv org abs 2111 10493 rvt https arxiv org abs 2105 07926 fan https arxiv org abs 2204 12451 apr https arxiv org abs 2108 08487 hat https arxiv org abs 2204 00993 prime https arxiv org abs 2112 13547 dat https arxiv org abs 2209 07735 and so on benchmark suite variety of benchmarks tasks including imagenet a https arxiv org abs 1907 07174 imagenet r https arxiv org abs 2006 16241 imagenet sketch https arxiv org abs 1905 13549 imagenet c https arxiv org abs 1903 12261 imagenetv2 https arxiv org abs 1902 10811 stylized imagenet https arxiv org abs 1811 12231 objectnet https objectnet dev scalability you can use easyrobust to conduct 1 gpu training multi gpu training on single machine and large scale multi node training model zoo open source more than 30 pretrained adversarially or non adversarially robust models analytical tools support analysis and visualization about a pretrained robust model including attention visualization tools decision boundary visualization tools convolution kernel visualization tools shape vs texture biases analysis tools etc using these tools can help us to explain how robust training improves the interpretability of the model technical articles we have a series of technical articles on the functionalities of easyrobust neurips2022 https mp weixin qq com s ewbajwq2yg z92uw7wdtxq tip 2022 https mp weixin qq com s qttxn3b4oyibazghzo9cga vit cvpr 2022 https mp weixin qq com s j6gqa09mxlwmn c40sjf1q neurips2022 https mp weixin qq com s biz mzu1ntmyoti4mw mid 2247610520 idx 1 sn d7ff15f6a89030a01ca03de406f2f4ec installation install from source bash git clone https github com alibaba easyrobust git cd easyrobust pip install e install from pypi bash pip install easyrobust download the imagenet dataset and place into path to imagenet specify imagenetdatadir as imagenet path by bash export imagenetdatadir path to imagenet optional if you use easyrobust to evaluate the model robustness download the benchmark dataset by bash sh download data sh optional if you use analysis tools in tools install extra requirements by bash pip install r requirements optional txt docker we have provided a runnable environment in docker dockerfile for users who do not want to install by pip to use it please confirm that docker and nvidia docker have installed then run the following command bash docker build t alibaba easyrobust v1 f docker dockerfile getting started easyrobust focuses on the basic usages of 1 evaluate and benchmark the robustness of a pretrained models and 2 train your own robust models or reproduce the results of previous sota methods 1 how to evaluate and benchmark the robustness of given models it only requires a few lines to evaluate the robustness of a model using easyrobust we give a minimalist example in benchmarks resnet50 example py benchmarks resnet50 example py python define your model model torchvision models resnet50 pretrained true model model eval if torch cuda is available model model cuda start evaluation ood evaluate imagenet val model benchmarks data imagenet val evaluate imagenet a model benchmarks data imagenet a evaluate imagenet r model benchmarks data imagenet r evaluate imagenet sketch model benchmarks data imagenet sketch evaluate imagenet v2 model benchmarks data imagenetv2 evaluate stylized imagenet model benchmarks data imagenet style evaluate imagenet c model benchmarks data imagenet c objectnet is optional since it spends a lot of disk storage we skip it here evaluate objectnet model benchmarks data objectnet images adversarial evaluate imagenet autoattack model benchmarks data imagenet val you can do evaluation by simply running the command python benchmarks resnet50 example py after running is completed your will get the following output top1 accuracy on the imagenet val 76 1 top1 accuracy on the imagenet a 0 0 top1 accuracy on the imagenet r 36 2 top1 accuracy on the imagenet sketch 24 1 top1 accuracy on the imagenet v2 63 2 top1 accuracy on the stylized imagenet 7 4 top1 accuracy 39 2 mce 76 7 on the imagenet c top1 accuracy on the autoattack 0 0 2 how to use easyrobust to train my own robust models we implement most robust training methods in the folder examples imageclassification imagenet all of them are based on a basic training script examples imageclassification imagenet base training script py examples imageclassification imagenet base training script py by comparing the difference you can clearly see where and which hyperparameters of basic training are modified to create a robust training example below we present the tutorials of some classic methods adversarial training on imagenet using 8 gpus examples imageclassification imagenet adversarial training augmix training on imagenet with 180 epochs examples imageclassification imagenet augmix advprop for improving non adversarial robustness and accuracy examples imageclassification imagenet advprop using stylized imagenet as extended data for training examples imageclassification imagenet sin discrete adversarial training for vits examples imageclassification imagenet dat training robust vision transformers rvt with 300 epochs examples imageclassification imagenet rvt robust finetuning of clip models examples imageclassification imagenet wiseft analytical tools see tools readme md tools model zoo and baselines submit your models we provide a tool benchmarks benchmark py to help users directly benchmark their models usage python benchmarks benchmark py options options model arch in timm data dir path of the bencmark datasets ckpt path url or path of the model weights if you are willing to submit the model to our benchmarks you can prepare a python script similar to benchmarks benchmark py and weights file xxx pth zip all the files then open an issue with the submit model template and provide a json storing submit information below is a submission template in adversarial robustness benchmark of image classification markdown submit json information date 19 06 2017 extra data no model b adversarial training b institution mit paper link https arxiv org abs 1706 06083 code link architecture swin b training framework easyrobust v1 imagenet val 75 05 autoattack 47 42 files a href http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain swin base patch4 window7 224 ep4 pth download a advrob imgcls leaderboard true oodrob imgcls leaderboard false advrob objdet leaderboard false oodrob objdet leaderboard false we will check the result and present your result into the benchmark if there is no problem for submission template of other benchmarks check submit model md github issue template submit model md below is the model zoo and benchmark of the easyrobust all the results are runned by benchmarks adv robust bench sh benchmarks adv robust bench sh and benchmarks non adv robust bench sh benchmarks non adv robust bench sh adversarial robust benchmark sorted by autoattack training framework method model imagenet val autoattack files easyrobust v1 adversarial training https arxiv org abs 1706 06083 swin b https arxiv org abs 2103 14030 75 05 47 42 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain swin base patch4 window7 224 ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 swin s https arxiv org abs 2103 14030 73 41 46 76 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain swin small patch4 window7 224 ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 vit b 16 https arxiv org abs 2010 11929 70 64 43 04 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain vit base patch16 224 ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 efficientnet b3 https arxiv org abs 1905 11946 67 65 41 72 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain efficientnet b3 ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 resnet101 https arxiv org abs 1512 03385 69 51 41 04 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain resnet101 ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 vit s 16 https arxiv org abs 2010 11929 66 43 39 20 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain vit small patch16 224 ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 efficientnet b2 https arxiv org abs 1905 11946 64 75 38 54 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain efficientnet b2 ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 resnest50d https arxiv org abs 2004 08955 70 03 38 52 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain resnest50d ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 vit b 32 https arxiv org abs 2010 11929 65 58 37 38 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain vit base patch32 224 ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 efficientnet b1 https arxiv org abs 1905 11946 63 99 37 20 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain efficientnet b1 ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 seresnet101 https arxiv org abs 1709 01507 71 11 37 18 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain seresnet101 ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 resnext50 32x4d https arxiv org abs 1611 05431 67 39 36 42 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain resnext50 32x4d ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 efficientnet b0 https arxiv org abs 1905 11946 61 83 35 06 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain efficientnet b0 ep4 pth robustness https github com madrylab robustness adversarial training https arxiv org abs 1706 06083 resnet50 https arxiv org abs 1512 03385 64 02 34 96 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models robustbench robustness advtrain resnet50 linf eps4 0 pth easyrobust ours adversarial training https arxiv org abs 1706 06083 resnet50 https arxiv org abs 1512 03385 65 1 34 9 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples adversarial training model best pth tar args http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples adversarial training args yaml logs http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples adversarial training summary csv easyrobust v1 adversarial training https arxiv org abs 1706 06083 seresnet50 https arxiv org abs 1709 01507 66 68 33 56 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain seresnet50 ep4 pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 densenet121 https arxiv org abs 1608 06993 60 90 29 78 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain densenet121 ep4 pth official https github com mahyarnajibi freeadversarialtraining free at https arxiv org abs 1904 12843 resnet50 https arxiv org abs 1512 03385 59 96 28 58 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models robustbench free adv step4 eps4 repeat4 pth official https github com locuslab fast adversarial fgsm at https arxiv org abs 2001 03994 resnet50 https arxiv org abs 1512 03385 55 62 26 24 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models robustbench fastadv imagenet model weights 4px pth easyrobust v1 adversarial training https arxiv org abs 1706 06083 vgg16 https arxiv org abs 1409 1556 59 96 25 92 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng imagenet pretrained models advtrain models advtrain vgg16 ep4 pth non adversarial robust benchmark sorted by imagenet c training framework method model files imagenet val v2 c mce r a sketch stylized objectnet easyrobust ours dat http arxiv org abs 2209 07735 vit b 16 https arxiv org abs 2010 11929 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples dat model best pth tar args http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples dat args yaml logs http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples dat summary csv 81 38 69 99 45 59 49 64 24 61 36 46 24 84 20 12 easyrobust ours rvt s https arxiv org abs 2105 07926 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples rvt model best pth tar args http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples rvt args yaml logs http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples rvt summary csv 82 10 71 40 48 22 47 84 26 93 35 34 20 71 23 24 official https github com vtddggg robust vision transformer rvt s https arxiv org abs 2105 07926 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models official models rvt small plus pth 81 82 71 05 49 42 47 33 26 53 34 22 20 48 23 11 easyrobust ours drvit s https arxiv org abs 2111 10493 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples drvit model best pth tar args http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples drvit args yaml logs http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples drvit summary csv 80 66 69 62 49 96 43 68 20 79 31 13 17 89 20 50 drvit s https arxiv org abs 2111 10493 77 03 64 49 56 89 39 02 11 85 28 78 14 22 26 49 official https github com amodas prime augmentations prime https arxiv org abs 2112 13547 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models official models prime pth 76 91 65 42 57 49 42 20 2 21 29 82 13 94 16 59 easyrobust ours prime https arxiv org abs 2112 13547 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples prime model best pth tar args http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples prime args yaml logs http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples prime summary csv 76 64 64 37 57 62 41 95 2 07 29 63 13 56 16 28 easyrobust ours deepaugment https arxiv org abs 2006 16241 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples deepaugment model best pth tar args http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples deepaugment args yaml logs http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples deepaugment summary csv 76 58 64 77 60 27 42 80 3 62 29 65 14 88 16 88 official https github com hendrycks imagenet r deepaugment https arxiv org abs 2006 16241 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models official models deepaugment pth 76 66 65 24 60 37 42 17 3 46 29 50 14 68 17 13 easyrobust ours augmix https arxiv org abs 1912 02781 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples augmix model best pth tar args http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples augmix args yaml logs http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples augmix summary csv 77 81 65 60 64 14 43 34 4 04 29 81 12 33 17 21 easyrobust ours apr https arxiv org abs 2108 08487 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples apr model best pth tar args http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples apr args yaml logs http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples apr summary csv 76 28 64 78 64 89 42 17 4 18 28 90 13 03 16 78 official https github com google research augmix augmix https arxiv org abs 1912 02781 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models official models augmix pth 77 54 65 42 65 27 41 04 3 78 28 48 11 24 17 54 official https github com icgy96 apr apr https arxiv org abs 2108 08487 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models official models apr sp pth 75 61 64 24 65 56 41 35 3 20 28 37 13 01 16 61 official https github com liyingwei shapetexturedebiasedtraining s t debiased https arxiv org abs 2010 05981 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models official models debiased pth 76 91 65 04 67 55 40 81 3 50 28 41 17 40 17 38 easyrobust ours sin in https arxiv org abs 1811 12231 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples sin model best pth tar args http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples sin args yaml logs http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples sin summary csv 75 46 63 50 67 73 42 34 2 47 31 39 59 37 16 17 official https github com rgeirhos texture vs shape sin in https arxiv org abs 1811 12231 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models official models sin in pth 74 59 62 43 69 32 41 45 1 95 29 69 57 38 15 93 non official https github com tingxueronghua pytorch classification advprop advprop https arxiv org abs 1911 09665 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models official models advprop pth 77 04 65 27 70 81 40 13 3 45 25 95 10 01 18 23 easyrobust ours s t debiased https arxiv org abs 2010 05981 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples shape texture debiased training model best pth tar args http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples shape texture debiased training args yaml logs http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples shape texture debiased training summary csv 77 21 65 10 70 98 38 59 3 28 26 09 14 59 16 99 easyrobust ours advprop https arxiv org abs 1911 09665 resnet50 https arxiv org abs 1512 03385 ckpt http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples advprop model best pth tar args http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples advprop args yaml logs http alisec competition oss cn shanghai aliyuncs com xiaofeng easy robust benchmark models ours examples advprop summary csv 76 64 64 35 77 64 37 43 2 83 24 71 7 33 16 82 credits easyrobust concretizes previous excellent works by many different authors we d like to thank in particular the following implementations which have helped us in our development timm https github com rwightman pytorch image models rwightman and the training script robustness https github com madrylab robustness madrylab and autoattack https github com fra31 auto attack fra31 for attack implementation modelvshuman https github com bethgelab model vs human bethgelab for model analysis adain https github com naoto0804 pytorch adain naoto0804 for style trnsfer and vqgan https github com compvis taming transformers compvis for image discretization all the authors and implementations of the robustness research work we refer in this library citing easyrobust we provide a bibtex entry for users who apply easyrobust to help their research bibtex misc mao2022easyrobust author xiaofeng mao and yuefeng chen and xiaodan li and gege qi and ranjie duan and rong zhang and hui xue title easyrobust a comprehensive and easy to use toolkit for robust computer vision howpublished url https github com alibaba easyrobust year 2022 | deep-learning image-classification imagenet adversarial-robustness robustness pretrained-models | ai |
NLP2018SPRING | nlp 1 fine grained sentiment analysis on financial microblogs link https github com thtang nlp tree master project1 2 semantic relations link https github com thtang nlp tree master project2 | nlp sentiment-analysis semantic-relationship-extraction | ai |
ice | go to manual pages http goosman lei github io ice index html php web ice http static cdn tec inf com post img 0009 ice core development progress png 1 tpl build conf 2 bin ice skel 3 tpl build conf 4 git 5 1 service web server src webroot service php 2 web web server src webroot web php app proxy resource app proxy service feature ice runner feature isenable ios ge 7 logger config util util mysqli db query sql db app proxy filter | front_end |
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fridgehound | fridgehound the smart tupperware authors orlando g rodriguez pedro henrique ribeiro pinto description have you ever forgotten something on the fridge and by time you opened it the food had gone bad or maybe you opened a tupperware and wasn t too sure if the food was still good well you are not alone the average american household wastes around 25 of the food they purchase this was the main reason why we decide to create fridgehound fridgehound is a smart tupperware that uses the mq 135 gas sensor to track your food with an easy to use interface you can store different type of foods and the sensor will estimate how much time you have left we also added a thermometer so you can keep track of your fridge s temperature the best part is that you can access all this information from a web application anywhere you go the gas sensor and thermometer send information via serial usb to a raspberry pi 2 which sends that information to our web application s database this way you always have the most up to date information anywhere you go system diagram p align center img src images system diagram png alt system diagram style width 800px p hardware components p align center img src images fridgehound parts png alt hardware components style width 750px p hardware prototype p align center img src images hw prototype png alt prototype style width 750px p pin out connections gas sensor mq 135 mbed sd p15 out gnd gnd vout vcc vu 5v temperature sensor tmp 36 mbed mq 135 gnd gnd vout 3 3v p18 out ulcd connections mbed ulcd vu 5v gnd gnd tx p9 rx rx p10 tx p11 reset up pushbutton connections mbed up pushbutton p8 p1 gnd p2 down pushbutton connections mbed down pushbutton p28 p1 gnd p2 left pushbutton connections mbed left pushbutton p29 p1 gnd p2 right pushbutton connections mbed right pushbutton p24 p1 gnd p2 select pushbutton connections mbed select pushbutton p20 p1 gnd p2 homepage pushbutton connections mbed homepage pushbutton p19 p1 gnd p2 usb serial connection mbed raspberry pi usb mini out usb 2 0 in demos smart tupperware smart tupperware demo https img youtube com vi 5hi07tz1wo8 0 jpg https www youtube com embed 5hi07tz1wo8 web interface web app demo https img youtube com vi qvngdb rom8 0 jpg https www youtube com embed qvngdb rom8 | cpp d3js embedded-systems firebase iot mbed raspberry-pi smart-device web-application arm | os |
cuda-3d | cuda 3d x tutorials of cuda fundamentals x hello world x atomic functions x thrust x cublas cudnn memory popular operators for 3d point clouds x uniform sampling x euclidean distance x chamfer distance earth mover distance euclidean clustering depth clustering structure operators chamfer distance cu euclidean distance cu makefile python chamfer distance py init py readme md readme md uniform sample cu readme md tutorials atomic atomic101 cu atomic find max cu makefile readme md cublas find max mag cu makefile readme md hello world coordinating parallel cu error101 cu error macro cu grid stride cu hello world cu loop accelerate cu makefile matrix mul cu memory101 cu mismatched config cu readme md troubleshoot md vector add cu memory thrust find max cu makefile readme md | cloud |
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CollegeAppIIITN | collegeapp iiitn android application for college iiitn a rewrite is going on new things introduced jetpack compose good architectural practices interactions with custom backend written in ktor collegeappbackend https github com 4shutosh collegeappbackend i might update some designs here canva https www canva com design daesik6xb8w 0vv2jswh9kkvp4w df7pcg watch utm content daesik6xb8w utm campaign designshare utm medium link utm source sharebutton here is a kanban for the project notion link https majestic echium 9ca notion site 2bbb7e02931449c4a2cbe77d06c6ca95 v a3c4da09d9b24e709ade955e8d7e3760 also trying out the new github kanban in projects tab | server |
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kitsune | kitsune kitsune is the image that runs on the application processor of the cc3200 setup this project is built with a vendor supplied toolchain that s derived from gcc to run the debugger requires windows 7 or greater install code composer studio v6 create a workspace and add all the projects in kitsune go to debug configurations and add the cc3200 config located at kitsune tools ccs patch cc3200 ccxml build all the projects except kitsune build kitsune debugger debug is via swd jtag on a ft2232 we simply pull the signals off the launchpad board four wire jtag in a vm isn t stable probably due to timing requirements for use as a debugger remove all the jumpers from the launchpad and connect the tck tmx rx tx 5v and gnd for best results power the ftdi chip off the 1 8v supply on the bottom board jumper j13 pin is labelled brd pwr after loading code and connecting to the uart the boot command is required to start the background tasks and led stop is needed to dismiss the debug cable animation note in the above configuration 3 3v is supplied on the opposite pin of j13 creating a convenient supply for a pill board architecture basic building blocks are freertos fatfs nanopb and ti s network stack kitsune does not exist in a vacuum the system is composed internally of two mcu s the cc3200 on the middle board and the nrf51422 on the top board which is the focus of kodobannin beyond this the pill is running another nrf which communicates via ant to the top board the mobile apps on ios or android and the backend server this complexity is managed at an interface level using protobuf the periodic data protobuf contains enviromnental data for each minute and the log protobuf contains chunks of debug text the communication between the phone and sense is via the monolithic protobuf morpheus ble which internally is composed of many optional fields and a command field which is used to determine the intent and what content to expect this message is also used in account pairing during which the phone will initiate the process by sending it to the sense which will annotate it and forward it to the server all data between the server and kitsune is transferred using serialized signed protobuf over http the signing process is done by computing the sha of the message and encrypting that sha with a per device aes key which is generated on the device during manufacturing in a process referred to as provisioning internally a few important threads exist networktask exists to serialze the accesses to our single secure socket fast and slow sampling threads record the sensor data a logging thread captures debug output a spi thread listens for data coming via the spi connection to the top board a command task handles initialization of many of the other tasks and brings up a uart debug terminal if on a debug cable an audio task handles recording and playback there is also a cross connect uart ccu which runs between the nrf on the top board and the cc3200 on the middle board this exists to enable remote updates of the nrf but also allows for the debug stream to be captured and merged with the debug stream uploaded to the server files to read for rapid familiarity commands c has the debug command line new commands should be added to the table near the end it also contains the fast slow sampling tasks and alarm logic wifi cmd c handles the nuts and bolts of signing and sending messages to the server fatfs cmd c handles the ota process audio task c handles the playback and record buffering ble proto c handles the phone interface logic sys time c handles managing the system time fetching from ntp and interfacing with the rtc further reading datasheets technical manuals and current schematics are included in the reference folder | os |
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cyberian_blog | header author notes course header include a 1280 640 image course title in sentence case and a concise description in emphasis in your repository settings enable template repository add your 1280 640 social image auto delete head branches add your open source license github uses mit license github pages create a site or blog from your github repositories with github pages header author notes finish review what we learned ask for feedback provide next steps finish congratulations friend you ve completed this course img src https octodex github com images constructocat2 jpg alt celebrate width 300 align right your blog is now live and has been deployed here s a recap of all the tasks you ve accomplished in your repository you enabled github pages you selected a theme using the config file you learned about proper directory format and file naming conventions in jekyll you created your first blog post with jekyll what s next keep working on your github pages site we love seeing what you come up with we d love to hear what you thought of this course in our discussion board https github com skills github discussions take another github skills course https github com skills read the github getting started docs https docs github com en get started to find projects to contribute to check out github explore https github com explore footer author notes footer add a link to get support github status page code of conduct license link get help post in our discussion board https github com skills github discussions bull review the github status page https www githubstatus com copy 2023 github bull code of conduct https www contributor covenant org version 2 1 code of conduct code of conduct md bull mit license https gh io mit footer | cloud |
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machine-learning-coursera | machine learning https www coursera org course ml machine learning is a free ten week online course taught by andrew ng https www coursera org instructor 35 of stanford university this course along with many others was provided through coursera https coursera org this repository contains notes and solutions or attempts at the assignments for the spring of 2014 session course summary machine learning is the science of getting computers to act without being explicitly programmed in the past decade machine learning has given us self driving cars practical speech recognition effective web search and a vastly improved understanding of the human genome machine learning is so pervasive today that you probably use it dozens of times a day without knowing it many researchers also think it is the best way to make progress towards human level ai in this class you will learn about the most effective machine learning techniques and gain practice implementing them and getting them to work for yourself more importantly you ll learn about not only the theoretical underpinnings of learning but also gain the practical know how needed to quickly and powerfully apply these techniques to new problems finally you ll learn about some of silicon valley s best practices in innovation as it pertains to machine learning and ai | ai |
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ibm_reporting | ibm cloud resource tracking report resource utilization for redhat migration engineering team on ibm cloud to setup dependencies setup python virtual environment https docs python org 3 library venv html 1 clone this repository git clone https github com amundra02 ibm reporting git 2 activate the virtual environment and install the required packages pip install target package r requirements txt obtain credentials these scripts use google service account credentials to allow bots to run the scripts 1 create a new google service account https support google com a answer 7378726 hl en 2 create a new key in json format 3 download the key to credentials json file in the current directory 4 share the google sheet with the google service account email abc do not delete xyz iam gserviceaccount com create aws lambda function create a lambda function with the console https docs aws amazon com lambda latest dg getting started html getting started create function lambda creates a function and an execution role that grants the function permission to upload logs the lambda function assumes the execution role when you invoke your function and uses the execution role to create credentials for the aws sdk and to read data from event sources create secret with aws secret manager open the secrets manager console at https console aws amazon com secretsmanager choose store a new secret for secret type choose other type of secret in key value pairs enter your secret ibm secrets name br a store ibm iam apikey and ibm account id create another secret for aws ses credentials aws ses secret name br a store aws access key id and aws access secret key on the review page review your secret details and then choose store add permission to read secrets from secretmanager to lambda function open the functions page https console aws amazon com lambda home functions of the lambda console choose your function choose configuration and then choose permissions and you will see the execution role go to execution role and add an inline policy version 2012 10 17 statement sid visualeditor0 effect allow action secretsmanager getsecretvalue resource arn of ibm secrets created above in secret manager arn of aws ses secrets created above in secret manager set aws lambda function environment variables change the value of each key as per your needs aws ses secret name aws stored secret name google sheet id sheet id ibm secrets name aws stored secret name secrets region region where secrets are stored aws ibm vpc service url https us east iaas cloud ibm com v1 sheet all clusters all clusters sheet all cluster instances all cluster instances sheet all gateways all gateways sheet all subnets all subnets sheet all instances all instances sheet all instances cost all instances cost sheet all vpcs all vpcs sheet cost summary cost summary sheet old clusters old clusters sheet link google sheet link smtp recievers receivers comma seperated smtp sender sender schedule execution of lambda function schedule https docs aws amazon com amazoncloudwatch latest events runlambdaschedule html schedule create rule aws lambda functions using eventbridge events upload code to lambda function a deployment package is required to create or update a lambda function the deployment package acts as the source bundle to run your function s code and dependencies on lambda to create the deployment package open a command prompt and navigate to the ibm reporting project directory install the required libraries to a new package directory pip install target package r requirements txt create a deployment package with the installed library at the root cd package zip r deployment package zip add all the files to the root of the zip file cd src zip g deployment package zip py credentials json note credential file downloaded while obtaining credetials above deploy your zip file to the function to deploy the new code to your function you upload the new zip file deployment package use the lambda console https docs aws amazon com lambda latest dg configuration function zip html configuration function update to upload a zip file to the function | cloud |
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Embedded_Systems_Design_Mean_Filter | embedded systems design mean filter a repository containing the project done during the 9th semester university subject embedded systems design the project reports are in greek team members stelios mouslech https github com steliosmouslech nina lazaridou https github com nlazaridou about project in this project we implement a mean filter for image processing using a given pseudocode on the arm7tdmi processor using the armulator environment afterwards we implement different code transformations and test different memory hierarchies for these implementations we measure important execution time and memory size metrics the project is split into 3 parts part 1 algorithm implementation optimizations and loop transformations in the first part of the project we assume ideal memory and we implement the mean filter given a pseudocode test different logical algorithmic optimizations e g integral image test different loop transformations e g loop unroll loop inversion loop tiling get execution time metrics for all the above implementations using axd debugger img src https github com steliosmouslech embedded systems design median filter blob main part1 readme png width 700 height 400 part 2 memory hierarchy in this part using the best version from the previous part we implement different memory hierarchies and measure the trade off between memory size speed and execution time img src https github com steliosmouslech embedded systems design median filter blob main part2 readme png width 650 height 400 part 3 data reusability in the final part of the project we implement a very fast cache like memory on the previous version and try to reduce the total execution cycles of our program using data reusability techniques we also try to reduce the overhead of additional instructions which correspond to our data reusability technique img src https github com steliosmouslech embedded systems design median filter blob main part3 readme png width 900 height 400 | os |
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miner | build status https badge buildkite com a2ced4f1160fa02aa8b735e7edb80f8ef787a299963ff88942 svg branch master https buildkite com helium miner miner quickstart miner for helium blockchain build make typecheck make typecheck test make test docker build a miner test image locally bash docker build t helium miner test f buildkite scripts dockerfile xxxnn note that miner for amd64 requires avx support on the processor due to the erasure library it is possible to build arm64 images from an amd64 machine install the following bash sudo apt get install qemu binfmt support qemu user static install the qemu packages installing miner from source amd64 amd intel you need a processor with avx extensions you can check this exists by running the following if it is empty your processor doesn t support avx bash grep avx proc cpuinfo next install git https git scm com and build dependencies bash apt install y git erlang libdbus 1 dev autoconf automake libtool flex libgmp dev cmake libsodium dev libssl dev bison libsnappy dev libclang dev doxygen vim build essential cargo parallel clone the git repository bash git clone https github com helium miner git and build bash cd miner make release arm raspbian install miner has been tested against erlang otp 22 and 23 follow this pr https github com helium miner pull 655 for progress on erlang otp 24 support to install otp 21 1 6 in raspian we ll first acquire the erlang package from erlang solutions https www erlang solutions com resources download html bash wget https packages erlang solutions com erlang debian pool esl erlang 22 1 6 1 raspbian buster armhf deb now we ll install various other dependencies and then install erlang itself you ll see some errors after running dpkg you can ignore them bash sudo apt get install libdbus 1 dev autoconf automake libtool flex libgmp dev cmake libsodium dev libssl dev bison libsnappy dev libclang dev doxygen make sudo dpkg i esl erlang 22 1 6 1 raspbian buster armhf deb sudo apt get install f clone the git repository bash git clone https github com helium miner git now it s time to build the miner this will take a while bash cd miner make release | blockchain |
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cs733 | engineering a cloud course assignments for cs 733 advanced distributed systems engineering a cloud | cloud |
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CE-App2 | ce app app for cloud engineering anaylsis | cloud |
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sport | football players tracking with yolov5 bytetrack youtube https badges aleen42 com src youtube svg https www youtube com watch v qcg8qmhga9k roboflow https raw githubusercontent com roboflow ai notebooks main assets badges roboflow blogpost svg https blog roboflow com track football players github https badges aleen42 com src github svg https github com roboflow ai notebooks blob main notebooks how to track football players ipynb i have long been fascinated by the use of computer vision in sports after all it is a combination of two things i love almost three years ago i wrote a post on my personal blog in which i tried at that time still using yolov3 to detect and classify basketball players on the court https towardsdatascience com chess rolls or basketball lets create a custom object detection model ef53028eac7d fifa world cup 2022 has motivated me to revisit this idea this time i used a combination of yolov5 https github com ultralytics yolov5 and bytetrack https github com ifzhang bytetrack to track football players on the field this blog post accompanies the roboflow video where i talk through how to track players on a football field https user images githubusercontent com 26109316 207858600 ee862b22 0353 440b ad85 caa0c4777904 mp4 3d football players pose estimation with yolov7 youtube https badges aleen42 com src youtube svg https www youtube com watch v awjkfjdgiye github https badges aleen42 com src github svg https github com skalskip sport tree master football players pose estimation i was watching a fifa 2022 world cup match the other day and one of the things that caught my eye was var video assistant referee or to be more precise the part of it responsible for analyzing whether a player was on the offside i did a little research https www youtube com watch v wycjdx6give and found that the system performs pose estimation on multiple cameras at once i decided to check how difficult it would be to reproduce it at home using two cameras and yolov7 https github com wongkinyiu yolov7 https user images githubusercontent com 26109316 207677038 20f951a6 e469 4b3f a934 66e036fcff69 mp4 | computer-vision deep-learning deep-neural-networks object-detection pytorch sports-analytics tutorial yolov5 yolov7 | ai |
braid-design-system | div align center img src logo png gh light mode only alt braid title braid width 186px img src logo inverted png gh dark mode only alt braid title braid width 186px br br themeable design system for the seek group br br npm https img shields io npm v braid design system svg style for the badge https www npmjs com package braid design system hr div setup this guide is currently optimised for usage with sku https github com seek oss sku since it s configured to support braid out of the box if your project has a custom build setup you ll need some extra guidance from project contributors to configure your bundler in your sku project first install this library bash npm install save braid design system at the very top of your application import the reset required theme and the braidprovider component warning the reset styles must be imported first to avoid css ordering issues for example js import braid design system reset must be first import apactheme from braid design system themes apac import braidprovider text from braid design system etc finally render the braidprovider component providing the imported theme via the theme prop js import braid design system reset import apactheme from braid design system themes apac import braidprovider text from braid design system export default braidprovider theme apactheme text hello world text braidprovider if you re rendering within the context of another application you may want to opt out of the provided body styles which set the background color and reset margin and padding js braidprovider theme apactheme stylebody false text hello world text braidprovider if you d like to customise the technical implementation of all link and textlink components from braid you can pass a custom component to the linkcomponent prop on braidprovider for example if you wanted to ensure that all relative links are react router https reacttraining com react router links tsx import react from react import link as reactrouterlink from react router dom import braidprovider makelinkcomponent from braid design system import wireframe from braid design system themes wireframe first create the custom link implementation const customlink makelinkcomponent href restprops ref href 0 reactrouterlink ref ref to href restprops a ref ref href href restprops then pass it to braidprovider export const app braidprovider theme wireframe linkcomponent customlink braidprovider style guide migration if you re migrating from an existing style guide please refer to the style guide migration docs style 20guide 20migration md guide local development this project uses pnpm https pnpm io for development dependencies installing with pnpm is required to ensure dependencies match the current pnpm lock yaml pnpm lock yaml bash pnpm install pnpm start start a local storybook server bash pnpm storybook contributing refer to contributing md contributing md thanks chromatic https www chromaticqa com for providing component screenshot testing powered by storybook https storybook js org license mit | design-system style-guide react babel webpack css-modules front-end | os |
Foundations-of-Blockchain | foundations of blockchain a href https packtpub com big data and business intelligence foundations blockchain img src https www packtpub com media catalog product cache e4d64343b1bc593f1c5348fe05efa4a6 9 7 9781789139396 cover png alt foundations of blockchain height 256px align right a this is the code repository for foundations of blockchain https packtpub com big data and business intelligence foundations blockchain published by packt https www packtpub com it contains all the supporting project files necessary to work through the book from start to finish you can purchase the book from amazon com or packt com buy from amazon com https www amazon com dp 1789139392 buy from packt com https www packtpub com big data and business intelligence foundations blockchain about the book foundations of blockchain helps you to understand the concepts of blockchain technology this book introduces you to cryptocurrency and several blockchain platforms it also gives an in depth analysis of the potential and concerns of the technology so that blockchain can be adopted where its implementation actually adds value contents chapters and their respective code directories of the book 1 chapter 1 introduction 2 chapter 2 a bit of cryptography chapter02 3 chapter 3 cryptography in blockchain chapter03 4 chapter 4 networking in blockchain chapter04 5 chapter 5 cryptocurrency chapter05 6 chapter 6 diving into blockchain proof of existence chapter06 7 chapter 7 diving into blockchain proof of ownership chapter07 8 chapter 8 blockchain projects 9 chapter 9 blockchain optimizations and enhancements 10 chapter 10 blockchain security chapter10 11 chapter 11 when shouldn t we use blockchain 12 chapter 12 blockchain use cases chapter12 instructions and navigation all the scripts and applications in this repo are organized chapter wise following the same order as that of the chapters in the book software and hardware requirements software required os required hardware required docker community edition git node js 8 python 3 6 computer with windows linux or macos 20 gb of disk space 2 gb of ram note 1 the mentioned hardware requirement is a general requirement some applications may need a different specification you can find the detailed requirements here prerequisites software hardware req md 2 all the above mentioned software requirements are general requirements for most of the chapters and the installation guide can be found here prerequisites chapter specific requirements can be found in the respective chapter directories contributing you are welcome to submit issues and enhancement requests and work on any of the existing issues follow this simple guide to contribute to the repository 1 create or pick an existing issue to work on 2 fork the repo on github 3 clone the forked project to your own machine 4 commit changes to your own branch 5 push your work back up to your forked repo 6 submit a pull request from the forked repo to our repo so that we can review your changes note be sure to merge the latest from upstream before making a pull request credits 1 chapter 3 hash nonce example py chapter03 hash nonce example py and proof of work example py chapter03 proof of work example py is inspired from mastering bitcoin https github com bitcoinbook bitcoinbook first edition by andreas m antonopoulos llc http antonopoulos com which is licensed under creative commons attribution sharealike 4 0 international license http creativecommons org licenses by sa 4 0 2 chapter 4 the application is inspired from the work naivechain https github com lhartikk naivechain by lauri hartikka https github com lhartikk which is licensed under apache license 2 0 https www apache org licenses license 2 0 3 chapter 5 the application chapter05 cryptocurrency application is inspired from the work naivecoin https github com lhartikk naivecoin by lauri hartikka https github com lhartikk which is licensed under apache license 2 0 https www apache org licenses license 2 0 4 chapter 6 the application is inspired from the work proof of existence on blockchain https github com recordskeeper proof of existence on blockchain by recordskeeper https github com recordskeeper which is licensed under apache license 2 0 https www apache org licenses license 2 0 5 chapter 10 the application is inspired from the work replace by fee tools https github com petertodd replace by fee tools blob master doublespend py by peter todd https github com petertodd which is licensed under gpl 3 0 https www gnu org licenses gpl 3 0 en html 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 9781789139396 https packt link free ebook 9781789139396 a p | blockchain bitcoin ethereum neo hyperledger cryptocurrency packt | blockchain |
js-ts-developer-challenges | dynamox developer challenges about dynamox dynamox https dynamox net is a high tech firm specializing in vibration analysis and industrial asset condition monitoring our expert team develops comprehensive hardware and software solutions encompassing firmware mobile applications android and ios and full stack cloud native applications with our proficiency in signal processing for vibration and acoustics we deliver advanced and precise monitoring systems we are committed to optimizing operational efficiency and facilitating proactive maintenance through our innovative technology and integrated solutions positions we are actively seeking a versatile full stack developer with a strong emphasis on front end development to join our team your primary responsibility will be enhancing our corporate channels our website blog support portal salesforce integration and our asset condition monitoring platform dynapredict https dynamox net en dynapredict you will become an essential part of one of our dedicated development teams where your front end expertise will drive our projects to new heights however while your main focus will be on front end tasks we also expect you to navigate backend development as and when necessary you won t be alone in this you will have the full support of our team to guide you through this opportunity to learn and grow across different aspects of development will foster a dynamic and engaging work environment we value flexibility and collaboration hence we provide opportunities for you to lend your skills to other teams when required join us on this exciting journey as we revolutionize our digital platforms currently we are particularly interested in individuals who can identify with one of the following role descriptions junior software developer with limited experience assists in coding testing and stabilizing systems under supervision communicates with immediate team members and solves straightforward problems with guidance should display a willingness to learn and grow professionally this is an individual contributor role mid level software developer with a certain level of proven experience contributes to software development solves moderate problems and starts handling ambiguous situations with minimal guidance communicates with the broader team and engages in code reviews and documentation this role also includes mentorship of junior engineers and a commitment to continuous learning this is an individual contributor role challenges 01 dynamox full stack developer challenge full stack challenge md 02 dynamox front end developer challenge front end challenge md ready to begin the challenges 1 fork this repository to your own github account 1 create a new branch using your first name and last name for example caroline oliveira 1 after completing the challenge create a pull request to this repository https github com dynamox s a js ts full stack test aimed at the main branch 1 we will receive a notification about your pull request review your solution and get in touch with you frequently asked questions 1 is it necessary to fork the project yes this allows us to see how much time you spent on the challenge br good luck we look forward to reviewing your submission | front_end |
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DevOps-Cloud-projects | devops cloud projects this is where abdul quadri bello stores all his devops and cloud engineering projects | cloud |
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AMLC19-GluonNLP | amlc 2019 dive into deep learning for natural language processing h3 time friday july 12 2019 br location meeting center 02 100 2031 7th ave seattle wa 98121 https goo gl maps lhweyrdmypvnkw3n6 h3 span style color grey presenter haibin lin leonard lausen xingjian shi haichen shen he he sheng zha span br a href https aws amazon com img src static aws logo png alt aws icon height 45 a nbsp a href https aws amazon com img src static amazon ai png alt amazonai icon height 58 a nbsp a href https aws amazon com sagemaker neo img src static neo png alt neo icon height 58 a nbsp a href https beta mxnet io img src static apache incubator logo png alt apache incubator icon height 39 a nbsp a href https beta mxnet io img src static mxnet logo 2 png alt mxnet icon height 39 a nbsp a href https gluon nlp mxnet io img src static gluon logo horizontal small png alt gluon icon height 42 a nbsp a href http tvm ai img src static tvm png alt tvm icon height 32 a abstract deep learning has rapidly emerged as the most prevalent approach for training predictive models for large scale machine learning problems advances in the neural networks also push the limits of available hardware requiring specialized frameworks optimized for gpus and distributed cloud based training moreover especially in natural language processing nlp models contain a variety of moving parts character based encoders pre trained word embeddings long short term memory lstm cells transformer layers and beam search for decoding sequential outputs among others this introductory and hands on tutorial walks you through the fundamentals of machine learning and deep learning with a focus on nlp we start off with a crash course on deep learning for nlp with gluonnlp https gluon nlp mxnet io covering data automatic differentiation and various model architectures such as convolutional recurrent and attentional neural networks then we dive into how context free and contextual representations help various nlp domains throughout the tutorial we start off from the basic classification problem and progress into how it can be structured to solve various nlp problems such as sentiment analysis question answering machine translation and natural language generation finally we demonstrate how we can deploy a state of the art nlp model such as bert on custom hardware such as ec2 a1 instances https aws amazon com ec2 instance types a1 with the help of tvm https tvm ai agenda time title 13 15 14 15 natural language processing and deep learning basics 14 15 14 25 break 14 25 15 15 word embeddings and applications of basic models 15 15 15 55 machine translation and sequence generation 15 55 16 35 contextual representations with bert 16 35 16 45 break 16 45 17 15 model deployment with tvm faq q how do i get access to the notebooks from the tutorial there are two notebook instances used in this tutorial all sessions except model deployment session use the following setup for setting it up on sagemaker notebook instances you can find the instructions here sagemaker setup md for setting up with conda you can use the following conda environment files cpu env amlc19 cpu yml and gpu env amlc19 gpu yml see this guide https docs conda io projects conda en latest user guide tasks manage environments html creating an environment from an environment yml file on creating conda environment from environment file the notebook instances for model deployment can be set up from the following setting for setting it up on sagemaker notebook instances you can find the instructions here sagemaker setup tvm md | ai |
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C2-Blockchain | c2 blockchain this is a c2 implemented over ethereum smart contracts based on ropsten testnet server br to read more about how we made this poc please refer to the series here https sarthaksaini com 2019 august blockchain parts 1 introduction html we have explained everything from introduction of blockchain to using smart contracts over python installation note this is just a proof of concept so the virus py would require web3 to be installed on target server but this limitation can be rectified if the virus was made in go so that an attacker could link the web3 files with the virus binary itself pip3 install web3 usage python3 handler py note the data which will be transferred over the blockchain can be publically accessed by anyone who has the wallet address of contract so don t try to pass any sensitive data and to rectify that you can implement a encryption before sending data to or from attacker demo demo http img youtube com vi sd03bdxzfvu 0 jpg http www youtube com watch v sd03bdxzfvu blockchain c2 | blockchain |
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QualityMatters | img width 100 height 100 src https github com radityagumay qualitymatters blob master documentation assets kotlin png raw true p align center img width 32 src https github com radityagumay qualitymatters blob master documentation assets android png raw true img width 31 src https github com radityagumay qualitymatters blob master documentation assets ios1 png raw true img width 31 src https github com radityagumay qualitymatters blob master documentation assets ios2 png raw true p qualitymatters is intended to showcases as proof of concept for kotlin multiplatform mobile kmm architecture img width 400 height 400 src https github com radityagumay qualitymatters blob master documentation assets kotlin multiplatform png raw true over the last few years we ve seen that multiplatfrom has emerging started from the hybrid platfrom such as apache cordova phonegap xamarin react native even a native like flutter on kotlin multiplatform there are at least three layers of building block such as 1 enterprise business rules 2 interopable business rules 3 platform specific business rules enterprise business rules common code includes the language core libraries and basic tools code written in common kotlin works everywhere on all platforms with kotlin multiplatform libraries you can reuse the multiplatform logic in common and platform specific code common code can rely on a set of libraries that cover everyday tasks such as http serialization and managing coroutines interopable business rules use platform specific versions of kotlin platform specific versions of kotlin kotlin jvm kotlin js kotlin native include extensions to the kotlin language and platform specific libraries and tools platform specific business rules here s where your native platform code is manifested e g android viewmodel or even jetpack compose swiftui and jetbrains compose android jetpack compose https developer android com jetpack compose a modern ui toolkit for android ios swift ui https developer apple com xcode swiftui a modern ui toolkit for ios foundation sqldelight https github com cashapp sqldelight for multiplatform storage ktor https ktor io for multiplatform network flow https kotlin github io kotlinx coroutines kotlinx coroutines core kotlinx coroutines flow flow for multiplatform reactive patterns kodein di https github com kodein framework kodein di for multiplatform dependency injection license copyright 2019 raditya gumay licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | kmm jetpack-compose swiftui modern-mobile-development kotlin | front_end |
LLM-Planning-Papers | llm planning papers awesome https camo githubusercontent com 64f8905651212a80869afbecbf0a9c52a5d1e70beab750dea40a994fa9a9f3c6 68747470733a2f2f617765736f6d652e72652f62616467652e737667 https github com agi edgerunners llm planning papers license mit https camo githubusercontent com fd551ba4b042d89480347a0e74e31af63b356b2cac1116c7b80038f41b04a581 68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4d49542d677265656e2e737667 https opensource org licenses mit img src https img shields io github last commit tensorflow tensorflow svg img https camo githubusercontent com eafac29b763e18c4d80c680d6a179f348cfa2afbc8d3a45642df19fd580d2404 68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5052732d57656c636f6d652d726564 https camo githubusercontent com eafac29b763e18c4d80c680d6a179f348cfa2afbc8d3a45642df19fd580d2404 68747470733a2f2f696d672e736869656c64732e696f2f62616467652f5052732d57656c636f6d652d726564 must read papers on the planning ability of large language models llms news 2023 08 12 we create this paper list on llm planning papers 1 language models as zero shot planners extracting actionable knowledge for embodied agents wenlong huang pieter abbeel deepak pathak igor mordatch abs https arxiv org abs 2201 07207 code https huangwl18 github io language planner icml 2022 1 least to most prompting enables complex reasoning in large language models denny zhou nathanael sch rli le hou jason wei nathan scales xuezhi wang dale schuurmans claire cui olivier bousquet quoc le ed chi abs https arxiv org abs 2205 10625 iclr 2023 1 large language models still can t plan a benchmark for llms on planning and reasoning about change karthik valmeekam alberto olmo sarath sreedharan subbarao kambhampati abs https arxiv org abs 2206 10498 preprint 2022 6 1 on grounded planning for embodied tasks with language models bill yuchen lin chengsong huang qian liu wenda gu sam sommerer xiang ren abs https arxiv org abs 2209 00465 code https github com ink usc g planet aaai 2023 1 on the planning abilities of large language models a critical investigation with a proposed benchmark karthik valmeekam s sreedharan matthew marquez alberto olmo hernandez subbarao kambhampati abs https arxiv org abs 2302 06706 neurips 2023 1 llm planner few shot grounded planning for embodied agents with large language models chan hee song jiaman wu clayton washington brian m sadler wei lun chao yu su abs https arxiv org abs 2212 04088 code https dki lab github io llm planner iccv 2023 1 describe explain plan and select interactive planning with large language models enables open world multi task agents zihao wang shaofei cai anji liu xiaojian ma yitao liang abs https arxiv org abs 2302 01560 code https github com craftjarvis mc planner neurips 2023 1 plan and solve prompting improving zero shot chain of thought reasoning by large language models lei wang wanyu xu yihuai lan zhiqiang hu yunshi lan roy ka wei lee ee peng lim abs https arxiv org abs 2305 04091 code https github com agi edgerunners plan and solve prompting acl 2023 5 1 large language models as commonsense knowledge for large scale task planning zirui zhao wee sun lee david hsu abs https arxiv org abs 2305 14078 neurips 2023 5 1 multimodal procedural planning via dual text image prompting yujie lu pan lu zhiyu chen wanrong zhu xin eric wang william yang wang abs https arxiv org abs 2305 01795 code https github com yujielu10 tip preprint 2023 5 1 llm p empowering large language models with optimal planning proficiency bo liu yuqian jiang xiaohan zhang qiang liu shiqi zhang joydeep biswas peter stone abs https arxiv org abs 2304 11477 code https github com cranial xix llm pddl git preprint 2023 5 1 tree of thoughts deliberate problem solving with large language models shunyu yao dian yu jeffrey zhao izhak shafran thomas l griffiths yuan cao karthik narasimhan abs https arxiv org abs 2305 10601 code https github com ysymyth tree of thought llm preprint 2023 5 1 pearl prompting large language models to plan and execute actions over long documents simeng sun yang liu shuohang wang chenguang zhu mohit iyyer abs https arxiv org abs 2305 14564 code https github com simengsun pearl preprint 2023 5 1 reasoning with language model is planning with world model shibo hao yilan gu haodi ma joshua jiahua hong zhen wang d wang zhiting hu abs https arxiv org abs 2305 14992 code https github com simengsun pearl preprint 2023 5 1 understanding the capabilities of large language models for automated planning vishal pallagani bharath muppasani k murugesan f rossi biplav srivastava l horesh f fabiano andrea loreggia abs https arxiv org abs 2305 16151 preprint 2023 5 1 leveraging pre trained large language models to construct and utilize world models for model based task planning vishal pallagani bharath muppasani k murugesan f rossi biplav srivastava l horesh f fabiano andrea loreggia abs https arxiv org abs 2305 14909 code https github com guansuns llms world models for planning preprint 2023 5 1 adaplanner adaptive planning from feedback with language models haotian sun yuchen zhuang lingkai kong bo dai chao zhang abs https arxiv org abs 2305 16653 code https github com haotiansun14 adaplanner preprint 2023 5 1 a real world webagent with planning long context understanding and program synthesis izzeddin gur hiroki furuta austin huang mustafa safdari yutaka matsuo douglas eck aleksandra faust abs https arxiv org abs 2307 12856 preprint 2023 7 1 metagpt meta programming for multi agent collaborative framework sirui hong xiawu zheng jonathan chen yuheng cheng jinlin wang ceyao zhang zili wang steven ka shing yau zijuan lin liyang zhou chenyu ran lingfeng xiao chenglin wu abs https arxiv org abs 2308 00352 code https github com geekan metagpt preprint 2023 8 1 dynamic planning with a llm gautier dagan frank keller alex lascarides abs https arxiv org abs 2308 06391 preprint 2023 8 1 tptu task planning and tool usage of large language model based ai agents jingqing ruan yihong chen bin zhang zhiwei xu tianpeng bao guoqing du shiwei shi hangyu mao xingyu zeng rui zhao abs https arxiv org abs 2308 03427 preprint 2023 8 1 codeplan repository level coding using llms and planning ramakrishna bairi atharv sonwane aditya kanade vageesh d c arun iyer suresh parthasarathy sriram rajamani b ashok shashank shet abs https arxiv org abs 2309 12499 preprint 2023 9 1 compositional foundation models for hierarchical planning anurag ajay seungwook han yilun du shaung li abhi gupta tommi jaakkola josh tenenbaum leslie kaelbling akash srivastava pulkit agrawal abs https arxiv org abs 2309 08587 neurips 2023 1 videodirectorgpt consistent multi scene video generation via llm guided planning han lin abhay zala jaemin cho mohit bansal abs https arxiv org abs 2309 15091 preprint 2023 9 1 evaluating cognitive maps and planning in large language models with cogeval ida momennejad hosein hasanbeig felipe vieira hiteshi sharma robert osazuwa ness nebojsa jojic hamid palangi jonathan larson abs https arxiv org abs 2309 15129 preprint 2023 9 1 can large language models be good path planners a benchmark and investigation on spatial temporal reasoning mohamed aghzal erion plaku ziyu yao abs https arxiv org abs 2310 03249 preprint 2023 10 1 tree planner efficient close loop task planning with large language models mengkang hu yao mu xinmiao yu mingyu ding shiguang wu wenqi shao qiguang chen bin wang yu qiao ping luo abs https arxiv org abs 2310 08582 neurips 2023 1 put your money where your mouth is evaluating strategic planning and execution of llm agents in an auction arena jiangjie chen siyu yuan rong ye bodhisattwa prasad majumder kyle richardson abs https arxiv org abs 2310 05746 preprint 2023 9 1 guiding language model reasoning with planning tokens xinyi wang lucas caccia oleksiy ostapenko xingdi yuan alessandro sordoni abs https arxiv org abs 2310 05707 preprint 2023 9 1 language agent tree search unifies reasoning acting and planning in language models andy zhou kai yan michal shlapentokh rothman haohan wang yu xiong wang abs https arxiv org abs 2310 04406 preprint 2023 10 1 navigation with large language models semantic guesswork as a heuristic for planning dhruv shah michael equi blazej osinski fei xia brian ichter sergey levine abs https arxiv org abs 2310 10103 preprint 2023 10 1 interactive task planning with language models boyi li philipp wu pieter abbeel jitendra malik abs https arxiv org abs 2310 10645 preprint 2023 10 1 bioplanner automatic evaluation of llms on protocol planning in biology odhran o donoghue aleksandar shtedritski john ginger ralph abboud ali essa ghareeb justin booth samuel g rodriques abs https arxiv org abs 2310 10632 emnlp 2023 1 diagrammergpt generating open domain open platform diagrams via llm planning abhay zala han lin jaemin cho mohit bansal abs https arxiv org abs 2310 12128 preprint 2023 10 star star history star history chart https api star history com svg repos agi edgerunners llm planning papers type date https star history com agi edgerunners llm planning papers date | awesome-list large-language-models llm paper-list papers planning review survey | ai |
LLMScore | llmscore p align center a href https yujielu10 github io target blank demo coming soon a a href https github com yujielu10 llmscore tree master assets t2i llmscore preprint pdf target blank paper a a href https twitter com yujielu 10 target blank twitter a br p in this work we propose llmscore a new framework that offers evaluation scores with multi granularity compositionality llmscore leverages the large language models llms to evaluate text to image models please check out our paper a href https github com yujielu10 llmscore tree master assets t2i llmscore preprint pdf target blank llmscore unveiling the power of large language models in text to image synthesis evaluation a overview the two images are generated using stable diffusion 2 based on the text prompt sampled from the concept conjunction dataset baseline section shows the scores from the existing model based evaluation metrics human section is the rating score from the human evaluation llmscore section is our proposed metric the right column also shows the rationale generated by llmscore p align center img src assets teaser png width 1024px img p comparison of text image matching sentence matching and our llm based instruction following matching pipeline for text to image synthesis evaluation our llmscore automatically provides accurate scores and reasonable rationales for text to image synthesis based on text prompts and visual descriptions following various evaluation instructions p align center img src assets matching pipeline png width 1024px img p installation please follow install install md page to set up the environments and models text to image synthesis evaluation get score with rationale for evaluating the alignment between image and text prompt python llm score py image sample sample png text prompt a red car and a white sheep try different llms by setting llm id as one of gpt 4 gpt 3 5 turbo vicuna python llm score py image sample sample png text prompt a red car and a white sheep llm id llm id notice that to use vicuna follow part install and part model weights in fastchat readme https github com lm sys fastchat to install fastchat and to obtain the vicuna weights to enable openai compastible apis used in our repo follow commands from guideline https github com lm sys fastchat blob main docs openai api md to launch the controller model worker and restful api server as below python3 m fastchat serve controller python3 m fastchat serve model worker model name vicuna 7b v1 1 model path path to vicuna weights python3 m fastchat serve openai api server host localhost port 8000 llmscore with rationale p align center img src assets showcase png width 1024px img p human correlation the rank correlation kendall s tau is aggregated across the compositional prompt dataset concept conjunction attribute binding contrast on the left two columns compbench and the general prompt dataset mscoco drawbench paintskills on the right two columns generalbench p align center img src assets vis kendall png width 1024px img p citation if you found this repository useful please consider cite our paper bibtex article xiao2023fastcomposer title llmscore unveiling the power of large language models in text to image synthesis evaluation author yujie lu xianjun yang xiujun li xin eric wang william yang wang journal arxiv year 2023 acknowledgement this repo benefits from blip 2 https github com salesforce lavis tree main projects blip2 grit https github com jialianw grit gpt 4 https openai com research gpt 4 thank for their awesome works | ai |
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System-Designs | system design preparation how to prepare for and answer system design questions objective learning about and implementing large scale distributed system is not easy i do not want to give the impression that it s something that can be learnt in a month what this repository aims to achieve is for software engineers and students to get a rough idea of how the thought process of designing a large scale works and how big companies have managed to solve really hard problems along with that there is a recent trend for companies to have an open ended interview with system design questions which is at times hard for engineers of all levels if they haven t gotten the opportunity to work on such systems themselves this is a collection of links documents for the following use cases a prepare for a system design or open ended rounds b learn more about how large scale systems work and thought process of designing a new system index starting point start basics basics how to answer in interviews howtoans steps how i approach the system design questions in interviews myapproach common design questions designques architecture architecture company engineering blog links blog low on time tldr a name start starting point a for a very broad overview please go through these lectures really useful gaurav sen s system design series https www youtube com playlist list plmcxhnjxntnvo6alsjvkgxv vh6epyvox starts from simple stuff like load balancing and message queues then moves to building full systems like whatsapp and tinder david malans cs75 scalability talk https www youtube com watch v w9f d3oy4 list plmhrnzyyvpdmlpavqm3mk5py5kb 4hlje index 10 feel free to go through other lectures if needed david huffman s talk scaling up talk https www udacity com course web development cs253 youtube link https www youtube com watch v pjntgulvvf4 list plvi1lmrukq0ninqfjklven7j2lzfl35wp index 1 scalability for dummies http www lecloud net tagged scalability designing data intensive appliations this is by far one of the best books about large scale systems and the practical challenges encountered during building them it s focussed more on data oriented applications though these talks should give you a starting point on how to think about such problems a name basics basics a but before you begin here are some topics in no particular order which in my opinion you should have a decent idea of before proceeding 1 operating system basics how a file system virtual memory paging instruction execution cycle etc work for starters silbershatz should be enough if you already have decent knowledge try stallings book on os 2 networking basics should know the tcp ip stack basics of how internet http tcp ip work at the minimum cs75 on youtube 1st lecture should give a broad overview i personally love networking a top down approach http www amazon com computer networking top down approach edition dp 0132856204 3 concurrency basics threads processes threading in the language you know locks mutex etc 4 db basics types of db s sql vs nosql etc hashing and indexing eav based databases sharding caching for databases master slave etc 5 a basic idea of how a basic web architecture is say load balancers proxy servers database servers caching servers precompute logging big data etc just know broadly what is each layer for 6 very basic summary of what the cap theorem http robertgreiner com 2014 08 cap theorem revisited is have never been asked about the theorem itself but knowing it will help you in designing large scale systems a name howtoans how to answer in interviews a i found hiredintech http www hiredintech com system design videos an excellent place to start with the way how to approach a design question as given in the link is really useful it goes into how we start with clearing the use cases of the system then thinking in the abstract manner of the various component and the interactions think about the bottlenecks of the system and what is more critical for your system eg latency vs reliability vs uptime etc address those giving the tradeoff of your approach system design in crack the coding interview http www flipkart com cracking coding interview 150 programming questions solutions english 5th p itmdz4pvzbhcv6uv good approach on how to begin attacking a problem by first solving for a small usecase then expanding the system the best way to prepare for such questions is do mock interviews pick any topic given below try to come up with a design and then go and see how and why it is designed in that manner there is absolutely no alternative to practice whiteboarding a system design question is similar to actually writing code and testing it just reading will only take you so far a name myapproach steps how i approach the system design questions in interviews a these are the steps i go through mentally in the interviews followed by actual interview experiences a be absolutely sure you understand the problem being asked clarify on the onset rather than assuming anything b use cases this is critical you must know what is the system going to be used for what is the scale it is going to be used for also constraints like requests per second requests types data written per second data read per second c solve the problem for a very small set say 100 users this will broadly help you figure out the data structures components abstract design of the overall model d write down the various components figured out so far and how will they interact with each other e as a rule of thumb remember at least these 1 processing and servers 2 storage 3 caching 4 concurrency and communication 5 security 6 load balancing and proxy 7 cdn 8 monetization if relevant how will you monetize eg what kind of db is postgres enough if not why do you need caching and how much is security a prime concern f special cases for the question asked say designing a system for storing thumbnails will a file system be enough what if you have to scale for facebook or google will a nosql based database work g after i have my components in place what i generally try to do is look for minor optimization in various places according to the use cases various tradeoffs that will help in better scaling in 99 cases h scaling out or up http highscalability com blog 2014 5 12 4 architecture issues when scaling web applications bottlene html i check with the interviewer is there any other special case he is looking to solve also it really helps if you know about the company you are interviewing with what its architecture is what will the interviewer have more interest in based on the company and what he works on a name designques common design questions a it generally depends what you are and you will be working on also what your level is but these are some of the more frequent interview questions design amazon s frequently viewed product page eg which shows the last 5 items you saw design an online poker game for multiplayer solve for persistence concurrency scale draw the er diagram for this design a url compression system http www hiredintech com system design the system design process search engine http infolab stanford edu backrub google html generally asked with people who have some domain knowledge basic crawling collection hashing etc depends on your expertise on this topic design dropbox s architecture good talk on this https www youtube com watch v pe4gwstwhmc design a picture sharing website http highscalability com blog 2011 12 6 instagram architecture 14 million users terabytes of photos html how will you store thumbnails photos usage of cdns caching at various layers etc design a news feed eg facebook twitter news feed http www quora com software engineering best practices what are best practices for building something like a news feed design a product based on maps eg hotel atm finder given a location design malloc free and garbage collection system http courses cs washington edu courses csep521 07wi prj rick pdf what data structures to use decorator pattern over malloc etc design a site like junglee com http www junglee com i e price comparision availability on e commerce websites when and will you cache how much to query how to crawl efficiently over e commerce sites sharding of databases basic database design a web application for instant messaging eg whatsapp http highscalability com blog 2014 2 26 the whatsapp architecture facebook bought for 19 billion html facebook chat issues of each scaling problems status and availability notification etc design a system for collaborating over a document simultaneously eg google docs https neil fraser name writing sync very common top n or most frequent items of a running stream of data design election commission architecture let s say we work with the election commission on counting day we want to collate the votes received at the lakhs of voting booths all over the country each booth has a voting machine which when connected to the network returns an array of the form party id num votes party id 2 num votes 2 we want to collect these and get the current scores in real time the report we need continuously is how many seats is each party leading in please design a system for this design a logging system for web applications it is common to have a large number of servers running the same application with a load balancer in front to distribute the incoming requests in this scenario we want to check and alarm in case an exception is thrown in any of the servers we want a system that checks for the appearance of specific words exception disk full etc in the logs of any of the servers how would you design this system a name architecture architectures a personally i looked into the following architectures basics of google search http infolab stanford edu backrub google html basics of messaging frameworks like kafka queuing architectures like rabbitmq broad overview and advantages of redis mongodb cassandra google file system http static googleusercontent com media research google com en archive gfs sosp2003 pdf google architecture http highscalability com google architecture instagram http instagram engineering tumblr com post 13649370142 what powers instagram hundreds of instances and other image based social networks memcache scaling by facebook https cs uwaterloo ca brecht courses 854 emerging 2014 readings key value fb memcached nsdi 2013 pdf twitter scaling https www youtube com watch v z8lu0cj6bou and facebook feeds facebook graph api https cs uwaterloo ca brecht courses 854 emerging 2014 readings data store tao facebook distributed datastore atc 2013 pdf facebook haystack needle architecture https www usenix org legacy event osdi10 tech full papers beaver pdf youtube architecture and optimizations for video https www youtube com watch v zw5 eekec28 a name blog company engineering blog links a courtesy checkcheckzz https github com checkcheckzz system design interview toc depending on where you are interviewing go through the company blog very useful in interviews it really helps if you have an idea of the architecture as the questions asked will generally be of that domain and your prior knowledge will help out here airbnb engineering http nerds airbnb com amazon https developer amazon com blogs amazon aws https aws amazon com blogs bandcamp tech http bandcamptech wordpress com banksimple simple blog https www simple com engineering bitly engineering blog http word bitly com cloudera developer blog http blog cloudera com blog dropbox tech blog https tech dropbox com engineering at quora http engineering quora com etsy code as craft http codeascraft com facebook engineering https www facebook com engineering flickr code http code flickr net foursquare engineering blog http engineering foursquare com google research blog http googleresearch blogspot com groupn engineering blog https engineering groupon com high scalability http highscalability com instagram engineering http instagram engineering tumblr com linkedin engineering http engineering linkedin com blog oyster tech blog http tech oyster com pinterest engineering blog http engineering pinterest com songkick technology blog http devblog songkick com soundcloud backstage blog https developers soundcloud com blog square the corner http corner squareup com the reddit blog http www redditblog com the github blog https github com blog category engineering the netflix tech blog http techblog netflix com twilio engineering blog http www twilio com engineering twitter engineering https engineering twitter com uber engineering https eng uber com walmart labs tech blog https medium com walmartlabs webengage engineering blog http engineering webengage com yammer engineering http eng yammer com blog yelp engineering blog http engineeringblog yelp com smarkets blog https smarketshq com a name tldr low on time a i would highly recommend you do not take a shortcut unless you have a week or so for an interview system design is best learnt by practising shortcuts might help you in the short term but would recommend coming back to this link for an in depth understanding after the interview a go through cs76 and udacity s links given above for scaling systems b go through the engineering blog of the company you are interviewing in or if its a startup go through the link of the company closest to yours c see this talk http www hiredintech com system design the system design process and develop a process for how to answer such questions d remember these terms just roll over them in your interview in your mind and if relevant mention it in the interview 1 processing and servers 2 storage 3 caching 4 concurrency and communication 5 security 6 load balancing and proxy 7 cdn 8 monetization best of luck 1 feel free to send pull requests to add more content to this git | system-design facebook google design-patterns design-system microsoft interview-preparation | os |
Mobile-Application-Development | mobile application development class repo for mobile application development integrated digital media tandon school of engineering nyu live share session https prod liveshare vsengsaas visualstudio com join 11d3da74fd09e82dd6b4c36c49025b1977f3 google meet session https meet google com kto ezgp jmg course details class syllabus https docs google com document d 12bogwh0kslocbe3rc04jbjoz3zotb zvdv5 1la6wl8 edit usp sharing office hours signup https calendar google com calendar selfsched sstoken uuhvyneyyvu0aevvfgrlzmf1bhr8ztfjmtcyowqxnjjjyze0m2vkytc2ztmwmjvim2uxnta class pages class 1 classes class 201 20 20introduction md class 2 classes class 202 20 20understanding 20react md class 3 classes class 203 md class 4 classes class 204 md class 5 classes class 205 md class 6 classes class 206 md relevant resources code javascript javascript data types https developer mozilla org en us docs web javascript data structures primitive types and reference types in javascript https gist github com branneman 7fb06d8a74d7e6d4cbcf75c50fec599c json placeholder testing api https jsonplaceholder typicode com react react component lifecycle diagram http projects wojtekmaj pl react lifecycle methods diagram react native expo io documentation https docs expo io versions v35 0 0 react navigation https reactnavigation org docs en hello react navigation html testing on ios simulator on osx https docs expo io versions v35 0 0 workflow ios simulator and android studio emulator https docs expo io versions latest workflow android studio emulator react native paper ui library https reactnativepaper com design | nyu react react-native mobile javascript | front_end |
keras-nlp | kerasnlp modular nlp workflows for keras https github com keras team keras nlp workflows tests badge svg branch master https github com keras team keras nlp actions query workflow 3atests branch 3amaster python https img shields io badge python v3 9 0 success svg contributions welcome https img shields io badge contributions welcome brightgreen svg style flat https github com keras team keras nlp issues kerasnlp is a natural language processing library that works natively with tensorflow jax or pytorch built on multi backend keras https keras io keras core announcement keras 3 these models layers metrics and tokenizers can be trained and serialized in any framework and re used in another without costly migrations kerasnlp supports users through their entire development cycle our workflows are built from modular components that have state of the art preset weights when used out of the box and are easily customizable when more control is needed this library is an extension of the core keras api all high level modules are layers https keras io api layers or models https keras io api models that receive that same level of polish as core keras if you are familiar with keras congratulations you already understand most of kerasnlp see our getting started guide https keras io guides keras nlp getting started to start learning our api we welcome contributions contributing md quick links for everyone home page https keras io keras nlp developer guides https keras io guides keras nlp api reference https keras io api keras nlp getting started guide https keras io guides keras nlp getting started for contributors contributing guide contributing md roadmap roadmap md style guide style guide md api design guide api design guide md call for contributions https github com keras team keras nlp issues q is 3aissue is 3aopen label 3a 22contributions welcome 22 installation to install the latest official release pip install keras nlp upgrade to install the latest unreleased changes to the library we recommend using pip to install directly from the master branch on github pip install git https github com keras team keras nlp git upgrade quickstart fine tune bert on a small sentiment analysis task using the keras nlp models https keras io api keras nlp models api python import os os environ keras backend jax or tensorflow or torch import keras nlp import tensorflow datasets as tfds imdb train imdb test tfds load imdb reviews split train test as supervised true batch size 16 load a bert model classifier keras nlp models bertclassifier from preset bert base en uncased num classes 2 activation softmax fine tune on imdb movie reviews classifier fit imdb train validation data imdb test predict two new examples classifier predict what an amazing movie a total waste of my time for more in depth guides and examples visit https keras io keras nlp configuring your backend keras 3 is an upcoming release of the keras library which supports tensorflow jax or torch as backends this is supported today in kerasnlp but will not be enabled by default until the official release of keras 3 if you pip install keras nlp and run a script or notebook without changes you will be using tensorflow and keras 2 if you would like to enable a preview of the keras 3 behavior you can do so by setting the keras backend environment variable for example shell export keras backend jax or in colab with python import os os environ keras backend jax import keras nlp important make sure to set the keras backend before import any keras libraries it will be used to set up keras when it is first imported until the keras 3 release kerasnlp will use a preview of keras 3 on pypi named keras core https pypi org project keras core important if you set keras backend variable you should import keras core as keras instead of import keras this is a temporary step until keras 3 is out to restore the default keras 2 behavior unset keras backend before importing keras and kerasnlp compatibility we follow semantic versioning https semver org and plan to provide backwards compatibility guarantees both for code and saved models built with our components while we continue with pre release 0 y z development we may break compatibility at any time and apis should not be consider stable disclaimer kerasnlp provides access to pre trained models via the keras nlp models api these pre trained models are provided on an as is basis without warranties or conditions of any kind the following underlying models are provided by third parties and subject to separate licenses bart deberta distilbert gpt 2 opt roberta whisper and xlm roberta citing kerasnlp if kerasnlp helps your research we appreciate your citations here is the bibtex entry bibtex misc kerasnlp2022 title kerasnlp author watson matthew and qian chen and bischof jonathan and chollet fran c c ois and others year 2022 howpublished url https github com keras team keras nlp acknowledgements thank you to all of our wonderful contributors a href https github com keras team keras nlp graphs contributors img src https contrib rocks image repo keras team keras nlp a | deep-learning keras machine-learning nlp tensorflow | ai |
Stock-Prediction-System-Application | hello welcome to my project this project is already uploaded to my github account where i have deployed this project you can find the project here https github com kumar laxmi stock prediction system application img src app static image banner png alt stock market prediction introduction p predicting stock prices is a cumbersome task as it does not follow any specific pattern changes in the stock prices are purely based on supply and demand during a period of time in order to learn the specific characteristics of a stock price we can use algorithm to identify these patterns through machine learning one of the most well known networks for series forecasting is lstm long short term memory which is a recurrent neural network rnn that is able to remember information over a long period of time thus making them extremely useful for predicting stock prices rnns are well suited to time series data and they are able to process the data step by step maintaining an internal state where they cache the information they have seen so far in a summarised version the successful prediction of a stock s future price could yield a significant profit p aim p to predict stock prices according to real time data values fetched from api p objective p the main objective of this project is to develop a web application that can predict stock price based on real time data p project scope p the project has a wide scope as it is not intended to a particular organization this project is going to develop generic software which can be applied by any businesses organization moreover it provides facility to its users also the software is going to provide a huge amount of summary data p technology used languages html https img shields io badge html5 e34f26 style for the badge logo html5 logocolor white css https img shields io badge css3 1572b6 style for the badge logo css3 logocolor white javascript https img shields io badge javascript 323330 style for the badge logo javascript logocolor f7df1e python https img shields io badge python ffd43b style for the badge logo python logocolor darkgreen framework bootstrap https img shields io badge bootstrap 563d7c style for the badge logo bootstrap logocolor white django https img shields io badge django 092e20 style for the badge logo django logocolor green deployment click to see deployement note deployement not working a href https stock prediction system herokuapp com heroku https img shields io badge heroku 430098 style for the badge logo heroku logocolor white a machine learning algorithms a href https en wikipedia org wiki linear regression multiple linear regression a ml dl numpy https img shields io badge numpy 23013243 svg style for the badge logo numpy logocolor white pandas https img shields io badge pandas 23150458 svg style for the badge logo pandas logocolor white scikit learn https img shields io badge scikit learn 23f7931e svg style for the badge logo scikit learn logocolor white database sqlite https img shields io badge sqlite 07405e style for the badge logo sqlite logocolor white api used for yahoo finance api rest api ide vs code https img shields io badge visual studio code 0078d4 style for the badge logo visual 20studio 20code logocolor white pycharm https img shields io badge pycharm 000000 svg style for the badge logo pycharm logocolor white jupyter notebook https img shields io badge jupyter f37626 svg style for the badge logo jupyter logocolor white os used for testing macos https img shields io badge mac 20os 000000 style for the badge logo apple logocolor white ubuntu https img shields io badge ubuntu e95420 style for the badge logo ubuntu logocolor white windows https img shields io badge windows 0078d6 style for the badge logo windows logocolor white designed using adobexd https img shields io badge adobe 20xd 470137 style for the badge logo adobe 20xd logocolor ff61f6 figma https img shields io badge figma f24e1e style for the badge logo figma logocolor white prerequisites bash django 3 2 6 django heroku 0 3 1 gunicorn 20 1 0 matplotlib 3 5 2 matplotlib inline 0 1 3 numpy 1 23 0 pandas 1 4 1 pipenv 2022 6 7 plotly 5 9 0 requests 2 28 1 scikit learn 1 1 1 scipy 1 8 1 seaborn 0 11 2 sklearn 0 0 virtualenv 20 14 1 virtualenv clone 0 5 7 yfinance 0 1 72 project installation step 1 clone the repository from github bash git clone https github com kumar laxmi stock prediction system application git step 2 change the directory to the repository bash cd stock prediction system application step 3 create a virtual environment for windows bash python m venv virtualenv for macos and linux bash python3 m venv virtualenv step 4 activate the virtual environment for windows bash virtualenv scripts activate for macos and linux bash source virtualenv bin activate step 5 install the dependencies bash pip install r requirements txt step 6 migrate the django project for windows bash python manage py migrate for macos and linux bash python3 manage py migrate step 7 run the application for windows bash python manage py runserver for macos and linux bash python3 manage py runserver walkthrough video https user images githubusercontent com 76027425 179440037 bf73c742 c463 434b a5f9 97b83e4ddb35 mp4 output screen shots the home page of the application that displays real time data of stock prices image https user images githubusercontent com 76027425 179440522 674b6e07 31dc 422f 81e3 0e0c9c74c85a png to predict stock price we move on to predicition page where we need to enter valid ticker value and number of days and click predict button image https user images githubusercontent com 76027425 179440538 a7054ec1 ce3b 44b1 b55e 72bf7e23692c png this page displays the predicted stock price alsong with searched ticker details and also generating unique qr code to view the predicted result image https user images githubusercontent com 76027425 179440583 dcb85f97 d358 42d7 a7b4 661461135efd png the left graph is the real time stock price of the searched ticker for past 1day the right graph is the predicted stock price for the number of days searched image https user images githubusercontent com 76027425 179440591 06b8b095 d2c4 4df8 93d7 fe389b748470 png the ticker info page displays the details of all the valid tickers accepted by the application image https user images githubusercontent com 76027425 179440611 3552e15a a66e 464b a000 cb45b864352c png disclaimer p this software is for educational purposes only use the software at your own risk the authors and all affiliates assume no responsibility for your trading results do not risk money which you are afraid to lose there might be bugs in the code this software does not come with any warranty p | bootstrap django-framework machine-learning python stock-price-prediction stock-market stock-prediction prediction stock-market-prediction stock-price-predictions | os |
Natural-Language-Processing-Specialization-for-Coursera | natural language processing specialization for coursera my class notebooks and assignments for coursera natural language processing specialization including natural language processing with classification and vector spaces natural language processing with probabilistic models natural language processing with sequence models natural language processing with attention models just for reference and i hope you can star me | ai |
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cortex | cortex travisci https travis ci com thinktopic cortex svg token pnfs4ajt3yqgnnwzvg5z branch master neural networks regression and feature learning in clojure cortex has been developed by thinktopic http thinktopic com in collaboration with mike anderson https github com mikera a href https www thinktopic com img src https cloud githubusercontent com assets 17600203 21554632 6257d9b0 cdce 11e6 8fc6 1a04ec8e9664 jpg width 200 a mailing list https groups google com forum forum clojure cortex usage clojars project https clojars org thinktopic cortex latest version svg https clojars org thinktopic cortex all libraries are released on clojars https clojars org thinktopic cortex cortex is not 1 0 yet preliminary and you should expect quite a few things to change over time but it should allow you to train some initial classifiers or regressions note that the save format has not stabilized and although we do just save edn data in nippy format it may require some effort to bring versions of saved forward cortex design design is detailed here cortex design document docs design md please see the various unit tests and examples for training a model specifically see mnist verification src cortex verify nn train clj also for an example of using cortex in a more real world scenario please see mnist example examples mnist classification src mnist classification core clj existing framework comparisons stanford cs 231 lecture 12 http cs231n stanford edu slides 2016 winter1516 lecture12 pdf contains a detailed breakdown of caffe torch theano and tensorflow todo hdf5 import of major keras models vgg net this requires each model along with a single input and per layer outputs for that input please don t ask for anything to be supported unless you can provide the appropriate thorough test recurrence in all forms there is some work towards that direction in the compute branch and it is specifically designed to match the cudnn api for recurrence this is less important at this point than running some of the larger pre trained models speaking of larger nets multiple gpu support and multiple machine support which could be helped by the above graph based description layer profiling gpu system to make sure we are using as much gpu as possible in the single gpu case better data import visualization support we have geom and we have a clear definition of the datasets now we need to put together the pieces and build some great visualizations as examples getting started get the project and run lein test in both cortex and compute the various unit tests train various models gpu compute install instructions ubuntu sudo apt install nvidia cuda toolkit reboot install cudnn https developer nvidia com cudnn and copy the cudnn files to the corresponding folders in the local cuda installation probably at usr local cuda for reference follow the installing cudnn section here http www pyimagesearch com 2016 07 04 how to install cuda toolkit and cudnn for deep learning to check everything is working run nvidia smi you should now have cuda8 0 installed current master is 8 0 so if you re running 7 5 you will need to change the javacpp dependency in your project file of the mnist example https github com thinktopic cortex blob master examples mnist classification project clj mac os these instructions follow the gpu setup from tensor flow https github com tensorflow tensorflow blob master tensorflow g3doc get started os setup md optional setup gpu for mac i e install coreutils and cuda brew install coreutils brew tap caskroom drivers brew cask install nvidia cuda add cuda tool kit to bash profile export cuda home usr local cuda export dyld library path dyld library path cuda home lib export path cuda home bin path download the cuda deep neural network libraries https developer nvidia com cudnn once downloaded and unzipped moving the files sudo mv include cudnn h developer nvidia cuda 8 0 include sudo mv lib libcudnn developer nvidia cuda 8 0 lib sudo ln s developer nvidia cuda 8 0 lib libcudnn usr local cuda lib should you see a jni linking error similar to this retrieving org bytedeco javacpp presets cuda 8 0 1 2 cuda 8 0 1 2 macosx x86 64 jar from central exception in thread main java lang unsatisfiedlinkerror no jnicudnn in java library path compiling think compute nn cuda backend c lj 82 28 at clojure lang compiler analyze compiler java 6688 at clojure lang compiler analyze compiler java 6625 at clojure lang compiler hostexpr parser parse compiler java 1009 make sure you have installed the appropriate cudnn for your version of cuda windows some preliminary information about getting gpu acceleration working on windows is available here https groups google com forum topic clojure cortex hnfw1t 2pzc see also roadmap docs roadmap md | ai |
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stacks.js | stacks js test action badge https github com hirosystems stacks js actions workflows tests yml badge svg https github com hirosystems stacks js actions workflows tests yml monorepo version label https img shields io github lerna json v hirosystems stacks js label monorepo https github com hirosystems stacks js tree main packages this repo is home to most of the stacks js packages which provide the building blocks to work with the stacks blockchain https www stacks co what is stacks from javascript typescript connecting wallets stacks connect https github com hirosystems connect connect web application to stacks wallet browser extensions separate repo stacks primitives stacks transactions https github com hirosystems stacks js tree main packages transactions construct decode transactions and work with clarity smart contracts on the stacks blockchain stacks wallet sdk https github com hirosystems stacks js tree main packages wallet sdk library for building wallets managing accounts and handling keys for the stacks blockchain stacks storage https github com hirosystems stacks js tree main packages storage store and fetch files with gaia the decentralized storage system stacks encryption https github com hirosystems stacks js tree main packages encryption encryption functions used by stacks js packages stacks auth https github com hirosystems stacks js tree main packages auth construct and decode authentication requests for stacks apps stacks profile https github com hirosystems stacks js tree main packages profile functions for manipulating user profiles stacks network https github com hirosystems stacks js tree main packages network network and api library for working with stacks blockchain nodes stacks common https github com hirosystems stacks js tree main packages common common utilities used by stacks js packages native smart contract interaction stacks bns https github com hirosystems stacks js tree main packages bns library for interacting with the bns contract stacks stacking https github com hirosystems stacks js tree main packages stacking library for pox stacking others stacks cli https github com hirosystems stacks js tree main packages cli command line interface to interact with auth storage and stacks transactions stacks keychain deprecated replaced by stacks wallet sdk https github com hirosystems stacks js tree main packages wallet sdk see the respective readme in each package directory for installation instructions and usage documentation documentation and library references for the stacks js packages are located at stacks js org https stacks js org migrating from previous versions to migrate your app from blockstack js to stacks js follow the steps in the respective migration guide github migration md bugs and feature requests if you encounter a bug or have a feature request we encourage you to follow the steps below 1 search for existing issues before submitting a new issue please search existing and closed issues issues to check if a similar problem or feature request has already been reported 1 open a new issue if it hasn t been addressed please open a new issue issues new choose choose the appropriate issue template and provide as much detail as possible including steps to reproduce the bug or a clear description of the requested feature 1 evaluation sla our team reads and evaluates all the issues and pull requests we are avaliable monday to friday and we make a best effort to respond within 7 business days please do not use the issue tracker for personal support requests or to ask for the status of a transaction you ll find help at the support discord channel https discord gg sk3dxdsp contributing development github issues marked help wanted https github com hirosystems stacks js labels help wanted are great places to start please ask in a github issue or discord before embarking on larger issues that aren t labeled as help wanted or adding additional functionality so that we can make sure your contribution can be included environment setup to setup the development environment for this repository follow these steps prerequisites nodejs npm are required v18 x x is currently recommended 1 clone this package 1 run npm install to install dependencies 1 run npm run build to build packages 1 run npm run test to run tests some tests may contain logging of errors and warnings this should not be confused with failing tests make sure the last lines of npm run test show lerna success stacks for every package code of conduct please read our code of conduct github blob main code of conduct md since we expect project participants to adhere to it community join our community and stay connected with the latest updates and discussions join our discord community chat https discord gg zqr6cyzc to engage with other users ask questions and participate in discussions visit hiro so https www hiro so for updates and subcribing to the mailing list follow hiro on twitter https twitter com hirosystems | blockchain |
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remoted | remoted is not live anymore remoted io this is the source code and issue board for https remoted io remoted is an aggregator for remote jobs for it professionals software engineering database cloud security you are more than welcome to participate to help making https remoted io the best remote job board give feedback report bugs suggest improvements and ask questions here https github com remoted io remoted issues other useful links remoted io https remoted io remoted graphql api https remoted io graphql jobseeker source code https github com remoted io jobseeker remoted source code is licensed as agpl https github com remoted io remoted blob master license md made with by andre pena https twitter com andrerpena | cloud |
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RLPHF | h1 align center rlphf h1 this is the official github repository for personalized soups personalized large language model alignment via post hoc parameter merging https arxiv org abs 2310 11564 citation article jang2023personalized title personalized soups personalized large language model alignment via post hoc parameter merging author jang joel and kim seungone and lin bill yuchen and wang yizhong and hessel jack and zettlemoyer luke and hajishirzi hannaneh and choi yejin and ammanabrolu prithviraj journal arxiv preprint arxiv 2310 11564 year 2023 setup install dependencies pip install r requirements txt get the data and unzip it wget https storage googleapis com personalized soups data zip step 1 generate rollouts torchrun nnodes 1 nproc per node 1 net nfs cirrascale mosaic joel personalized rlhf generate rollouts py output dir output dir base model path to tulu ckpt dataset name data alpaca gpt4 10k json prompt generate a response that can be easily understood by an elementary school student batch size 16 start per 0 end per 100 to get the tulu checkpoints refer to this repository https arxiv org abs 2302 03202 feel free to put any customized prompt from the prompt config step 2 label generated rollouts using gpt4 cd gpt4 annotate python run py open ai key open ai key input dir rollout dir saving path save path annotators yaml file of annotator config the yaml file of the gpt4 annotator configs used for our experiments are provided in the gpt4 b5 directory first clone https github com tatsu lab alpaca farm git https github com tatsu lab alpaca farm git next place the gpt4 b5 directory inside alpaca farm auto annotations annotators and refer to the target yaml file e g pref1a yaml with the annotators config please refer to the alpacafarm code repo for more details step 3 training reward model next we utilize the gpt4 annotation for reward model training an example script is provided below torchrun nnodes 1 nproc per node 4 training reward model py model name path to tulu ckpt dataset name path to rm data eval dataset name eval dataset name output dir output dir per device train batch size 2 num train epochs 1 wandb project wandb project name wandb run name wandb run name you can find the list of reward model training data in the data rm training directory you can choose to create your own custom eval dataset during rm training step 4 policy model training here are sample script rns you can use to train each models traditional rlhf torchrun nnodes 1 nproc per node 4 training rlhf py dataset name data alpaca gpt4 10k json model name path to tulu ckpt reward model name dir to rm output dir output dir adafactor false save freq 10 output max length 512 batch size 16 gradient accumulation steps 8 batched gen true ppo epochs 8 learning rate 1 4e 5 mini batch size 2 early stopping true log with wandb val dataset name data koala eval 50 json val every n steps 10 wandb project wandb project name wandb run name wandb run name dir to rm is the directory to the adapter model bin from the reward model training output directory multitask training torchrun nnodes 1 nproc per node 4 training multitask training py base model path to tulu ckpt dataset name data alpca gpt4 10k mt json streaming lr scheduler type constant learning rate 1e 5 max steps 1000 output dir output dir project name wandb project name run name wandb run name p morl torchrun nnodes 1 nproc per node 4 training pmorl py dataset name data alpaca gpt4 pmorl 8 json model name path to tulu ckpt reward model name dir to rm output dir output dir adafactor false save freq 10 output max length 512 batch size 16 gradient accumulation steps 8 batched gen true ppo epochs 8 learning rate 1 4e 5 mini batch size 2 early stopping true log with wandb wandb project wandb project name wandb run name wandb run name val dataset name data koala eval 50 json val every n steps 10 p soups torchrun nnodes 1 nproc per node 4 training psoups py dataset name data psoups alpaca gpt4 p1a 10k json model name path to tulu ckpt reward model name dir to rm output dir output dir adafactor false save freq 10 output max length 512 batch size 16 gradient accumulation steps 8 batched gen true ppo epochs 8 learning rate 1 4e 5 mini batch size 2 early stopping true log with wandb wandb project wandb project name wandb run name wandb run name val dataset name data koala eval 50 json val every n steps 10 you can choose the different preference training files in data psoups directory step 5 generate model outputs example of generating outputs using trained policy models e g p morl torchrun nnodes 1 nproc per node 1 eval py output dir output dir base model path to tulu ckpt dataset name data koala eval 50 json prompt generate a response that can easily be understandable by an elementary school student generate a response that is concise and to the point without being verbose generate a response that is friendly witty funny and humorous like a close friend batch size 16 start per 0 end per 100 checkpoint dir policy model dir example of generating outputs using p soups torchrun nnodes 1 nproc per node 1 eval py output dir output dir base model path to tulu ckpt dataset name data koala eval 50 json prompt generate a response that can easily be understandable by an elementary school student generate a response that is concise and to the point without being verbose generate a response that is friendly witty funny and humorous like a close friend batch size 16 start per 0 end per 100 checkpoint dirs policy model dir 1 checkpoint dirs policy model dir 2 checkpoint dirs policy model dir 3 you can append any combination for the prompt configuration that you want to evaluate step 6 gpt4 evaluation after obtaining the model outputs from the previous step you could use gpt 4 as an evaluator to judge measure the win rate across different baselines go to gpt4 evaluate and run the following command python run py input dir1 first output file input dir2 second output file annotators annotators criteria wise eval gpt4 p1a yaml saving path eval results crit 1a json the demonstrations used for gpt4 evaluation and the criteria mentioned in the paper are all stored within gpt4 evaluate alpaca farm auto annotators criteria wise eval gpt4 feel free to add additional preferences you would like to evaluate on | ai |
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papers | papers repository of research papers on various topics databases machine learning software engineering | server |
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llm-fall-2023 | large language models grad center at cuny fall 2023 | ai |
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neon-wallet | p align center img src https raw githubusercontent com cityofzion visual identity develop coz 20branding logo logo 20icon png 20200x178px coz icon darkblue 200x178px png width 125px p h1 align center neon wallet h1 p align center electron wallet for the b neo b blockchain p p align center a href https circleci com gh cityofzion neon wallet img src https circleci com gh cityofzion neon wallet svg style svg a a href https coveralls io github cityofzion neon wallet branch dev img src https coveralls io repos github cityofzion neon wallet badge svg branch dev alt coverage status a p p align center img src app assets images wallet ss png p overview what does it currently do create a wallet encrypt a private key login with ledger private key encrypted private key or a stored account import export wallet accounts nep6 standard view balance view prices for gas and neo in multiple currencies send gas neo and any nep5 token claim gas send to multiple recipients address book switch networks test main nep9 qr support participate in neo token sales view wallet activity translation support for arabic chinese french german italian korean portuguese russian turkish and vietnamese installation the latest release binaries can be found here https neonwallet com to build manually see the steps below required tools and dependencies node this project uses the current lts node version yarn https yarnpkg com lang en docs install developing and running execute these commands in the project s root directory setup yarn installing node dependencies if you get any errors related to the node hid package please check installation instructions here https github com node hid node hid compiling from source on linux you may need to run sudo apt install libusb 1 0 0 libusb 1 0 0 dev for example developing yarn dev start the application in development mode with hot reloading enabled there is a known condition that may arise on linux systems where yarn dev builds but neon never opens try using yarn dev dev null running for production yarn assets yarn start testing yarn test or yarn run test watch for live testing support a gentle reminder github issues are meant to be used by developers for maintaining and improving the codebase and is not the proper location for support issues questions such as why can t i log in i lost my private key is there anyway to recover it why is my balance not showing should be asked in proper support channels such as the neo subreddit https www reddit com r neo or the official neo discord channel https discord gg r8v48ya you should also check the list of frequently asked questions faq https github com cityofzion awesome neo blob master resources faq md to see if your question has been answered there already releasing the ci process for deploys is triggered via tags the script below will automatically bump the version in package json and create a tag on whatever branch it is being run from ci will automatically create a draft release that must manually be promoted to the latest release currently release notes must also be manually generated to bump the patch version yarn create release patch to bump the minor version yarn create release minor to bump the major version yarn create release major | blockchain |
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aws-mobile-android-notes-tutorial | aws mobile android kotlin notes the aws mobile notes tutorial code for android java you can start the tutorial by looking in the tutorial directory tutorial index md license summary this sample code is made available under a modified mit license see the license license file | android | front_end |
beautifully-simple-app-ci | beautifully simple app ci this repository demonstrates some beautifully simple techniques for handling ci and ci for mobile apps it is the companion to my article tips and tricks for beautifully simple mobile app ci http www dwmkerr com tips and tricks for beautifully simple mobile app ci each sample project demonstrates the same fundamental techniques applied to a different combination of technologies and ci cd providers each pipeline can be run locally with ease sample build tool ci cd provider app distribution technology status react native app 1 react native app gradle gym circleci https circleci com gh dwmkerr beautifully simple app ci testfairy https testfairy com circleci https circleci com gh dwmkerr beautifully simple app ci svg style shield https circleci com gh dwmkerr beautifully simple app ci ionic 2 hybrid app 2 ionic app cordova travisci https travis ci org dwmkerr beautifully simple app ci hockeyapp https www hockeyapp net build status https travis ci org dwmkerr beautifully simple app ci svg branch master https travis ci org dwmkerr beautifully simple app ci native app 3 native app gradle xcode buddybuild https dashboard buddybuild com apps 58b6e6ddf3eea90100b2e721 build latest branch master buddybuild https dashboard buddybuild com apps 58b6e6ddf3eea90100b2e721 build latest branch master buddybuild https dashboard buddybuild com api statusimage appid 58b6e6ddf3eea90100b2e721 branch master build latest https dashboard buddybuild com apps 58b6e6ddf3eea90100b2e721 build latest branch master xamarin app 4 xamarinapp xbuild bitrise https www bitrise io app 8621395af91a1318 bitrise https www bitrise io app 8621395af91a1318 build status https www bitrise io app 8621395af91a1318 svg token xslhdofg35mclt1cvzt7rw branch master https www bitrise io app 8621395af91a1318 the commands there is a project wide makefile to quickly build everything command notes make build builds all apps to the artifacts folder here are each of the commands for each of the projects the react native app the source is at 1 react native app 1 react native app cd 1 react native app command example notes make test make test runs the unit tests make build make build builds the apps to the artifacts folder build specific versions with build android or build ios make deploy make deploy deploys apps from the artifacts folder to testfairy deploy specific versions with deploy android or deploy ios make label make label labels the icon with the current short git sha the cordova app the source is at 2 ionic app 2 ionic app cd 2 ionic app command example notes make test make test runs the tests currently a no op make build make build builds the apps to the artifacts folder build specific versions with build android or build ios make deploy make deploy deploys apps from the artifacts folder to testfairy deploy specific versions with deploy android or deploy ios make label make label build num 2 version 1 2 3 labels the icon with the current version default to what is in package json and build number defaults to 0 the native app the source is at 3 native app 3 native app cd 3 native app command example notes make test make test runs the tests currently a no op make label make label build num 2 label uat labels the icon with the label default to qa and build number defaults to 0 the xamarin app the source is at 4 xamarinapp 4 xamarinapp cd 2 xamarinapp command example notes make build make build builds the apps to the artifacts folder build specific versions with build android or build ios make label make label build num 2 env qa labels the icon with the current env default to production and build number defaults to 0 make name make name env qa sets the app id to com dwmkerr xamarinapp default or com dwmkerr xamarinapp env otherwise | front_end |
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nlup | nlup contains some base libraries i use in natural language processing projects some highlights confusion py classifier evaluation objects decorators py clever decorators for various purposes jsonable py a mix in which allows the state of most objects to be serialized to and deserialized from compressed json perceptron py perceptron like classifiers binary and multiclass including some forms of structured prediction reader py classes and readers for tagged and dependency parsed data timer py a with block that logs wall clock time elapsed all have been tested on cpython 3 4 1 and pypy 3 2 5 pypy version 2 3 1 they will not work on python 2 without modification some projects using nlup detector morse http github com cslu nlp detectormorse simple sentence boundary detection perceptronix point never http github com cslu nlp perceptronixpointnever simple part of speech tagging where s yr head at http github com cslu nlp wheresyrheadat simple transition based dependency parsing | ai |
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addons-frontend | addons frontend code of conduct https img shields io badge e2 9d a4 code 20of 20conduct blue svg https github com mozilla addons frontend blob master github code of conduct md circleci https circleci com gh mozilla addons frontend svg style svg https circleci com gh mozilla addons frontend codecov https codecov io gh mozilla addons frontend branch master graph badge svg https codecov io gh mozilla addons frontend documentation https readthedocs org projects addons frontend badge version latest http addons frontend readthedocs io en latest front end infrastructure and code to complement mozilla addons server https github com mozilla addons server security bug reports this code and its associated production website are included in mozilla s web and services bug bounty program if you find a security vulnerability please submit it via the process outlined in the program and faq pages further technical details about this application are available from the bug bounty onramp page please submit all security related bugs through bugzilla using the web security bug form never submit security related bugs through a github issue or by email bug bounty program https www mozilla org en us security web bug bounty faq pages https www mozilla org en us security bug bounty faq webapp bug bounty onramp page https wiki mozilla org security bugbountyonramp web security bug form https bugzilla mozilla org form web bounty requirements you need node https nodejs org en 16 x which is the current lts https github com nodejs release long term support release install yarn https yarnpkg com en to manage dependencies and run scripts the easiest way to manage multiple node versions in development is to use nvm https github com nvm sh nvm get started if you are on windows please make sure to follow windows guidelines docs windows md windows too type yarn to install all dependencies type yarn amo stage to start a local server that connects to a hosted staging server development commands here are some commands you can run command description yarn amo olympia start the dev server proxy for amo using data from a local addons server environment yarn amo dev start the dev server proxy for amo using data from the dev server https addons dev allizom org yarn amo dev https same as amo dev but with https available at https example com 3000 read about setting up this environment docs moz addon manager md developing with a local https server recommended yarn amo stage start the dev server proxy for amo using data from the staging server https addons allizom org yarn build build the app yarn build ci run the build and bundlewatch npm scripts yarn bundlewatch run bundlewatch to check the generated amo bundle sizes building amo is required first building and running services yarn flow run flow by default this checks for errors and exits yarn flow check explicitly check for flow errors and exit yarn flow dev continuously check for flow errors yarn eslint lint the js yarn start func test server start a docker container for functional tests yarn stylelint lint the scss yarn lint run all the js scss linters yarn prettier run prettier to automatically format the entire codebase yarn prettier dev run pretty quick to automatically compare and format modified source files against the master branch yarn prettier ci run prettier and fail if some code has been changed without being formatted yarn version check check you have the required dependencies yarn test run all tests enters jest in watch mode yarn test debug run all tests with full console output and full error messages enters jest in watch mode yarn test coverage run all tests and generate code coverage report enters jest in watch mode yarn test coverage once run all tests generate code coverage report then exit yarn test once run all tests run all js scss linters then exit yarn test ci run all continuous integration checks this is only meant to run on ci running tests you can enter the interactive jest mode by typing yarn test or yarn test debug this is the easiest way to develop new features here are a few tips yarn test will hide most console output and detailed test failure messages so it is best when you are running a full suite of tests when working on an individual test you likely want to run yarn test debug when you start yarn test you can switch to your code editor and begin adding test files or changing existing code as you save each file jest will only run tests related to the code you change if you had typed a when you first started then jest will continue to run the full suite even when you change specific files type o to switch back to the mode of only running tests related to the files you are changing sometimes running tests related to your file changes is slow in these cases you can type p or t to filter tests by name while you working fixing a specific test suite more info https github com jest community jest watch typeahead if you see something like error watching file for changes emfile on mac os then brew install watchman might fix it see https github com facebook jest issues 1767 run a subset of the tests by default yarn test will only run a subset of tests that relate to the code you are working on to explicitly run a subset of tests you can type t or p which are explained in the jest watch usage alternatively you can start the test runner with a specific file or regular expression https facebook github io jest docs en cli html jest regexfortestfiles like yarn test tests unit amo components testaddon js run all tests if you want to run all tests and exit type yarn test once eslint as you run tests you will see a report of eslint errors at the end of the test output yarn test if you would like to run tests without eslint checks set an environment variable no eslint 1 yarn test flow there is limited support for using flow https flowtype org to validate the intention of our program as you run tests you will see a report of flow errors at the end of the test output yarn test if you would like to run tests without flow checks set an environment variable no flow 1 yarn test to only check for flow issues during development while you edit files run yarn flow dev if you are new to working with flow here are some tips check out the getting started https flow org en docs getting started guide read through the web ext guide https github com mozilla web ext blob master contributing md check for flow errors for hints on how to solve common flow errors to add flow coverage to a source file put a flow comment at the top the more source files you can opt into flow the better here is our flow manifesto we use flow to declare the intention of our code and help others refactor it with confidence flow also makes it easier to catch mistakes before spending hours in a debugger trying to find out what happened avoid magic flow declarations https flowtype org en docs config libs for any internal code just declare a type alias https flowtype org en docs types aliases next to the code where it s used and export import https flow org en docs types modules it like any other object never import a real js object just to reference its type make a type alias and import that instead never add more type annotations than you need flow is really good at inferring types from standard js code it will tell you when you need to add explicit annotations when a function like getalladdons takes object arguments call its type object getalladdonsparams example js type getalladdonsparams categoryid number function getalladdons categoryid getalladdonsparams use exact object types https flowtype org en docs types objects toc exact object types via the pipe syntax key when possible sometimes the spread operator triggers an error like inexact type is incompatible with exact type but that s a bug https github com facebook flow issues 2405 you can use the exact t workaround from src amo types util https github com mozilla addons frontend blob master src amo types util js if you have to this is meant as a working replacement for exact t https flow org en docs types utilities toc exact add a type hint for components wrapped in hocs higher order components so that flow can validate calls to the component we need to add a hint because we don t yet have decent type coverage for all the hocs we rely on here is an example js imagine this is something like components confirmbutton index js import compose from redux import as react from react this expresses externally used props i e to validate how the app would use confirmbutton type props prompt string null this expresses internally used props such as i18n which is injected by translate type internalprops props i18n i18ntype export class confirmbuttonbase extends react component internalprops render const prompt this props prompt this props i18n gettext confirm return button prompt button this provides a type hint for the final component with its external props the i18n prop is not in external props because it is injected by translate for internal use only const confirmbutton react componenttype props compose translate confirmbuttonbase export default confirmbutton try to avoid loose types like object or any but feel free to use them if you are spending too much time declaring types that depend on other types that depend on other types and so on you can add a flowfixme comment to skip a flow check if you run into a bug or if you hit something that s making you bang your head on the keyboard if it s something you think is unfixable then use flowignore instead please explain your rationale in the comment and link to a github issue if possible if you re stumped on why some flow annotations aren t working try using the yarn flow type at pos command to trace which types are being applied to the code see yarn flow help type at pos for details prettier we use prettier to automatically format our javascript code and stop all the on going debates over styles code coverage to see a report of code coverage type yarn test coverage once this will print a table of files showing the percentage of code coverage the uncovered lines will be shown in the right column but you can open the full report in a browser open coverage lcov report index html running amo for local development a proxy server is provided for running the amo app with the api on the same host as the frontend this mimics our production setup start developing against a hosted api like this yarn amo dev this configures the proxy to use https addons dev allizom org for api data this command is the most common way to develop new frontend features see the table of commands up above for similar ways to run the server to use a local api server running in docker https addons server readthedocs io en latest topics install index html you can use the yarn amo command however this is currently not working see issue 7196 authentication will work when initiated from addons frontend and will persist to addons server but it will not work when logging in from an addons server page see mozilla addons server 4684 https github com mozilla addons server issues 4684 for more information on fixing this local configuration if you need to override any settings while running yarn amo yarn amo dev or yarn amo stage first create a local config file named exactly like this touch config local development js make any config changes for example javascript module exports trackingenabled true restart the server to see it take affect consult the config file loading order docs https github com lorenwest node config wiki configuration files file load order to learn more about how configuration is applied configuring an android device for local development if you want to access your local server on an android device you will need to change a few settings let s say your local machine is accessible on your network at the ip address 10 0 0 1 you could start your server like this api host http 10 0 0 1 3000 server host 10 0 0 1 webpack server host 10 0 0 1 yarn amo dev on your android device you could then access the development site at http 10 0 0 1 3000 note at this time it is not possible to sign in with this configuration because the mozilla accounts client redirects to localhost 3000 you may be able to try a different approach by editing etc hosts on your device so that localhost points to your development machine but this has not been fully tested disabling csp for local development when developing locally with a webpack server the randomly generated asset url will fail our content security policy csp and clutter your console with errors you can turn off all csp errors by settings csp to false in any local config file such as local development amo js example javascript module exports csp false working on the documentation the documentation you are reading right now lives inside the source repository as github flavored markdown https guides github com features mastering markdown github flavored markdown when you make changes to these files you can create a pull request to preview them or better yet you can use grip https github com joeyespo grip to preview the changes locally after installing grip run it from the source directory like this grip open its localhost url and you will see the rendered readme md file as you make edits it will update automatically building and running services the following are scripts that are used in deployment you generally won t need unless you re testing something related to deployment or builds the env vars are node env the node environment e g production or development node config env the name of the configuration to load e g dev stage prod script description yarn start starts the express server requires env vars yarn build builds the libs all apps requires env vars example building and running a production instance of the app node env production node config env prod yarn build node env production node config env prod yarn start running builds locally to run the app locally in production mode you ll need to create a config file for local production builds production builds can be built for different environments dev stage and prod controlled by the node config env env var but only one extra config file is needed for these environments to run locally rename the file named config local js dist to config local js after this re build and restart using yarn build and yarn start as documented above if you have used 127 0 0 1 before with a different configuration be sure to clear your cookies the application should be available at http 127 0 0 1 4000 note at this time it s not possible to sign in using this approach what version is deployed you can check to see what commit of addons frontend is deployed which a b experiments are running or which feature flags are enabled by making a request like this curl https addons dev allizom org frontend version build https circleci com gh mozilla addons frontend 10333 commit 47edfa6f24e333897b25516c587f504e294e8fa9 experiments homehero true feature flags enablefeatureaminstallbutton true enablefeaturestaticthemes true source https github com mozilla addons frontend version this will return a 415 response if a version json file doesn t exist in the root directory this file is typically generated by the deploy process for consistency with monitoring scripts the same data can be retrieved at this url curl https addons dev allizom org version bulb you can install the amo info extension https addons mozilla org en us firefox addon amo info to easily view this information addons frontend blog utils this project also contains code to build a library named addons frontend blog utils and offers the following commands yarn build blog utils dev build the library start a watcher to rebuild the library on change and serve a development page at http 127 0 0 1 11000 yarn build blog utils prod build the library in production mode this library is exclusively designed to work with addons blog release process in order to publish a new version of addons frontend blog utils a special tag has to be pushed to the main repository the tag name must start with blog utils and usually contains the version number this can be automated using the following command npm version major minor patch issuing this command from the master branch will update the version in the package json create a commit and create a tag push both this commit and the tag to the main repository note when a new addons frontend blog utils release is merged in addons blog you should publish a new version of the wordpress theme please follow these instructions in the addons blog repository addons blog wp theme core technologies based on redux react code written in es2015 universal rendering via node unit tests with high coverage aiming for 100 bundlewatch https github com bundlewatch bundlewatch jest https jestjs io docs en getting started html prettier https prettier io addons blog https github com mozilla addons blog issue 7196 https github com mozilla addons frontend issues 7196 addons blog wp theme https github com mozilla addons blog about the wordpress theme | addons mozilla react redux amo | front_end |
fabric-sdk-go | hyperledger fabric client sdk for go release https img shields io github release hyperledger fabric sdk go svg style flat square https github com hyperledger fabric sdk go releases latest license https img shields io badge license apache 202 0 blue svg https raw githubusercontent com hyperledger fabric sdk go main license godoc https godoc org github com hyperledger fabric sdk go status svg https godoc org github com hyperledger fabric sdk go build status https dev azure com hyperledger fabric sdk go apis build status hyperledger fabric sdk go branchname main https dev azure com hyperledger fabric sdk go build latest definitionid 19 branchname main codecov https codecov io gh hyperledger fabric sdk go branch main graph badge svg https codecov io gh hyperledger fabric sdk go go report card https goreportcard com badge github com hyperledger fabric sdk go https goreportcard com report github com hyperledger fabric sdk go this sdk enables go developers to build solutions that interact with hyperledger fabric http hyperledger fabric readthedocs io en latest getting started obtain the client sdk packages for fabric and fabric ca bash go get github com hyperledger fabric sdk go you re good to go happy coding check out the examples for usage demonstrations documentation sdk documentation can be viewed at godoc https godoc org github com hyperledger fabric sdk go the packages intended for end developer usage are within the pkg client folder along with the main sdk package pkg fabsdk if you wish to use the fabric gateway programming model then the api is in the pkg gateway folder examples e2e test test integration e2e end to end go basic example that uses sdk to query and execute transaction ledger query test test integration pkg client ledger ledger queries test go basic example that uses sdk to query a channel s underlying ledger multi org test test integration e2e orgs multiple orgs test go an example that has multiple organisations involved in transaction dynamic endorser selection test integration pkg fabsdk provider sdk provider test go an example that uses dynamic endorser selection based on chaincode policy e2e pkcs11 test test integration e2e pkcs11 e2e test go e2e test using a pkcs11 crypto suite and configuration more examples needed community discussion is happening in rocket chat https chat hyperledger org channel fabric sdk go issue tracking is handled in jira https jira hyperledger org secure rapidboard jspa projectkey fab rapidview 7 view planning client sdk current compatibility the sdk s integration tests run against three tagged fabric versions prev currently v1 4 7 stable currently v2 2 0 prerelease currently disabled additionally for development purposes integration tests also run against the devstable fabric version as needed retired versions when the prev code level is updated the last tested fabric sdk go commit or tag is listed below fabric v1 3 ac70276 fabric v1 2 5e291d3 fabric v1 1 f7ae259 fabric v1 0 5ac5226 running the test suite obtain the client sdk packages for fabric and fabric ca bash git clone https github com hyperledger fabric sdk go git bash in the fabric sdk go directory cd fabric sdk go optional automatically install go tools used by test suite make depend running test suite make clean test suite run artifacts make clean go tags the following go tags can be supplied to enable additional functionality experimental includes support for experimental features contributing to the go sdk if you want to contribute to the go sdk please run the test suite and submit patches for review for general guidelines please refer to the fabric project s contribution page http hyperledger fabric readthedocs io en latest contributing html you need go 1 14 make docker docker compose git gobin go111module off go get u github com myitcv gobin libtool notes dependencies are handled using go modules https github com golang go wiki modules running a portion of the test suite bash in the fabric sdk go directory cd fabric sdk go optional automatically install go tools used by test suite make depend optional running only code checks linters license spelling etc make checks running all unit tests and checks make unit test running all integration tests make integration test running package unit tests manually bash in a package directory go test running integration tests manually you need a working fabric and fabric ca set up it is recommended that you use the docker compose file provided in test fixtures dockerenv it is also recommended that you use the default env settings provided in test fixtures dockerenv see steps below customized settings in the test fixtures config config test yaml in case your hyperledger fabric network is not running on localhost or is using different ports testing with fabric images at docker hub the test suite defaults to the latest compatible tag of fabric images at docker hub the following commands starts fabric bash in the fabric sdk go directory cd fabric sdk go start fabric stable tag make dockerenv stable up or more generally start fabric at a different code level prev stable prerelease devstable make dockerenv codelevel up running integration tests fabric should now be running in a different shell run integration tests bash in the fabric sdk go directory cd fabric sdk go use script to setup parameters for integration tests and execute them previously we use to have hostnames like fabric ca server orderer and peer pointed to localhost now since we removed this now we will be using a different configuration make integration tests local or more generally run integration tests at a different code level prev stable prerelease devstable and fixture target version fabric codelevel ver ver fabric codelevel tag codelevel make integration tests local bash previously we use to have hostnames like fabric ca server orderer and peer pointed to localhost now since we removed this now we will be using a different config file config test local yaml which has the fabric ca server orderer and peers pointed to localhost it is also possible to run integration tests using go test directly for example cd fabric sdk go test integration go test args testlocal true cd fabric sdk go test integration orgs go test args testlocal true you should review test scripts integration sh for options and details note you should generally prefer the scripted version to setup parameters for you testing with local build of fabric advanced alternatively you can use a local build of fabric using the following commands bash start fabric devstable codelevel with latest docker tags make dockerenv latest up license hyperledger fabric sdk go software is licensed under the apache license version 2 0 license this document is licensed under a a rel license href http creativecommons org licenses by 4 0 creative commons attribution 4 0 international license a | hyperledger-fabric go distributed-ledger blockchain hyperledger | blockchain |
RTOS-RTX51-Tiny-Tutorial | rtos rtx51 tiny tutorial | os |
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CS224n_NLP | hits https hitcounter pythonanywhere com count tag svg url https 3a 2f 2fgithub com 2fskksaikia 2fcs224n nlp cs224n natural language processing with deep learning http web stanford edu class cs224n img src https github com skksaikia cs224n nlp blob master nlp png cs224n is a nlp deep learning course at stanford this course is open and you ll find everything in their course website gotta learn this course and start my nlp journey the notes are amazing the course is amazing let s get started b book b foundations of statistical natural language processing chris manning hinrich sch tze https github com skksaikia cs224n nlp blob master ed3book pdf speech and language processing 3rd edition in making dan jurafsky james h martin https github com skksaikia cs224n nlp blob master manningschutze 1999 foundationsofstatisticalnaturallanguageprocessing pdf natural language processing with python https github com skksaikia cs224n nlp blob master natural 20language 20processing 20with 20python pdf nltk essentials https github com skksaikia cs224n nlp blob master nltk 20essentials pdf p align justify natural language processing nlp is one of the most important technologies of the information age understanding complex language utterances is also a crucial part of artificial intelligence applications of nlp are everywhere because people communicate most everything in language web search advertisement emails customer service language translation radiology reports etc there are a large variety of underlying tasks and machine learning models behind nlp applications recently deep learning approaches have obtained very high performance across many different nlp tasks these can solve tasks with single end to end models and do not require traditional task specific feature engineering in this winter quarter course students will learn to implement train debug visualize and invent their own neural network models the course provides a thorough introduction to cutting edge research in deep learning applied to nlp on the model side we will cover word vector representations window based neural networks recurrent neural networks long short term memory models recursive neural networks convolutional neural networks as well as some recent models involving a memory component through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems p the winter 2017 version lectures are available here at youtube https www youtube com watch v oqq w 63ugq list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 assignments http web stanford edu class cs224n assignments html assignment1 https github com skksaikia cs224n nlp tree master assignment assignment1 solution https github com skksaikia cs224n nlp blob master assignment assignment1 assignment1 solution pdf assignment2 https drive google com drive folders 1iwvt1fd5jd07k2hntv iflfa5cqdkqoh usp sharing solution https drive google com file d 1vnoetmz vbi4fwxjec1vqx0jvm3unyp6 view usp sharing assignment3 https github com skksaikia cs224n nlp tree master assignment assignment3 solution https github com skksaikia cs224n nlp blob master assignment assignment3 assignment3 soln pdf course https web stanford edu class archive cs cs224n cs224n 1184 syllabus html h2 b natural language processing b h2 introduction to nlp and deep learning word vectors neural networks backpropagation and project advice introduction to tensorflow dependency parsing recurrent neural networks and language models vanishing gradients fancy rnns machine translation seq2seq and attention advanced attention transformer networks and cnns coreference resolution tree recursive neural networks and constituency parsing advanced architectures and memory networks reinforcement learning for nlp guest lecture semi supervised learning for nlp future of nlp models multi task learning and qa systems the notes and slides are available in the course website here http web stanford edu class cs224n syllabus html exams 2018 winter midterm https github com skksaikia cs224n nlp blob master midterm cs224n midterm 2018 pdf solution https github com skksaikia cs224n nlp blob master midterm cs224n midterm 2018 solution pdf 2017 winter midterm https github com skksaikia cs224n nlp blob master practice midterm cs224n practice midterm 3 pdf solution https github com skksaikia cs224n nlp blob master practice midterm cs224n practice midterm 3 sol pdf 2017 winter prac mid1 https github com skksaikia cs224n nlp blob master practice midterm cs224n practice midterm 1 pdf solution https github com skksaikia cs224n nlp blob master practice midterm cs224n practice midterm 1 pdf 2017 winter prac mid2 https github com skksaikia cs224n nlp blob master practice midterm cs224n practice midterm 2 pdf solution https github com skksaikia cs224n nlp blob master practice midterm cs224n practice midterm 2 sol pdf udacity nlp nanodegree https github com skksaikia nlpnanod nlp dan jurafsky christopher manning https www youtube com watch v 3dt yh1mf u list plqiyvnmpdlknzybtuolsi9mi9waerftfm nltk with python 3 for natural language processing https www youtube com watch v flzvoksckxy list plqvvvaa0qudf2jswnfigklibinznic4hl nltk https www nltk org how to solve 90 of nlp problems a step by step guide https blog insightdatascience com how to solve 90 of nlp problems a step by step guide fda605278e4e natural language processing is fun https medium com ageitgey natural language processing is fun 9a0bff37854e a practitioner s guide to nlp i https towardsdatascience com a practitioners guide to natural language processing part i processing understanding text 9f4abfd13e72 final project past projects http web stanford edu class cs224n reports html as a part of this course i did this project | ai |
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design-system-ui-kit | decorative slds image https user images githubusercontent com 1750832 85477474 3dbcc480 b56f 11ea 9aeb b3d478138040 png br br salesforce lightning design system slds ui kit the salesforce lightning design system slds ui kit is a collection of design resources to support designing and prototyping using a href https www lightningdesignsystem com target blank lightning design system a components tokens and design patterns there are also useful resources to help make design workflows more efficient with artifacts like page templates wireframes key product screens and components for writing specifications quick start download and install these libraries through the slds plugin for sketch https www lightningdesignsystem com tools sketch br br requirements sketch download and install the most recent version of sketch https www sketchapp com br em if you do not have sketch you can use the a href https packages framer com package darshilv lightning design system ui kit target blank title framer slds ui kit framer slds ui kit a or a href https www figma com salesforce target blank title figma slds ui kit figma slds ui kit a however they may not be as up to date as the sketch files here em slds plugin for sketch download and install the most recent version of slds plugin for sketch https www lightningdesignsystem com tools sketch salesforce sans fonts download and install salesforce sans https github com salesforce ux design system tree master assets fonts from the design system repository br br getting started the installation of slds sketch libraries is handled through the slds plugin for sketch https www lightningdesignsystem com tools sketch visit the plugin page https www lightningdesignsystem com tools sketch on the slds website to read more br br design file descriptions slds components for web br sketch equivalents of component blueprints https www lightningdesignsystem com components overview and tokens https www lightningdesignsystem com design tokens as seen on the slds website slds components for mobile br sketch collection of native mobile patterns and mobile web coded components slds icon library br a file of design system icons https www lightningdesignsystem com icons which is automatically generated from design system code pattern builder br builder design guideline https www lightningdesignsystem com guidelines builder customized component symbols pattern user engagement br user engagement design guideline https www lightningdesignsystem com guidelines user engagement overview customized component symbols pattern chart br chart design guideline https www lightningdesignsystem com guidelines charts customized component symbols pattern rules filters and logic br rfl design guideline https www lightningdesignsystem com guidelines rules filters logic customized component symbols standard artboards br based on user data sketch artboards are sized to the common viewport dimensions used spec library br a collection of symbols to use when documenting dimensions and details of designs for engineers wireframes br grey box stencils of common lightning interfaces key screens br a collection of the most common product screens on mobile and desktop coming soon br br contributing feature requests and bug reporting the slds team welcomes your feedback to help maintain these design resources please add any bugs or feature requests under the issues https github com salesforce ux design system ui kit issues tab of this repository external contributions are currently closed br throughout a release salesforce s design team contributes to these sketch files through an application called a href https www abstract com target blank title abstract abstract a public contributions become unmanagable to review and merge since github doesn t have the capability to view binary files br br license all icons and images are licensed under creative commons attribution noderivatives 4 0 https github com salesforce ux licenses blob master license icons images txt br br decorative slds image https user images githubusercontent com 1750832 85477474 3dbcc480 b56f 11ea 9aeb b3d478138040 png | salesforce-lightning sketch | os |
SQL-Employee-Database | employee database a mystery in two parts sql png images sql png background a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files 1 data modeling erd png images erd png 2 data engineering a table schema specifying data types primary keys foreign keys and other constraints is created for each of the six csv files and each csv file is imported into the corresponding sql table 3 data analysis list the following details of each employee employee number last name first name gender and salary list employees who were hired in 1986 list the manager of each department with the following information department number department name the manager s employee number last name first name and start and end employment dates list the department of each employee with the following information employee number last name first name and department name list all employees whose first name is hercules and last names begin with b list all employees in the sales department including their employee number last name first name and department name list all employees in the sales and development departments including their employee number last name first name and department name in descending order list the frequency count of employee last names i e how many employees share each last name the sql database is then imported into pandas and visualizations are generated for the employee data as below average salary bytitle png images average salary bytitle png salary range employees png images salary range employees png | sql sql-database sqlalchemy sqlalchemy-python employee-database | server |
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