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openmlsys-zh | https openmlsys github io pdf 2022 27 06 2022 openmlsys ai 60 https zhuanlan zhihu com p 531498210 https github com openmlsys openmlsys cuda jie ren https github com jieren98 wenteng liang https github com went liang 17 03 2022 issue info editors md pr custom operators ai c c computational graph intermediate representation operator runtime gpu ascend ai safety critical ai ai info info md pdf pr markdown info style md info terminology md | machine-learning software-architecture textbook computer-systems | os |
Blockchain-Known-Pools | blockchain pools bitcoin mining known pools tracking tags for https blockchain info pools https www blockchain com pools contributions welcome guidelines provide only 1 mining pool by pr the website provided must be available | blockchain |
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FirmAE | firmae firmae is a fully automated framework that performs emulation and vulnerability analysis firmae significantly increases the emulation success rate from firmadyne https github com firmadyne firmadyne s 16 28 to 79 36 with five arbitration techniques we tested firmae on 1 124 wireless router and ip camera firmware images from top eight vendors we also developed a dynamic analysis tool for 0 day discovery which infers web service information based on the filesystem and kernel logs of target firmware by running our tool on the succesfully emulation firmware images we discovered 12 new 0 days which affect 23 devices installation note that we tested firmae on ubuntu 18 04 1 clone firmae console git clone recursive https github com pr0v3rbs firmae 2 run download sh script console download sh 3 run install sh script console install sh usage 1 execute init sh script console init sh 2 prepare a firmware console wget https github com pr0v3rbs firmae releases download v1 0 dir 868l fw revb 2 05b02 eu multi 20161117 zip 3 check emulation console sudo run sh c brand firmware 4 analyze the target firmware analysis mode uses the firmae analyzer console sudo run sh a brand firmware run mode helps to test web service or execute custom analyzer console sudo run sh r brand firmware debug after run sh c finished 1 user level basic debugging utility useful when an emulated firmware is network reachable console sudo run sh d brand firmware 2 kernel level boot debugging console sudo run sh b brand firmware turn on off arbitration check the five arbitrations environment variable in the firmae config sh head firmae config bin sh firmae boot true firmae network true firmae nvram true firmae kernel true firmae etc true if firmae etc then timeout 240 docker first prepare a docker image console docker init sh parallel mode then run one of the below commands ec checks only the emulation and ea checks the emulation and analyzes vulnerabilities console docker helper py ec brand firmware docker helper py ea brand firmware debug mode after a firmware image successfully emulated console docker helper py ed firmware evaluation emulation result google spreadsheet view https docs google com spreadsheets d 1dbkxr woz7umneoogug1zykj1erpfk gzrnni8djroi edit usp sharing dataset google drive download https drive google com file d 1hdm75nvkbvs evh9rkb5xfgrynsnsg 8 view usp sharing cves asus cve 2019 20082 https github com pr0v3rbs cve tree master cve 2019 20082 belkin belkin01 https github com pr0v3rbs cve tree master belkin01 d link cve 2018 20114 https github com pr0v3rbs cve tree master cve 2018 20114 cve 2018 19986 https github com pr0v3rbs cve tree master cve 2018 19986 20 2019990 cve 2018 19986 hnap1setroutersettings cve 2018 19987 https github com pr0v3rbs cve tree master cve 2018 19986 20 2019990 cve 2018 19987 hnap1setaccesspointmode cve 2018 19988 https github com pr0v3rbs cve tree master cve 2018 19986 20 2019990 cve 2018 19988 hnap1setclientinfodemo cve 2018 19989 https github com pr0v3rbs cve tree master cve 2018 19986 20 2019990 cve 2018 19989 hnap1setqossettings cve 2018 19990 https github com pr0v3rbs cve tree master cve 2018 19986 20 2019990 cve 2018 19990 hnap1setwifiverifyalpha cve 2019 6258 https github com pr0v3rbs cve tree master cve 2019 6258 cve 2019 20084 https github com pr0v3rbs cve tree master cve 2019 20084 trendnet cve 2019 11399 https github com pr0v3rbs cve tree master cve 2019 11399 cve 2019 11400 https github com pr0v3rbs cve tree master cve 2019 11400 authors this research project has been conducted by syssec lab https syssec kr at kaist mingeun kim https pr0v3rbs blogspot kr dongkwan kim https 0xdkay me eunsoo kim https hahah kim suryeon kim yeongjin jang https www unexploitable systems yongdae kim https syssec kaist ac kr yongdaek citation we would appreciate if you consider citing our paper https syssec kaist ac kr pub 2020 kim acsac2020 pdf when using firmae bibtex inproceedings kim 2020 firmae author mingeun kim and dongkwan kim and eunsoo kim and suryeon kim and yeongjin jang and yongdae kim title firmae towards large scale emulation of iot firmware for dynamic analysis booktitle annual computer security applications conference acsac year 2020 month dec address online | emulation firmware arbitration analysis fuzzing | server |
car-reservation | a name readme top a div align center ruby https img shields io badge ruby 23cc342d svg style for the badge logo ruby logocolor white shell script https img shields io badge shell script 23121011 svg style for the badge logo gnu bash logocolor white postgres https img shields io badge postgres 23316192 svg style for the badge logo postgresql logocolor white rails https img shields io badge rails 23cc0000 svg style for the badge logo ruby on rails logocolor white div table of contents about the project about project built with built with tech stack tech stack key features key features getting started getting started setup setup prerequisites prerequisites install install usage usage run tests run tests authors authors future features future features contributing contributing show your support support acknowledgements acknowledgements license license car reservation a name about project a this repo holds the backend development source for the car reservation app the world s best free car reservation website car reservation allows user to add car and see the car model and shop it built with a name built with a tech stack a name tech stack a describe the tech stack and include only the relevant sections that apply to your project details summary frontend ui summary ul li a href https github com pamphilemkp car reservation frontend react redux frontend repo a li ul details key features a name key features a add an accommodation make a reservation p align right a href readme top back to top a p kanban board kanban link https github com users pamphilemkp projects 3 views 1 kanban empty screen shot https github com pamphilemkp car reservation issues 11 final members 5 getting started a name getting started a describe how a new developer could make use of your project to get a local copy up and running follow these steps prerequisites in order to work on this project you need to have the following dependencies installed ul li a href https www ruby lang org en ruby a li li a href https www postgresql org postgresql a li li a href https nodejs org en node js a li li a href https yarnpkg com yarn a li li a href https rubyonrails org rails a li ul example command sh gem install rails setup clone this repository to your desired folder sh git clone https github com pamphilemkp car reservation git cd budget app commands to run to get all the gems required for the project bundle install to install packages such as style linters npm install to check linters locally use rubocop npx stylelint css scss once you have the project correctly set up run to run all migrations create the database for testing and for development and insert some data into the database for you to visualize the changes bin rails db setup finally each time you make changes to the project run in the root folder to check the consistency of the app please don t make changes to the tests unless completely necessary and mention it in your pr description rspec getting started check the ruby version or if you have at all by running ruby v if you get something like this ruby 2 6 8p205 2021 07 07 revision 67951 you have ruby installed clone the repository by running git clone https github com pamphilemkp car reservation git in your cli type cd exploration getaways backend type code run bundle install note you need to have a master key in config directory if none exists generate one first delete credentials yml enc run editor code bundle exec rails credentials edit run rails db create db migrate optionally you can run rails db seed to populate it run rails server to open the local server open browser http localhost 3000 usage to run the project execute the following command rails server run tests to run tests run the following command sh bin rails test test models article test rb api documentation open browser and visit http localhost 3000 api docs api documentation screenshot https user images githubusercontent com 98436409 211160780 b2cd9819 dc13 4fd8 beed 675c1f25ab96 png p align right a href readme top back to top a p writing hand authors a name authors a man technologist pamphile mkp writing hand github pamphilemkp https github com pamphilemkp twitter pamphilemkp https twitter com pamphilemusonda linkedin pamphilemkp https www linkedin com in pamphile musonda man technologist basit ali writing hand github githubhandle https github com basitali111 linkedin linkedin https www linkedin com in basit ali jobs twitter twitter https twitter com basital35031734 p align right a href readme top back to top a p man technologist patrick maina writing hand github pndungumaina https github com pndungumaina linkedin patrick maina https www linkedin com in pndungumaina twitter ndunguwamaina https twitter com ndunguwamaina man technologist mark otuya writing hand github githubhandle https github com markotuya0 linkedin linkedin https www linkedin com in mark otuya twitter twitter https twitter com mark anthonny man technologist adebowale adegboye monsur writing hand github githubhandle https github com ademibowale twitter twitterhandle https twitter com ademibowale1 linkedin linkedin https www linkedin com in adebowale adegboye 143568221 future features a name future features a allow user to upload avatar include my profile section page p align right a href readme top back to top a p contributing a name contributing a contributions issues and feature requests are welcome feel free to check the issues page issues p align right a href readme top back to top a p show your support a name support a if you like this project or find it useful interesting please make sure you give a since this will make it easily accesible for you too p align right a href readme top back to top a p acknowledgments a name acknowledgements a a rel license href http creativecommons org licenses by 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by 4 0 88x31 png a br this work is licensed under a a rel license href http creativecommons org licenses by murat korkmaz creative commons attribution 4 0 international license a p align right a href readme top back to top a p license license a name license a this project is mit mit md licensed note we recommend using the mit license https choosealicense com licenses mit you can set it up quickly by using templates available on github https docs github com en communities setting up your project for healthy contributions adding a license to a repository you can also use any other license https choosealicense com licenses if you wish p align right a href readme top back to top a p table of contents about the project about project built with built with tech stack tech stack key features key features getting started getting started setup setup prerequisites prerequisites install install usage usage run tests run tests authors authors future features future features contributing contributing show your support support acknowledgements acknowledgements license license car reservation a name about project a this repo holds the backend development source for the car reservation app the world s best free car reservation website car reservation allows user to add car and see the car model and shop it built with a name built with a tech stack a name tech stack a describe the tech stack and include only the relevant sections that apply to your project details summary frontend ui summary ul li a href https github com pamphilemkp car reservation frontend react redux frontend repo a li ul details key features a name key features a add an accommodation make a reservation p align right a href readme top back to top a p kanban board kanban link https github com users pamphilemkp projects 3 views 1 kanban empty screen shot https github com pamphilemkp car reservation issues 11 final members 5 getting started a name getting started a describe how a new developer could make use of your project to get a local copy up and running follow these steps prerequisites in order to work on this project you need to have the following dependencies installed ul li a href https www ruby lang org en ruby a li li a href https www postgresql org postgresql a li li a href https nodejs org en node js a li li a href https yarnpkg com yarn a li li a href https rubyonrails org rails a li ul example command sh gem install rails setup clone this repository to your desired folder sh git clone https github com pamphilemkp car reservation git cd budget app commands to run to get all the gems required for the project bundle install to install packages such as style linters npm install to check linters locally use rubocop npx stylelint css scss once you have the project correctly set up run to run all migrations create the database for testing and for development and insert some data into the database for you to visualize the changes bin rails db setup finally each time you make changes to the project run in the root folder to check the consistency of the app please don t make changes to the tests unless completely necessary and mention it in your pr description rspec getting started check the ruby version or if you have at all by running ruby v if you get something like this ruby 2 6 8p205 2021 07 07 revision 67951 you have ruby installed clone the repository by running git clone https github com pamphilemkp car reservation git in your cli type cd exploration getaways backend type code run bundle install note you need to have a master key in config directory if none exists generate one first delete credentials yml enc run editor code bundle exec rails credentials edit run rails db create db migrate optionally you can run rails db seed to populate it run rails server to open the local server open browser http localhost 3000 usage to run the project execute the following command rails server run tests to run tests run the following command sh bin rails test test models article test rb api documentation open browser and visit http localhost 3000 api docs api documentation screenshot https user images githubusercontent com 98436409 211160780 b2cd9819 dc13 4fd8 beed 675c1f25ab96 png p align right a href readme top back to top a p writing hand authors a name authors a man technologist pamphile mkp writing hand github pamphilemkp https github com pamphilemkp twitter pamphilemkp https twitter com pamphilemusonda linkedin pamphilemkp https www linkedin com in pamphile musonda man technologist basit ali writing hand github githubhandle https github com basitali111 linkedin linkedin https www linkedin com in basit ali jobs twitter twitter https twitter com basital35031734 p align right a href readme top back to top a p man technologist patrick maina writing hand github pndungumaina https github com pndungumaina linkedin patrick maina https www linkedin com in pndungumaina twitter ndunguwamaina https twitter com ndunguwamaina man technologist mark otuya writing hand github githubhandle https github com markotuya0 linkedin linkedin https www linkedin com in mark otuya twitter twitter https twitter com mark anthonny man technologist adebowale adegboye monsur writing hand github githubhandle https github com ademibowale twitter twitterhandle https twitter com ademibowale1 linkedin linkedin https www linkedin com in adebowale adegboye 143568221 future features a name future features a allow user to upload avatar include my profile section page p align right a href readme top back to top a p contributing a name contributing a contributions issues and feature requests are welcome feel free to check the issues page issues p align right a href readme top back to top a p show your support a name support a if you like this project or find it useful interesting please make sure you give a since this will make it easily accesible for you too p align right a href readme top back to top a p acknowledgments a name acknowledgements a a rel license href http creativecommons org licenses by 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by 4 0 88x31 png a br this work is licensed under a a rel license href http creativecommons org licenses by murat korkmaz creative commons attribution 4 0 international license a p align right a href readme top back to top a p license license a name license a this project is mit mit md licensed note we recommend using the mit license https choosealicense com licenses mit you can set it up quickly by using templates available on github https docs github com en communities setting up your project for healthy contributions adding a license to a repository you can also use any other license https choosealicense com licenses if you wish p align right a href readme top back to top a p | rails react redux ruby | server |
datto | https api travis ci com kristiewirth datto svg branch master https travis ci com github kristiewirth datto https readthedocs org projects datto badge https datto readthedocs io en latest installation pip install datto overview datto is a package with various data tools to help in data analysis and data science work you can find the documentation here https datto readthedocs io en latest some examples of what you can do remove links from some text extract body of an email only no greeting or signature easily load save data from s3 run sql from python explore data check for mistyped data find correlated data assign a given user to an experimental condition create an html dropdown from a dataframe find the most common phrases by a category classify free text responses into any number of meaningful groups e g find survey themes make a simple python logger with default options take some data and test various machine learning models on it for detailed examples of how you can use it check out this juypter notebook datto examples ipynb other templates check out the templates folder templates for files that automate certain tasks but don t fit within the realm of a python package contributing create virtualenv bash pyenv virtualenv datto activate virtualenv bash pyenv activate datto install dependencies specified in pyproject toml file in virtualenv bash poetry install to add any new dependencies you need to poetry run bash poetry add package name if adding deleting a module you ll need to update these files datto init py docs source index rst docs source datto rst run the following to make sure all tests pass bash make test run the following to update all the docs bash make docs create a pr with your desired change s and request review from the code owner | ai |
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image-filter-starter-code | udagram image filtering microservice udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com udacity cloud developer tree master course 02 exercises udacity c2 frontend a basic ionic client web application which consumes the restapi backend covered in the course 2 the restapi backend https github com udacity cloud developer tree master course 02 exercises udacity c2 restapi a node express server which can be deployed to a cloud service covered in the course 3 the image filtering microservice https github com udacity cloud developer tree master course 02 project image filter starter code the final project for the course it is a node express application which runs a simple script to process images your assignment tasks setup node environment you ll need to create a new node server open a new terminal within the project directory and run 1 initialize a new project npm i 2 run the development server with npm run dev create a new endpoint in the server ts file the starter code has a task for you to complete an endpoint in src server ts which uses query parameter to download an image from a public url filter the image and return the result we ve included a few helper functions to handle some of these concepts and we re importing it for you at the top of the src server ts file typescript import filterimagefromurl deletelocalfiles from util util deploying your system follow the process described in the course to eb init a new application and eb create a new environment to deploy your image filter service don t forget you can use eb deploy to push changes stand out optional refactor the course restapi if you re feeling up to it refactor the course restapi to make a request to your newly provisioned image server authentication prevent requests without valid authentication headers note if you choose to submit this make sure to add the token to the postman collection and export the postman collection file to your submission so we can review custom domain name the image filter can be found here http udagram image filter dev222222 us east 1 elasticbeanstalk com the repository containing the solved udagram image filter code can be found here https github com akpas image filter starter code add your own domain name and have it point to the running services try adding a subdomain name to point to the processing server note domain names are not included in aws free tier and will incur a cost | cloud |
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ubuntu-dev-tools | ubuntu development tools install script what is this i started this project because i was fed up of wasting hours or even days for configuring a new machine for web development after installing ubuntu the goal of this project is to provide a way to setup the development environment by launching only a single script usage there are several bash scripts organized by category languages these scripts install programming languages java python node and their related tools npm pip db these scripts install databases postgresql mysql and their related tools like management guis tools these scripts install development tools like docker aliases these scripts add useful aliases to bashrc like shortcuts for apt get commands extra these scripts install tools to customize the gui and behaviour of ubuntu core sh this script installs libraries required to compile run versioning code it s possible to install only the required software by running the specific script in this case core sh should be launched before or to install all by running sudo sh install all sh | front_end |
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metrostrap | metro bootstrap front end framework for web development | front_end |
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ICCV-2023-Papers | iccv 2023 papers awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome conference http img shields io badge iccv 2023 7395c5 svg https iccv2023 thecvf com version https img shields io badge version v0 0 0 rc0 github repo size https img shields io github repo size dmitryryumin iccv 2023 papers license mit https img shields io badge license mit green svg https github com dmitryryumin iccv 2023 papers blob main license contributions welcome https img shields io badge contributions welcome brightgreen svg style flat https github com dmitryryumin iccv 2023 papers blob main readme md github contributors https img shields io github contributors dmitryryumin iccv 2023 papers github commit activity branch https img shields io github commit activity t dmitryryumin iccv 2023 papers github closed issues https img shields io github issues closed dmitryryumin iccv 2023 papers github issues https img shields io github issues dmitryryumin iccv 2023 papers github closed pull requests https img shields io github issues pr closed dmitryryumin iccv 2023 papers github pull requests https img shields io github issues pr dmitryryumin iccv 2023 papers github last commit https img shields io github last commit dmitryryumin iccv 2023 papers github watchers https img shields io github watchers dmitryryumin iccv 2023 papers github forks https img shields io github forks dmitryryumin iccv 2023 papers github repo stars https img shields io github stars dmitryryumin iccv 2023 papers visitors https api visitorbadge io api combined path https 3a 2f 2fgithub com 2fdmitryryumin 2ficcv 2023 papers label visitors countcolor 23263759 style flat div style float left img src https geps dev progress 62 successcolor 006600 img src https cdn jsdelivr net gh dmitryryumin neweraai papers main images completed checkmark done svg width 25 div iccv 2023 papers explore a comprehensive collection of cutting edge research papers presented at iccv 2023 https iccv2023 thecvf com the premier computer vision conference keep up to date with the latest advances in computer vision and deep learning code implementations included star the repository for the development of visual intelligence p align center a href https iccv2023 thecvf com target blank img width 600 src https github com dmitryryumin iccv 2023 papers blob main images iccv2023 banner jpg alt iccv 2023 a p the online version of the iccv 2023 conference programme https iccv2023 thecvf com main conference program 107 php comprises a list of all accepted full papers their presentation order as well as the designated presentation times a href https github com dmitryryumin neweraai papers style float left img src https cdn jsdelivr net gh dmitryryumin neweraai papers main images arrow click cursor pointer png width 25 other collections of the best ai conferences a br br exclamation conference table will be up to date all the time table tr td strong conference strong td td colspan 1 align center strong year strong td tr tr td colspan 2 align center i computer vision cv i td tr tr td cvpr td td a href https github com dmitryryumin cvpr 2023 papers target blank 2023 a td tr tr td colspan 2 align center i speech sp i td tr tr td icassp td td a href https github com dmitryryumin icassp 2023 papers target blank 2023 a td tr tr td interspeech td td a href https github com dmitryryumin interspeech 2023 papers target blank 2023 a td tr table contributors a href https github com dmitryryumin iccv 2023 papers graphs contributors img src http contributors nn ci api repo dmitryryumin iccv 2023 papers a br br contributions to improve the completeness of this list are greatly appreciated if you come across any overlooked papers please feel free to create pull requests https github com dmitryryumin iccv 2023 papers pulls open issues https github com dmitryryumin iccv 2023 papers issues or contact me via email mailto neweraairesearch gmail com your participation is crucial to making this repository even better papers https openaccess thecvf com iccv2023 day all img src https cdn jsdelivr net gh dmitryryumin neweraai papers main images ai svg width 30 exclamation final paper links will be added post conference table thead tr th scope col section th th scope col papers th th scope col img src https cdn jsdelivr net gh dmitryryumin neweraai papers main images arxiv logo svg width 45 th th scope col img src https cdn jsdelivr net gh dmitryryumin neweraai papers main images github code developer svg width 27 th th scope col img src https cdn jsdelivr net gh dmitryryumin neweraai papers main images video svg width 27 th tr thead tbody tr td a href https github com dmitryryumin iccv 2023 papers blob main sections 3d from multi view and sensors md 3d from multi view and sensors a td td a href https github com dmitryryumin iccv 2023 papers blob main sections 3d from multi view and sensors md img src https 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dmitryryumin iccv 2023 papers blob main sections segmentation grouping and shape analysis md segmentation grouping and shape analysis a td td a href https github com dmitryryumin iccv 2023 papers blob main sections segmentation grouping and shape analysis md img src https img shields io badge 72 42ba16 alt papers a td td a href https github com dmitryryumin iccv 2023 papers blob main sections segmentation grouping and shape analysis md img src https img shields io badge 49 b31b1b alt preprints a td td a href https github com dmitryryumin iccv 2023 papers blob main sections segmentation grouping and shape analysis md img src https img shields io badge 37 1d7fbf alt open code a td td a href https github com dmitryryumin iccv 2023 papers blob main sections segmentation grouping and shape analysis md img src https img shields io badge 6 ff0000 alt videos a td tr tr td a href https github com dmitryryumin iccv 2023 papers blob main sections recognition categorization md recognition 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td tr tr td a href https github com dmitryryumin iccv 2023 papers blob main sections machine learning and dataset md machine learning and dataset a td tr tbody table star history p align center a href https star history com dmitryryumin iccv 2023 papers date target blank img width 500 src https api star history com svg repos dmitryryumin iccv 2023 papers type date alt star history chart a p | iccv iccv2023 3d-graphics 3d-reconstruction biometrics computer-vision datasets deep-learning explainable-ai face-recognition gesture-recognition image-processing image-synthesis pattern-recognition robotics video-synthesis multimodal-learning photogrammetry pose-estimation transfer-learning | ai |
Matterport | matterport3d matterport3d img teaser jpg the matterport3d v1 0 dataset contains data captured throughout 90 properties with a matterport pro camera this repository includes the raw data for the dataset plus derived data annotated data and scripts models for several scene understanding tasks visit the main website https niessner github io matterport for updates and to browse the data paper matterport3d learning from rgb d data in indoor environments https arxiv org abs 1709 06158 if you use the matterport3d data or code please cite article matterport3d title matterport3d learning from rgb d data in indoor environments author chang angel and dai angela and funkhouser thomas and halber maciej and niessner matthias and savva manolis and song shuran and zeng andy and zhang yinda journal international conference on 3d vision 3dv year 2017 data the dataset consists of several types of annotations color and depth images camera poses textured 3d meshes building floor plans and region annotations object instance semantic annotations for details see the data organization data organization md document to download the dataset you must indicate that you agree to the terms of use by signing the terms of use http kaldir vc in tum de matterport mp tos pdf agreement form and sending it to matterport3d googlegroups com mailto matterport3d googlegroups com we will then provide download access to the dataset benchmark tasks using the matterport3d data we present several benchmark tasks image keypoint matching view overlap prediction surface normal estimation region type classification and semantic voxel labeling see the tasks tasks directory for details tools we provide code for loading and viewing the data see the code code directory for details license the data is released under the matterport3d terms of use http kaldir vc in tum de matterport mp tos pdf and the code is released under the mit license | 3d-reconstruction semantic-scene-understanding | ai |
FaiRLLM | fairllm the code for is chatgpt fair for recommendation evaluating fairness in large language model recommendation here is our fairllm benchmark run run music sh or run movie sh to get the chatgpt response for recommend num in 20 do for sst in country neutral do echo sst python3 u music run py singer list music 10000 mtv music artists page 1 csv sst class sst recommend num recommend num save folder music top recommend num sst sst json path sst json json api key your api key done done sst is the sensitive attribute can be age country gender continent occupation race religion physics to be specific if the sst neutral this leads to neutral response and it is important for evaluating the fairness if you want to evaluate the fairness of chatgpt using the generated data from chatgpt you can use process ipynb before using process ipynb please check the following in the begining of process ipynb input the sst json path sst path xxx input the llm result path like movie result path xxx thank milos bejda for his excellent 10 000 mtv s top music artists dataset at https gist github com mbejda 9912f7a366c62c1f296c please kindly cite our paper if you use our code dataset article zhang2023chatgpt title is chatgpt fair for recommendation evaluating fairness in large language model recommendation author zhang jizhi and bao keqin and zhang yang and wang wenjie and feng fuli and he xiangnan journal arxiv preprint arxiv 2305 07609 year 2023 | ai |
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coursera-ml-py | coursera machine learning assignments in python author https img shields io badge author nsoojin red svg https www linkedin com in soojinro python https img shields io badge python 3 6 blue svg license https img shields io github license mashape apistatus svg contribution https img shields io badge contribution welcome brightgreen svg title image title image png about if you ve finished the amazing introductory machine learning on coursera by prof andrew ng you probably got familiar with octave matlab programming with this repo you can re implement them in python step by step visually checking your work along the way just as the course assignments how to start dependencies this project was coded in python 3 6 numpy matplotlib scipy scikit learn scikit image nltk installation the fastest and easiest way to install all these dependencies at once is to use anaconda https www continuum io downloads important note there are a couple of things to keep in mind before starting all column vectors from octave matlab are flattened into a simple 1 dimensional ndarray e g y s and thetas are no longer m x 1 matrix just a 1 d ndarray with m elements so in octave matlab matlab size theta 2 1 now it is python theta shape 2 numpy matrix is never used just plain ol numpy ndarray contents exercise 1 https github com nsoojin coursera ml py tree master machine learning ex1 linear regression linear regression with multiple variables exercise 2 https github com nsoojin coursera ml py tree master machine learning ex2 logistic regression logistic regression with regularization exercise 3 https github com nsoojin coursera ml py tree master machine learning ex3 multiclass classification neural networks prediction fuction exercise 4 https github com nsoojin coursera ml py tree master machine learning ex4 neural networks learning exercise 5 https github com nsoojin coursera ml py tree master machine learning ex5 regularized linear regression bias vs variance exercise 6 https github com nsoojin coursera ml py tree master machine learning ex6 support vector machines spam email classifier exercise 7 https github com nsoojin coursera ml py tree master machine learning ex7 k means clustering principal component analysis exercise 8 https github com nsoojin coursera ml py tree master machine learning ex8 anomaly detection recommender systems solutions you can check out my implementation of the assignments here https github com nsoojin coursera ml py sj i tried to vectorize all the solutions | andrew-ng-course andrew-ng-ml-course coursera-machine-learning andrew-ng-machine-learning numpy-exercises machine-learning-ex1 neural-network support-vector-machines principal-component-analysis logistic-regression anomaly-detection python-ml | ai |
ITDOCS | itdocs i information technology documentation system i h2 deskripsi h2 sebuah sistem yang melakukan proses pembuatan dan disposisi dokumentasi pengembangan sistem it h2 latar belakang h2 ol li kurang efisiensinya proses pembuatan dokumentasi it dikarenakan masih dilakukan secara manual by ms words li li dokumentasi sering tercecer di beberapa folder dan membutuhkan waktu dalam pencariannya li li kurang terinformasikan disposisi dokumentasi li ol h2 prinsip dasar h2 ol li efisiensi dalam pembuatan dokumentasi li li terarsipkan dengan rapi li li disposisi dokumen mudah untuk dilacak li ol h2 dasar hukum h2 kmk no 351 kmk 01 2011 tentang kebijakan dan standar siklus pengembangan sistem informasi di lingkungan kementerian keuangan br br kmk bisa diunduh a href http www setjen kemenkeu go id sites default files download pusintek kmkno351 2011 pdf target blank di sini a h2 pihak pihak terkait proses bisnis kmk no 351 kmk 01 2011 h2 ol li b pemilik proses bisnis b pihak yang memiliki kebutuhan akan sistem informasi untuk mendukung jalannya proses bisnis li li b pengembang sistem informasi b pihak yang melakukan tugas merancang mengembangkan melakukan i quality control i dan dukungan teknis pada sistem informasi tersebut li li b tim i quality assurance i b pihak yang melakukan pengendalian mutu atas sistem informasi yang dikembangkan anggota tim i quality assurance i tidak boleh merangkap sebagai anggota tim pengembang sistem informasi li li b pengguna b pihak yang memiliki hak akses dan penggunaan atas sistem informasi tersebut li ol h2 siklus pengembangan sistem informasi h2 ol li b proses analisa kebutuhan sistem informasi b ins merupakan tanggung jawab dari pemilik proses bisnis untuk dok 0 dan pengembang si untuk dok 1 dan 2 ins proses mengumpulkan dan menganalisa spesifikasi kebutuhan bisnis dan sistem informasi secara rinci br output dari proses ini antara lain ul li a href doku dok 0 20151106 dokumen kebutuhan pengguna docx target blank dok 0 dokumen kebutuhan pengguna a li li a href doku dok 1 20151106 dokumen analisis dan spesifikasi kebutuhan docx target blank dok 1 dokumen analisis dan spesifikasi kebutuhan a li li a href doku dok 2 20151106 dokumen perubahan analisis dan spesifikasi kebutuhan docx target blank dok 2 dokumen perubahan analisis dan spesifikasi kebutuhan a li ul li li b proses perancangan sistem informasi b ins merupakan tanggung jawab dari pengembang sistem informasi perancang ins proses penyusunan rancangan sistem informasi berdasarkan analisa kebutuhan sistem informasi dan hasilnya akan digunakan sebagai acuan dalam mengembangkan sistem informasi tersebut br output dari proses ini antara lain ul li a href doku dok 3 20151106 dokumen rancangan tingkat tinggi docx target blank dok 3 dokumen rancangan tingkat tinggi a li li a href doku dok 4 20151106 dokumen rancangan rinci docx target blank dok 4 dokumen rancangan rinci a li ul li li b proses pengembangan sistem informasi b ins merupakan tanggung jawab dari pengembang sistem informasi pengembang ins proses yang dilaksanakan untuk membangun sistem informasi sesuai dengan kebutuhan berdasarkan rancangan sistem informasi br output dari proses ini antara lain ul li a href doku dok 5 20151106 dokumen pengembangan sistem informasi docx target blank dok 5 dokumen pengembangan sistem informasi a li li a href doku dok 6 20151106 formulir permintaan perubahan dan persetujuan docx target blank dok 6 formulir permintaan perubahan dan persetujuan a li li a href doku dok 7 20151106 dokumen rencana dan skenario pengujian docx target blank dok 7 dokumen rencana dan skenario pengujian a li li a href doku dok 7 0 20151106 formulir permintaan pengendalian mutu aplikasi docx target blank dok 7 0 formulir permintaan pengendalian mutu aplikasi a li ul li li b proses pengujian sistem informasi b ins merupakan tanggung jawab dari pengembang sistem informasi i quality control i ins proses yang dilaksanakan untuk menguji sistem informasi yang dikembangkan sesuai dengan rencana dan skenario pengujian br output dari proses ini antara lain ul li a href doku dok 7 1 20151106 dokumen pengendalian mutu docx target blank dok 7 1 dokumen pengendalian mutu a li li a href doku dok 8 20151106 dokumen hasil pengujian oleh pengguna docx target blank dok 8 dokumen hasil pengujian oleh pengguna a li li a href doku dok 8 1 20151106 form pengujian oleh pengguna docx target blank dok 8 1 form pengujian oleh pengguna a li li a href doku dok 9 20151106 dokumen analisis hasil pengujian docx target blank dok 9 dokumen analisis hasil pengujian a li ul li li b proses implementasi sistem informasi b ins merupakan tanggung jawab dari pengembang sistem informasi dukungan teknis ins proses yang dilaksanakan untuk penerapan sistem informasi yang telah dikembangkan pada lingkungan operasional br output dari proses ini antara lain ul li a href doku dok 10 20151106 dokumen rencana implementasi docx target blank dok 10 dokumen rencana implementasi a li li a href doku dok 11 20151106 dokumen implementasi rilis si docx target blank dok 11 dokumen implementasi rilis si a li li a href doku dok 12 20151106 laporan pelaksanaan pelatihan docx target blank dok 12 laporan pelaksanaan pelatihan a li li a href doku dok 13 20151106 bast si docx target blank dok 13 bast si a li ul li li b proses tinjauan pasca implementasi sistem informasi b ins merupakan tanggung jawab dari pemilik proses bisnis ins proses yang dilaksanakan untuk melakukan evaluasi atas proses keseluruhan dari pengembangan sistem informasi br output dari proses ini antara lain ul li a href doku dok 14 20151106 dokumen laporan evaluasi pasca implementasi docx target blank dok 14 dokumen laporan evaluasi pasca implementasi a li li a href doku dok 15 20151106 dokumen tinjauan pasca implementasi docx target blank dok 15 tinjauan pasca implementasi a li ul li ol h2 diagram keseluruhan proses bisnis h2 img src image rancangan jpg alt diagram probis h2 pembagian user terkait kmk 351 h2 img src image user roles png alt roles differ | server |
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DL-workshop-series | dl workshop series code used for deep learning related workshops for machine learning tokyo mlt part i convolution operations implementation convkernels https github com machine learning tokyo dl workshop series blob master part 20i 20 20convolution 20operations convkernels ipynb convkernels colab notebook with simple examples of various kernels applied on an image using convolution operation convnets https github com machine learning tokyo dl workshop series blob master part 20i 20 20convolution 20operations convnets ipynb convnets colab notebook with functions for constructing keras models models 1 alexnet 2 vgg 3 inception 4 mobilenet 5 shufflenet 6 resnet 7 densenet 8 xception 9 unet 10 squeezenet 11 yolo 12 refinenet slides link to the presentation https drive google com open id 1sxztx3e9m3g0birlh6sxaqvoeodjvjtrzhoixa5b rm cheat sheet alt text https github com machine learning tokyo dl workshop series blob master part 20i 20 20convolution 20operations convops cheatsheet jpg raw true cheat sheet conv operations video series cnn architectures including implementation youtube playlist https github com machine learning tokyo dl workshop series blob master part 20i 20 20convolution 20operations cnn architectures png https www youtube com playlist list plapdeey26uxywkvfcy0tmroqorsstfyq3 part ii learning in deep networks youtube playlist https github com machine learning tokyo dl workshop series blob master part 20ii 20 20learning 20in 20deep 20networks dl series png https www youtube com playlist list plapdeey26uxxvlzz485w61w4lgo0luzfg lerning in deep networks video series | deep-learning workshop convolutional-neural-networks cnn-keras keras | ai |
AESD | aesd code for psoc 4200 using the kit kit cypress http www cypress com documentation development kitsboards cy8ckit 044 psoc 4 m series pioneer kit for more details the following course could be followed mastering cypress psoc an embedded system design perspective https www udemy com mastering cypress psoc an embedded system design perspective currently contains the following projects clock communication interrupts power modes watchdog timer architecture technical reference manual https kartikey0303 github io pdf arch trm pdf register technical reference manual http kartikey0303 github io pdf register trm pdf | embedded psoc | os |
polymer-starter-kit-old | polymer starter kit join the chat at https gitter im startpolymer polymer starter kit https badges gitter im join 20chat svg https gitter im startpolymer polymer starter kit utm source badge utm medium badge utm campaign pr badge utm content badge polymer starter kit is a boilerplate for web development using web components and modern tools keeping up to date with web starter kit https github com google web starter kit html5 boilerplate https github com h5bp html5 boilerplate polymer generator https github com yeoman generator polymer and gulp generator https github com yeoman generator gulp webapp following the 10 commandments of modern web application https gist github com josefjezek 8020bd8f02c4992e7d7d sparkles demo http polymer starter kit startpolymer org sparkles features clean index html https github com startpolymer polymer starter kit blob master app index html gulp tasks https github com startpolymer polymer starter kit tree master gulp tasks per file using polymer demo https github com startpolymer polymer demo element with polymer theme https github com startpolymer polymer theme based on bem methodology http getbem com sass http sass lang com css preprocessor with ruby https www ruby lang org psk need css preprocessor for variables http sass guidelin es variables loops http sass guidelin es loops mixins http sass guidelin es mixins and other features libsass http libsass org is a c c port of the sass engine replace ruby sass with libsass https github com startpolymer polymer starter kit issues 2 issue scss have css like syntax check out the styles https github com startpolymer polymer starter kit tree master app styles dir ready to use any template engine how to add any template engine https github com startpolymer polymer starter kit wiki how to add any template engine for any developers autoprefixer https github com postcss autoprefixer for css asset revisioning https github com smysnk gulp rev all for css html and js by appending content hash to their filenames compress text files with pako https github com jameswyse gulp pako for avoiding the overhead of on the fly compression on server pagespeed insights https developers google com speed docs insights about for performance tuning built in preview server with browsersync http www browsersync io automagically wire up dependencies installed with bower http bower io vulcanize with content security policy https github com polymer vulcanize content security policy web component tester https github com polymer web component tester support quick deploy to cdn http en wikipedia org wiki content delivery network hosting github pages https pages github com more info https github com blog 1715 faster more awesome github pages installation tools on ubuntu sh add ruby repository sudo add apt repository y ppa brightbox ruby ng script to install nodesource repository curl sl https deb nodesource com setup sudo bash install git node js and ruby sudo apt get install y git nodejs ruby2 2 install bower gulp and npm sudo npm install g bower gulp npm install sass sudo gem install sass atom https atom io is great editor for web development you can use atom on ubuntu https gist github com josefjezek 6d7386cb7011cc8f5d37 script for other os you can use ubuntu vm image http www osboxes org ubuntu or google search wink usage fork https github com startpolymer polymer starter kit fork this repository syncing a fork https help github com articles syncing a fork of a repository to keep it up to date with the upstream repository or clone this repository to separate branch psk sh git clone https github com startpolymer polymer starter kit git my repo name cd my repo name git branch m psk git checkout b master git remote rename origin psk git remote add origin https github com user my repo name git git push u origin master how to use git https gist github com josefjezek 775e54583ef319c8c641 install dependencies sh bower install npm install check out the variables gulp variables are in the file gulp psk config js https github com startpolymer polymer starter kit blob master gulp psk config js serve to local and external url http localhost 9000 and http your ip 9000 sh gulp serve build and serve the output from the dist build sh gulp serve dist build the app sh gulp build a polymer element check out the file structure https github com startpolymer polymer demo tree develop app elements of polymer demo element sh gulp build el explain psk prefix psk prefix in file names is for git merging with psk branch without conflicts deploy tada deploy to github pages first you need to be sure you have a gh pages branch if you don t have one you can do the following sh git checkout orphan gh pages git rm rf touch readme md git add readme md git commit m init gh pages git push set upstream origin gh pages git checkout master sh gulp deploy gh tools pagespeed insights sh gulp pagespeed extending use a recipes https github com yeoman generator gulp webapp blob master docs recipes readme md for integrating other popular technologies like coffeescript or this a recipes https github com gulpjs gulp tree master docs recipes web component tester https github com polymer web component tester sh bower install web component tester save dev npm install web component tester save dev contributing 1 1 fork it 2 create your feature branch git checkout b my new feature 3 make your changes 4 run the tests adding new ones for your own code if necessary 5 commit your changes git commit am added some feature 6 push to the branch git push origin my new feature 7 create new pull request mit license https github com startpolymer polymer starter kit blob master license copyright c 2015 start polymer http startpolymer org http startpolymer org | front_end |
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srinib | srinib srinivas boddula s aws devops cloud design engineering | cloud |
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arcus.webapi | arcus web api build status https dev azure com codit arcus apis build status commit 20builds ci 20 20arcus webapi branchname main https dev azure com codit arcus build latest definitionid 733 branchname main nuget badge https buildstats info nuget arcus webapi security includeprereleases true https www nuget org packages arcus webapi security codecov https codecov io gh arcus azure arcus webapi branch main graph badge svg token 65p1ycxnxp https codecov io gh arcus azure arcus webapi web api development in a breeze arcus https raw githubusercontent com arcus azure arcus master media arcus png documentation all documentation can be found on here https webapi arcus azure net customers are you an arcus user let us know and get listed https bit ly become a listed arcus user license information this is licensed under the mit license mit which means that you can use copy modify merge publish distribute sublicense and or sell copies of the web application but you always need to state that codit is the original author of this web application read the full license here https github com arcus azure arcus webapi blob master license | azure web-apps app-service asp-net-core openapi | front_end |
llm-apex-agents | llm apex agents run large language model gpt agents in salesforce apex img width 1506 alt screen recording 2023 04 18 at 4 09 13 pm mov src https user images githubusercontent com 5217568 232917551 a0e53a4a 65a7 4b8b ad74 0f9860dce1cc png watch full demo here https www youtube com watch v zlp33ssxufu shipit what is an agent an agent is a technique for instilling the ability for an llm to reason and take action this approach is introduced by the react paper https arxiv org pdf 2210 03629 pdf reason act and used in popular libraries like langchain https github com hwchase17 langchain and auto gpt https github com torantulino auto gpt library terminology model llm gtp model currently only openai gpt completion chat models are supported prompt the communication protocol between the model system currently only react style prompts are supported agent a instance formed to solve a given objective tools the commands at the agents disposal action the act of invoking a tool getting started 1 install in a scratch org force source push or to a developer edition force mdapi deploy warning scratch developer orgs have a limit of 5 chained queueable jobs which will limit how much work the agent can do 2 assign yourself to the apex agent permission set sfdx force user permset assign n apex agents 3 open setup named credential external credential openai edit the permission set mapping to add a authentication parameters with name of api key and your openai api key as the value 4 optional open setup custom settings apex agent settings manage and add your serp api key https serpapi com dashboard extactorapi key https extractorapi com this is required to enable the internet search capabilities both have free tiers 5 navigate to the apex agents app from app launcher and run a task or start the agent using example code below exclamation warning this is experimental this library is not production ready and may never be the api usage can be relatively expensive if you let it run wild the code itself is likely to undergo significant breaking changes it is not yet optimized for performance and is not yet fully tested use at your own risk warning salesforce seems to have a bug with aborting queueable jobs with long running http callouts if you abort a job it will still run to completion and continue to schedule the following jobs example code java openaichatmodel chatllm new openaichatmodel chatllm model gpt 3 5 turbo chatllm model gpt 4 serpapikey c mc serpapikey c getinstance userinfo getuserid map string iagenttool tools new map string iagenttool search internet new internetsearchagenttool mc api key c find records new soslsearchagenttool send notification new sentnotificationagenttool create records new createrecordagenttool get fields new getsobjectfieldsagenttool list custom objects new listsobjectagenttool execute soql new runsqlagenttool reactzeroshotchatprompt prompt new reactzeroshotchatprompt tools reactchatagent agent new reactchatagent find 3 accounts with missing phone numbers and try to populate them from the internet prompt chatllm agent maxinvocations 15 agentqueueable manager new agentqueueable agent manager startagent note for best results it s recommended that you use openaichatmodel with gpt 4 reactzeroshotchatpromptmanager and only include the minimum number of tools you need hammer and wrench tools native sf tools x search for records x send custom notification x get sobject fields x list sobjects x write soql query x execute soql query x insert record x update record x get current user x draft send email post to chatter merge records create file attachment search kb create platform event approvals launch flow delete flows trollface read apex class write apex class run apex trollface run tests internet api tools x internet search limited x extract info from web page calculate wolfram alpha stonks https github com public apis public apis finance company lookup https api orb intelligence com docs news https github com public apis public apis news link preview linkpreview microlink https microlink io gpt tools batch summarize text categorization sentiment analysis load save memory other user feedback x create run other agents limited writing custom tools creating a custom tool is easy just create a class that implements the iagenttool interface and add it to the map of tools when you create the prompt java public class greetingagenttool implements iagenttool public string getdescription return get a greeting public map string string getparameters return new map string string name the name to greet public string execute map string string args if string isempty args get name throw an error with instructions so the agent can self correct throw new agent actionruntimeexception missing required parameter name return hello args get name tips you can return json or yaml using json serialize make your input as forgiving as possible and always return helpful error messages back if the input is invalid custom prompts you can write your own react style prompts by implementing prompt ireact logging currently there is some primitive logging place to an object call agent log c agent steps fire agent event e immediate platform events which result in the log being updated in realtime other planned improvements ask for permission to run actions suspend reanimate agent support for few shot prompts support for long term memory add tests have agent write them use gpt 3 5 to reduce returned tokens to agent thought reasoning action result x refactor api tokens to use named credentials x improve observability lwc dashboard x lwc chat frontend first pass tested prompts white check mark search for accounts containing the word sample and send a notification to the user with name charlie jonas notifying them that they should remove the account white check mark write a soql query that returns all billing related fields for an account send me a notification with the results of the query white check mark get the weather tomorrow in lander wyoming send a notification to charlie jonas letting him know how to dress white check mark research 3 companies that offer leading edge solutions for building api insert the new account with basic information about the business but only if the account does not already exist white check mark find out how many employees work at amazon and update the account white check mark query 3 accounts that do not have number of employees set update the account with the number of employees from the internet white check mark see if you can fill in any missing information on the amazon account send me a notification with the summary white check mark search for accounts containing the word sample create a task assigned to me with the subject remove sample account white check mark write a soql query to group invoice total by project name send me the results in a notification contributing the easiest way to contribute at this stage is to create new agenttools pr s welcome and experiment to see what this thing can do | apex chatgpt gpt-4 llm openai salesforce | ai |
supercharge.info | about source code for the web portion of supercharge info https supercharge info environment setup nodejs npm running locally localhost only npm install npm run start http localhost 9090 running an open development server choose port and comma separated list of allowed hostnames npm install npm run start env open env port 9191 env hosts mydevsite mydevsite dev supercharge info http mydevsite 9191 or http mydevsite dev supercharge info 9191 build without running npm run devbuild or npm run prodbuild forum https forum supercharge info c code | front_end |
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kilt-node | kilt node middot tests https gitlab com kiltprotocol kilt node badges develop pipeline svg https gitlab com kiltprotocol kilt node commits develop p align center img src maintain media kilt png p the kilt blockchain is the heart and soul behind kilt protocol it provides the immutable transaction ledger for the various kilt processes in the network the nodes use parity substrate as the underlying blockchain technology stack extended with our custom functionality for handling dids ctypes attestations and delegations the kilt developer documentation https dev kilt io | blockchain parachain self-sovereign-identity substrate | blockchain |
teachable-machine-v1 | teachable machine about teachable machine is an experiment that makes it easier for anyone to explore machine learning live in the browser no coding required learn more about the experiment and try it yourself on g co teachablemachine https g co teachablemachine the experiment is built using the tensorflow js https js tensorflow org library we have also released a boilerplate version of this project that can be used as a starting point for your own projects googlecreativelab teachable machine boilerplate https github com googlecreativelab teachable machine boilerplate development install dependencies by running similar to npm install yarn build project yarn build start local server by running yarn run watch code styles there s a pre commit hook set up that will prevent commits when there are errors run yarn eslint for es6 errors warnings run yarn stylint for stylus errors warnings to run https locally https is required to get camera permissions to work when not working with localhost 1 generate keys openssl genrsa out server key 2048 openssl req new x509 sha256 key server key out server cer days 365 subj cn your ip 2 use yarn run watch https 3 go to https your ip 3000 then accept the insecure privacy notice and proceed credit this is not an official google product but an experiment that was a collaborative effort by friends from st j http stoj io use all five https useallfive com and creative lab and pair https ai google pair teams at google | teachable-machine machine-learning | ai |
iOSAppReverseEngineering | as a 5 year n00b this is my gift to the jailbreak community enjoy ios app reverse engineering is the world s 1st book of very detailed ios app reverse engineering skills targeting 4 kinds of readers ios enthusiasts senior ios developers who have good command of app development and have the desire to understand ios better architects during the process of reverse engineering they can learn architectures of those excellent apps so that they can improve their ability of architecture design reverse engineers in other systems who re also interested in ios the book consists of 4 parts i e concepts tools theories and practices the book follows an abstraction concrete abstraction concrete structure starting from basic concepts like ios filesystem hierarchy and ios file types that apple didn t expose to app developers but ios jailbreak researchers should know then goes through the most commonly used tools like class dump theos cycript reveal ida and lldb to introduce what to do in ios reverse engineering after that ios reverse engineering theories based on objective c and arm assembly are explained in a methodological way pointing out the core of this book last but not least 4 originally elaborated practices are there to cover all previous contents of the book and give you the most intuitive perception of ios reverse engineering happy hacking for more info please visit our official forum http bbs iosre com and follow iosappre http twitter com iosappre thank you if you find the book worth buying after reading through it how about taking a look at here http www lulu com shop zishe sha ios app reverse engineering ebook product 22147315 html and buying one | os |
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DDWA | https makshladki github io ddwa index html 1 ecmascript 5 javascript event loop https makshladki github io ddwa lecture ecmascript 5 index html https makshladki github io ddwa task ecmascript 5 ecmascript 5 pdf 2 ecmascript 6 ecmascript 6 promises promise then catch all race resolve reject map set https makshladki github io ddwa lecture ecmascript 6 index html https makshladki github io ddwa task ecmascript 6 ecmascript 6 pdf 3 ecmascript 7 ecmascript 8 ecmascript object atomics https makshladki github io ddwa lecture ecmascript 8 index html https makshladki github io ddwa task ecmascript 8 ecmascript 8 pdf 4 gulp webpack gulp webpack c browser sync https makshladki github io ddwa lecture task management index html https makshladki github io ddwa task task management task management pdf 5 jquery css sizzle dom ajax restful api https makshladki github io ddwa lecture jquery index html https makshladki github io ddwa task jquery jquery pdf | javascript es6 gulp webpack jquery css less sass promise npm es5 es7 es8 | front_end |
govuk-design-system-dotnet | gov uk design system in c asp net core this is a collection of gov uk design system https design system service gov uk components build in c asp net core it s the c equivalent of the gov uk prototyping kit https govuk prototype kit herokuapp com docs nunjucks templates warning work in progress warning this is very much a work in progress only a subset of the components have been built and there is the possibility of breaking changes as we make bug fixes add extra features please feel free to use this in your own project you can see our contributing guide here contributing md if you do please let us know so we can avoid making changes that will cause problems for you email dan corder dan corder softwire com mailto dan corder softwire com breaking change 13 05 22 as of the 13 05 22 the validation used by this library has changed so that it is more in line with the standard asp net validation the main impacts are view models do not need to inherit from govukviewmodel in controllers instead of viewmodel hasanyerrors you can use the more standard modelstate isvalid you should be able to use any of the built in asp net validation attributes and have their error messages appear in the govukerrorsummary if you need to use fix the old version of the library it is now on the pre 2022 05 13 branch how to use this library can be used in two ways view components only view components validation error message generation these features are interoperable and you can use as much or as little of them as you like in your project it s not necessary to migrate totally to using this library view components e g for a gov uk button on the design system website https design system service gov uk components button the nunjucks code would look like this javascript from govuk components button macro njk import govukbutton govukbutton text save and continue the c code would look like this csharp using govukdesignsystem using govukdesignsystem govukdesignsystemcomponents html govukbutton new buttonviewmodel text save and continue e g for a text input https design system service gov uk components text input the c code would be csharp html govuktextinput new textinputviewmodel label new labelviewmodel text your email address name emailaddress inputmode email view components validation error message generation this library includes a number of validation attributes that can be used alongside the built in mvc validation attributes the govukerrorsummary will display any errors in the model state for property types where a badly formed value may make it impossible to bind the submitted value to the model this library provides custom model binders that will generate binding failure messages that match the govuk design system suggested error messages here s an example of how to get automatic error message generation for a mandatory integer field in your application s startup class add the following to the configureservices method csharp services addcontrollerswithviews options options modelmetadatadetailsproviders add new govukdatabindingerrortextprovider on your view model use a nullable integer int property add the modelbinder attribute with typeof govukmandatoryintbinder add the govukdatabindinginterrortext attribute to specify the text to use within the error messages csharp public class myviewmodel govukviewmodel modelbinder typeof govukmandatoryintbinder govukdatabindinginterrortext errormessageifmissing enter the number of children you have nameatstartofsentence number of children public int numberofchildren on the view csharp using govukdesignsystem using govukdesignsystem govukdesignsystemcomponents model myviewmodel html govukerrorsummary viewdata modelstate html govuktextinputfor m m numberofchildren labeloptions new labelviewmodel text how many children do you have on the controller check modelstate isvalid as normal csharp httppost url to action public iactionresult actionname myviewmodel viewmodel return the user back to the page if there s an error if modelstate isvalid return view viewname viewmodel packaging currently this project is not published on nuget to manually create a nuget package from the code ensure you have the dotnet cli tool installed if dotnet version works then you should be fine otherwise in stall the net core sdk from https www microsoft com net download change directory to govukdesignsystem run dotnet pack p packageversion your version here c release o development pre requisites net 3 1 https dotnet microsoft com en us download dotnet 3 1 winmerge https winmerge org project structure the project contains 3 general folders govukdesignsystem where all features such as components and attributes are located govukdesignsystem unittests where tests regarding functionality should be added govukdesignsystem snapshottests where tests regarding how html components are rendered should be added snapshot tests we use approval tests https approvaltests com to ensure html components are rendered properly after writing a test and running it a winmerge window will open with the test result on the left and the expected value on the right edit the file to the right to represent what the test outcome should be in the case of newly added components for example copying the contents of the left file and save the changes | os |
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azure-iot-developer-kit | archived this repository is no longer maintained please see http aka ms iot devkit http aka ms iot devkit for current documentation on using the az3166 board with azure rtos microsoft azure iot developer kit visit the project overview page to get started https microsoft github io azure iot developer kit https microsoft github io azure iot developer kit repository structure docs https github com microsoft azure iot developer kit tree master docs project home page with all other documents contributing this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments | azure azure-iot azure-functions iot iothub arduino vscode vscode-extension iot-central | server |
f3-qt | fight flash fraud qt this is a simple gui for f3 fight flash fraud https github com altramayor f3 no guarantee for data safety please do backups before operation licensed under gnu gplv3 the licence text can be found in file license license how to build and install see file install install | front_end |
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MachineLearning_Fall2015 | bus 41204 machine learning fall 2015 repository for bus 41204 machine learning http chicagoboothml github io machinelearning fall2015 | ai |
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Search-in-the-Chain | this is the project for searh in the chain welcome to read our paper https arxiv org abs 2304 14732 misc xu2023searchinthechain title search in the chain towards accurate credible and traceable large language models for knowledge intensive tasks author shicheng xu and liang pang and huawei shen and xueqi cheng and tat seng chua year 2023 eprint 2304 14732 archiveprefix arxiv primaryclass cs cl you can try to run our project by following the steps below running in different environments may encounter various problems we are still working hard to make it robust and bug free 1 index your corpus via colbert process your data into a format suitable for colbert indexing python colbert process hotpotqa wiki py indext your data python colbert index py run the service for retrieval python colbert server retrieval py 2 run the serive for verification and completion in information retrieval python server server py 3 construct chain of query and and interact with search service communicate with server server py an example on hotpotqa in the setting without ir python searchain without ir py an example on hotpotqa in the setting with ir python searchain w ir py | ai |
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interoperable-web-development | interoperable web development in these labs you will learn and work with the latest trends practices and technologies for web development this hands on lab includes the following labs lab 1 setup setup setting up your environment for the labs in this lab you will find the prerequisites and steps to help you set up your computer after completing the lab you will have a working environment ready for the other labs lab 2 edge mode and feature detection edge mode and feature detection working with the browser in this lab you will analyze an existing application and update it to comply with the best practices for feature detection additionally you will learn about edge mode and its perks lab 3 web development best practices best practices improving what we have in this lab you will take the application from the previous lab and introduce additional best practices to it you will improve the app styles and remove old fashioned plugins from it you will also be introduced to hand js https handjs codeplex com and will update the app to leverage its advantages lab 4 cross browser testing testing making sure everything works in this lab you will learn about two well known testing tools remote ie https remote modern ie and browser stack http www browserstack com and test the application with the latter additionally you will learn about graceful degradation for older browsers and update the app to incorporate fixes for them lab 5 mobile first with responsive design mobile first design taking over other platforms in this lab you will continue improving the application in this case to extend its reach to other platforms you will discover how the use of media queries and other elements can help you provide the users with a better experience for every platform | front_end |
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musichistory-boilerplate | music history this is the project that you will be working for your individual work throughout the entire front end course don t worry you ll be building lots of other applications but when you learn a new technique library or language you ll be cutting your teeth with it on music history i ve started you off with a very basic html document the index html file this file name is the default file that most any web server looks for in the directory in which is was started this is why you don t have to type in www google com index html if the file exists the web server sends it back to you if you just request the root url terminology root url simply means your domain name or ip address with no other documents or folders specified www google com is the root url of google s web site but www google com finance is not your first fork clone you re going to get a copy of this github repository downloaded a k a cloned to your machine using the git command here s how to do it 1 open your terminal window and make sure you are in your vagrant machine 2 if you are not already there go to the vagrant directory 3 look all the way up and to the right of the screen and you ll see a button with the word fork on it click that button 4 what you ve just done is taken a copy of my repository and all the code inside it and copied into your github account you can now do whatever you like to your fork of my repository and it won t affect mine at all 5 now on the right side of the screen just about half way down you will see the text ssh clone url with a text box beneath it under those click the link labeled https that will change the text above to https clone url 6 click the little clipboard icon to the right and it copies that url to your computer s clipboard 7 go back to your terminal window remember to use cmd tab keyboard shortcut on a mac or alt tab on windows 8 type in git clone and then paste the url after that text you should see git clone https github com your account name here musichistory git 1 hit your enter key and git will do two things first it creates a musichistory sub directory under vagrant and then downloads all the code into that directory 1 now ls musichistory 1 you will see the lonely index html file sitting in there congratulations you ve just cloned your first github repository now here s your assignment individual assignment you will be building the basic structure of your music history application in html and making it look good with the skills you learned in css visit the music history mockup https moqups com chortlehoort 1e8ljx7r that i created you will be recreating that document in your own html file criteria 1 create five options for the artist select element of any artist that you enjoy 1 create at least five options for the album select element one or more album for each artist 1 the links in the navigation bar don t need to link to anything just yet you can use a href view music a for now 1 pick your four favorite songs from the artists you have chosen and use the information for each in the list that s on the right hand side you can use h1 tags div tags section tags whatever you like completing once you are done make sure you add your files to git make a commit and then push your new code up to github with the following command git push origin master | front_end |
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supervisely | h1 align center a href https supervise ly img alt supervisely title supervisely src https i imgur com b276ems png a h1 h3 align center a href https supervise ly computer vision platform a a href https ecosystem supervise ly open ecosystem of apps a a href https developer supervise ly sdk for python a h3 p align center a href https pypi org project supervisely target blank img src https static pepy tech personalized badge supervisely period total units international system left color grey right color blue left text pip 20installs alt package version a a href https hub docker com r supervisely agent tags target blank img alt docker pulls src https img shields io docker pulls supervisely agent label docker 20pulls 20 20supervisely 2fagent a br a href https pypi org project supervisely target blank img alt pypi python version src https img shields io pypi pyversions supervisely color 4ec528 a a href https supervise ly slack target blank img src https img shields io badge slack chat green svg logo slack color 4ec528 alt slack a a href https pypi org project supervisely target blank img src https img shields io pypi v supervisely color 4ec528 label pypi 20package alt package version a a href https developer supervise ly target blank img src https readthedocs org projects supervisely badge version stable color 4ec528 a p website https supervise ly https supervise ly supervisely ecosystem https ecosystem supervise ly https ecosystem supervise ly dev documentation https developer supervisely com https developer supervisely com source code of sdk for python https github com supervisely supervisely https github com supervisely supervisely supervisely ecosystem on github https github com supervisely ecosystem https github com supervisely ecosystem complete video course on youtube what is supervisely https supervisely com what is supervisely 0 table of contents introduction introduction supervisely platform supervisely platform supervisely ecosystem supervisely ecosystem development development what developers can do what developers can do level 1 http rest api level 1 http rest api level 2 python scripts for automation and integration level 2 python scripts for automation and integration level 3 headless apps without ui level 3 headless apps without ui level 4 apps with interactive uis level 4 apps with interactive uis level 5 apps with ui integrated into labeling tools level 5 apps with ui integrated into labeling tools principles principles main features main features start in a minute start in a minute magically simple api magically simple api customization is everywhere customization is everywhere interactive gui is a game changer interactive gui is a game changer develop fast with ready ui widgets develop fast with ready ui widgets convenient debugging convenient debugging apps can be both private and public apps can be both private and public single click deployment single click deployment reliable versioning releases and branches reliable versioning releases and branches supports both github and gitlab supports both github and gitlab app is just a web server use any technology you love app is just a web server use any technology you love built in cloud development environment coming soon built in cloud development environment coming soon trusted by fortune 500 used by 65 000 researchers developers and companies worldwide trusted by fortune 500 used by 65 000 researchers developers and companies worldwide community community have an idea or ask for help have an idea or ask for help contribution contribution partnership partnership cite this project cite this project introduction every company wants to be sure that its current and future ai tasks are solvable the main issue with most solutions on the market is that they build as products it s a black box developing by some company you don t really have an impact on as soon as your requirements go beyond basic features offered and you want to customize your experience add something that is not in line with the software owner development plans or won t benefit other customers you re out of luck that is why supervisely is building a platform instead of a product supervisely platform https supervise ly a href https supervise ly img src https user images githubusercontent com 73014155 178843741 996aff24 7ceb 4e3e 88fe 1c19ccd9a757 png style max width 100 alt supervisely platform a you can think of supervisely https supervise ly as an operating system available via web browser to help you solve computer vision tasks the idea is to unify all the relevant tools within a single ecosystem https ecosystem supervise ly of apps tools ui widgets and services that may be needed to make the ai development process as smooth and fast as possible more concretely supervisely includes the following functionality data labeling for images videos 3d point cloud and volumetric medical images dicom data visualization and quality control state of the art deep learning models for segmentation detection classification and other tasks interactive tools for model performance analysis specialized deep learning models to speed up data labeling aka ai assisted labeling synthetic data generation tools instruments to make it easier to collaborate for data scientists data labelers domain experts and software engineers supervisely ecosystem https ecosystem supervise ly a href https ecosystem supervise ly img src https user images githubusercontent com 73014155 178843764 a92b7ad4 0cce 40ce b849 17b49c1e1ad3 png style max width 100 alt supervisely platform a the simplicity of creating supervisely apps has already led to the development of hundreds of applications https ecosystem supervise ly ready to be run within a single click in a web browser and get the job done label your data perform quality assurance inspect every aspect of your data collaborate easily train and apply state of the art neural networks integrate custom models automate routine tasks and more like in a real appstore there should be an app for everything development https developer supervise ly supervisely provides the foundation for integration customization development and running computer vision applications to address your custom tasks just like in os like windows or macos what developers can do there are different levels of integration customization and automation 1 http rest api level 1 http rest api 2 python scripts for automation and integration level 2 python scripts for automation and integration 3 headless apps without ui level 3 headless apps without ui 4 apps with interactive uis level 4 apps with interactive uis 5 apps with uis integrated into labeling tools level 5 apps with ui integrated into labeling tools level 1 http rest api supervisely has a rich http rest api https api docs supervise ly that covers basically every action you can do manually you can use any programming language and any development environment to extend and customize your supervisely experience for python developers we recommend using our python sdk https supervisely readthedocs io en latest sdk packages html because it wraps up all api methods and can save you a lot of time with built in error handling network re connection response validation request pagination and so on details summary curl example summary there s no easier way to kick the tires than through curl http curl haxx se if you are using an alternative client note that you are required to send a valid header in your request example bash curl h x api key your token here https app supervise ly public api v3 projects list as you can see url starts with https app supervise ly it is for community edition for enterprise edition you have to use your custom server address details level 2 python scripts for automation and integration supervisely sdk for python https supervisely readthedocs io en latest sdk packages html is specially designed to speed up development reduce boilerplate and lets you do anything in a few lines of python code with supervisely annotatation json format communicate with the platform import and export data manage members upload predictions from your models etc details summary python sdk example summary look how it is simple to communicate with the platform from your python script python import supervisely as sly authenticate with your personal api token api sly api from env create project and dataset project api project create workspace id 123 name demo project dataset api dataset create project id dataset 01 upload data image info api image upload path dataset id img png users max img png api annotation upload path image info id users max ann json download data img api image download np image info id ann api annotation download json image info id details level 3 headless apps without ui create python apps to automate routine and repetitive tasks share them within your organization and provide an easy way to use them for end users without coding background headless apps are just python scripts that can be run from a context menu run app from context menu https user images githubusercontent com 73014155 178843779 2af6fff3 ce28 4278 a57f f6577615b849 png it is simple and suitable for the most basic tasks and use cases for example import and export in custom format example1 https ecosystem supervise ly apps import images groups example2 https ecosystem supervise ly apps export as masks example3 https ecosystem supervise ly apps export to pascal voc example4 https ecosystem supervise ly apps render video labels to mp4 assets transformation example1 https ecosystem supervise ly apps rasterize objects on images example2 https ecosystem supervise ly apps resize images example3 https ecosystem supervise ly apps change video framerate example4 https ecosystem supervise ly apps convert ptc to ptc episodes users management example1 https ecosystem supervise ly apps invite users to team from csv example2 https ecosystem supervise ly apps create users from csv example3 https ecosystem supervise ly apps export activity as csv deploy special models for ai assisted labeling example1 https ecosystem supervise ly apps supervisely ecosystem 2fritm interactive segmentation 2fsupervisely example2 https ecosystem supervise ly apps supervisely ecosystem 2ftrans t 2fsupervisely 2fserve example3 https ecosystem supervise ly apps volume interpolation level 4 apps with interactive uis interactive interfaces and visualizations are the keys to building and improving ai solutions from custom data labeling to model training such apps open up opportunities to customize supervisely platform to any type of task in computer vision implement data and models workflows that fit your organization s needs and even build vertical solutions for specific industries on top of it a href https ecosystem supervise ly apps dev smart tool batched img src https user images githubusercontent com 73014155 178845451 8350a6d7 f318 4f5b a9ee 4b871016e2e4 gif style max width 100 alt this interface is completely based on python in combination with easy to use supervisely ui widgets batched smarttool app for ai assisted object segmentations a here are several examples custom labeling interfaces with ai assistance for images https ecosystem supervise ly apps dev smart tool batched and videos https ecosystem supervise ly apps batched smart tool for videos interactive model performance analysis https ecosystem supervise ly apps semantic segmentation metrics dashboard interactive nn training dashboard https ecosystem supervise ly apps supervisely ecosystem 2fmmsegmentation 2ftrain data exploration https ecosystem supervise ly apps action recognition stats and visualization https ecosystem supervise ly apps objects thumbnails preview by class apps vertical solution https ecosystem supervise ly collections supervisely ecosystem 2fgl metric learning 2fsupervisely 2fretail collection for labeling products on shelves in retail inference interfaces in labeling tools https ecosystem supervise ly apps supervisely ecosystem 2fnn image labeling 2fannotation tool for images https ecosystem supervise ly apps supervisely ecosystem 2fnn image labeling 2fproject dataset videos https ecosystem supervise ly apps apply nn to videos project and point clouds https ecosystem supervise ly apps apply det3d to project dataset for model ensembles https ecosystem supervise ly apps apply det and cls models to project level 5 apps with ui integrated into labeling tools there is no single labeling tool that fits all tasks labeling tool has to be designed and customized for a specific task to make the job done in an efficient manner supervisely apps can be smoothly integrated into labeling tools to deliver amazing user experience including multi tenancy and annotation performance a href https ecosystem supervise ly apps supervisely ecosystem 252fgl metric learning 252fsupervisely 252flabeling tool img src https user images githubusercontent com 12828725 179206991 1c76f61d b88a 4a2b 9116 d87fb1ed9d0e png style max width 100 alt ai assisted retail labeling app is integrated into labeling tool and can communicate with it via web sockets a here are several examples apps designed for custom labeling workflows example1 https ecosystem supervise ly apps visual tagging example2 https ecosystem supervise ly apps review labels side by side nn inference is integrated for labeling automation and model predictions analysis example https ecosystem supervise ly apps supervisely ecosystem 2fnn image labeling 2fannotation tool industry specific labeling tool annotation of thousands of product types on shelves with ai assistance retail collection https ecosystem supervise ly collections supervisely ecosystem 2fgl metric learning 2fsupervisely 2fretail collection labeling app https ecosystem supervise ly apps ai assisted classification principles development for supervisely builds upon these five principles all in pure python and build on top of your favourites libraries opencv requests fastapi pytorch imgaug etc easy for python developers and data scientists to build and share apps with teammates and the ml community no front end experience is required build powerful and interactive web based gui apps using the comprehensive library of ready to use ui widgets and components easy to learn fast to code and ready for production sdk provides a simple and intuitive api by having complexity under the hood every action can be done just in a few lines of code you focus on your task supervisely will handle everything else interfaces databases permissions security cloud or self hosted deployment networking data storage and many more supervisely has solid testing documentation and support everything is customizable from labeling interfaces to neural networks the platform has to be customized and extended to perfectly fit your tasks and requirements not vice versa hundreds of examples cover every scenario and can be found in our ecosystem of apps https ecosystem supervise ly apps can be both open sourced or private all apps made by supervisely team are open sourced https github com supervisely ecosystem use them as examples just fork and modify the way you want at the same time customers and community users can still develop private apps to protect their intellectual property main features start in a minute start in a minute magically simple api magically simple api customization is everywhere customization is everywhere interactive gui is a game changer interactive gui is a game changer develop fast with ready ui widgets develop fast with ready ui widgets convenient debugging convenient debugging apps can be both private and public apps can be both private and public single click deployment single click deployment reliable versioning releases and branches reliable versioning releases and branches supports both github and gitlab supports both github and gitlab app is just a web server use any technology you love app is just a web server use any technology you love built in cloud development environment coming soon built in cloud development environment coming soon trusted by fortune 500 used by 65 000 researchers developers and companies worldwide trusted by fortune 500 used by 65 000 researchers developers and companies worldwide start in a minute supervisely s open source sdk and app framework are straightforward to get started with it s just a matter of pip install supervisely magically simple api supervisely sdk for python https supervisely readthedocs io en latest sdk packages html is simple intuitive and can save you hours reduce boilerplate and build custom integrations in a few lines of code it has never been so easy to communicate with the platform from python python authenticate with your personal api token api sly api from env create project and dataset project api project create workspace id 123 name demo project dataset api dataset create project id dataset 01 upload data image info api image upload path dataset id img png users max img png api annotation upload path image info id users max ann json download data img api image download np image info id ann api annotation download json image info id customization is everywhere customization is the only way to cover all tasks in computer vision supervisely allows to customizing everything from labeling interfaces and context menus to training dashboards and inference interfaces check out our ecosystem of apps https ecosystem supervise ly to find inspiration and examples for your next ml tool interactive gui is a game changer the majority of python programs are command line based while highly experienced programmers don t have problems with it other tech people and end users do this creates a digital divide a gui gap app with graphic user interface gui becomes more approachable and easy to use to a wider audience and finally some tasks are impossible to solve without a gui at all imagine how it will be great if all ml tools and repositories have an interactive gui with the run button it will take minutes to start working with a top deep learning framework instead of spending weeks running it on your data our ambitious goal is to make it possible a href https ecosystem supervise ly apps semantic segmentation metrics dashboard img src https user images githubusercontent com 73014155 178846370 ae86dd3c e08d 4df2 871b d342bf7ba370 gif style max width 100 alt semantic segmentation metrics app a develop fast with ready ui widgets hundreds of interactive ui widgets and components are ready for you just add to your program and populate with the data python devs don t need to have any front end experience in our developer portal you will find needed guides examples and tutorials we support the following ui widgets 1 widgets made by supervisely https ecosystem supervise ly docs grid gallery specifically for computer vision tasks like rendering galleries of images with annotations playing videos forward and backward with labels interactive confusion matrices tables charts 2 element widgets https element eleme io 1 4 en us component button vue 2 0 based component library 3 plotly https plotly com python graphing library for python 4 you can develop your own ui widgets example https github com supervisely ecosystem dev smart tool batched blob master static smarttool js supervisely team makes most of its apps publically available on github https github com supervisely ecosystem use them as examples for your future apps fork modify and copy paste code snippets convenient debugging supervisely is made by data scientists for data scientists we trying to lower barriers and make a friendly development environment especially we care about debugging as one of the most crucial steps even in complex scenarios like developing a gui app integrated into a labeling tool we keep it simple use breakpoints in your favorite ide to catch callbacks step through the program and see live updates without page reload as simple as that supervisely handles everything else websockets authentication redis rabitmq postgres watch the video below how we debug the app https ecosystem supervise ly apps supervisely ecosystem 2fnn image labeling 2fannotation tool that applies nn right inside the labeling interface a href https youtu be fonyl8yhobm img src https user images githubusercontent com 12828725 179207006 bcdd0922 21c1 4958 86e7 d532fbf7c974 png style max width 100 a apps can be both private and public all apps made by supervisely team are open source https github com supervisely ecosystem use them as examples find on github https github com supervisely ecosystem fork and modify them the way you want at the same time customers and community users can still develop private apps to protect their intellectual property a href https youtu be kyuc lzu tg img src https user images githubusercontent com 12828725 179207014 55659b39 0f58 42db 96e3 8063f1e6ad5d png style max width 100 a single click deployment supervisely app is a git repository just provide the link to your git repo supervisely will handle everything else now you can press run button in front of your app and start it on any computer with supervisely agent https youtu be adqqiyycqyk reliable versioning releases and branches users run your app on the latest stable release and you can develop and test new features in parallel just use git releases and branches supervisely automatically pull updates from git even if the new version of an app has a bug don t worry users can select and run the previous version in a click a href https youtu be ngohfm98r8k img src https user images githubusercontent com 12828725 179207015 d5f839a6 907b 4469 9f86 950ee348024e png style max width 100 a supports both github and gitlab since supervisely app is just a git repository we support public and private repos from the most popular hosting platforms in the world github and gitlab app is just a web server use any technology you love supervisely sdk for python provides the simplest way for python developers and data scientists to build interactive gui apps of any complexity python is a recommended language for developing supervisely apps but not the only one you can use any language or any technology you love any web server can be deployed on top of the platform for example even visual studio code for web https github com coder code server can be run as an app see video below built in cloud development environment coming soon in addition to the common way of development in your favorite ide on your local computer or laptop cloud development support will be integrated into supervisely and released soon to speed up development standardize dev environments and lower barriers for beginners how will it work just connect your computer to your supervisely instance and run ide app jupyterlab https jupyter org and visual studio code for web https github com coder code server to start coding in a minute we will provide a large number of template apps that cover the most popular use cases a href https youtu be pthjsdolhhk img src https user images githubusercontent com 73014155 178956713 0de05a39 3ecc 41b2 a46e 54d3a23d4e64 png style max width 100 a trusted by fortune 500 used by 65 000 researchers developers and companies worldwide https user images githubusercontent com 106374579 204510683 4aaa1e11 e934 4268 8365 f140028508d0 png supervisely helps companies and researchers all over the world to build their computer vision solutions in various industries from self driving and agriculture to medicine join our community edition https app supervise ly or request enterprise edition https supervise ly enterprise for your organization community join our constantly growing supervisely community https app supervise ly with more than 65k users have an idea or ask for help if you have any questions ideas or feedback please 1 suggest a feature or idea https ideas supervise ly or give a technical feedback https github com supervisely supervisely issues 2 join our slack https supervise ly slack 3 contact us https supervise ly contact us your feedback helps us a lot and we appreciate it contribution want to help us bring computer vision r d to the next level we encourage you to participate and speed up r d for thousands of researchers by building and expanding supervisely ecosystem with us integrating to supervisley and sharing your ml tools and research with the entire ml community partnership we are happy to expand and increase the value of supervisely ecosystem with additional technological partners researchers developers and value added resellers feel free to contact us https supervise ly contact us if you have ml service or product unique domain expertise vertical solution valuable repositories and tools that solve the task custom nn models and data let s discuss the ways of working together particularly if we have joint interests technologies and customers cite this project if you use this project in a research please cite it using the following bibtex misc supervisely title supervisely computer vision platform type computer vision tools author supervisely howpublished url https supervisely com url https supervisely com journal supervisely ecosystem publisher supervisely year 2023 month jul note visited on 2023 07 20 | ai neural-networks python deep-learning | ai |
delphimvcframework | delphimvcframework mentioned in awesome go https awesome re mentioned badge svg github all releases https img shields io github downloads danieleteti delphimvcframework total label downloads https img shields io badge stable dmvcframework 3 4 0 neon blue https img shields io badge beta dmvcframework 3 4 1 sodium beta red start doctoc generated toc please keep comment here to allow auto update don t edit this section instead re run doctoc to update table of contents what s delphimvcframework whats delphimvcframework support dmvcframework support dmvcframework delphimvcframework main features delphimvcframework main features install the latest stable version install the latest stable version book delphimvcframework the official guide book delphimvcframework the official guide book translations book translations how to partecipate to dmvcframework development and or tests how to partecipate to dmvcframework development andor tests sponsors sponsors what users say about dmvcframework what users say about dmvcframework what s new in delphimvcframework 3 4 0 neon stable version whats new in delphimvcframework 340 neon stable version older versions older versions what s new in dmvcframework 3 3 0 fluorine whats new in dmvcframework 330 fluorine what s new in dmvcframework 3 2 3 radium whats new in dmvcframework 323 radium bug fix in 3 2 3 radium bug fix in 323 radium what s new in delphimvcframework 3 2 2 nitrogen whats new in delphimvcframework 322 nitrogen bug fixes in 3 2 2 nitrogen bug fixes in 322 nitrogen breaking changes in 3 2 2 nitrogen breaking changes in 322 nitrogen what s new in delphimvcframework 3 2 1 carbon whats new in delphimvcframework 321 carbon improvements improvements what s new in 3 2 0 boron whats new in 320 boron breaking changes in 3 2 0 boron breaking changes in 320 boron bug fixes in 3 2 0 boron bug fixes in 320 boron what s new in 3 1 0 lithium whats new in 310 lithium what s new in 3 0 0 hydrogen whats new in 300 hydrogen what s new in 2 1 3 lithium whats new in 213 lithium what s new in 2 1 2 helium whats new in 212 helium what s new in 2 1 1 hydrogen whats new in 211 hydrogen roadmap roadmap trainings consultancy or custom development service trainings consultancy or custom development service samples and documentation samples and documentation getting started 5 minutes guide getting started 5 minutes guide delphimvcframework installation delphimvcframework installation sample controller sample controller how to create a dmvcframework servers container how to create a dmvcframework servers container rql introduction rql introduction rql as implemented by dmvcframework rql as implemented by dmvcframework dotenv syntax dotenv syntax links links end doctoc generated toc please keep comment here to allow auto update delphimvcframework logo docs dmvcframework logofacebook png start doctoc end doctoc what s delphimvcframework dmvcframework is a very popular delphi framework which provides an easy to use scalable flexible restful https en wikipedia org wiki representational state transfer json rpc https en wikipedia org wiki json rpc and activerecord https www martinfowler com eaacatalog activerecord html framework for delphi developers dmvcframework is the most popular delphi project on github and compiles for windows 32 and 64bit and linux 64bit dmvcframework services can be compiled as console application windows service linux daemon apache module windows and linux and iis isapi windows dmvcframework works with delphi 11 alexandria delphi 10 4 sydney delphi 10 3 rio delphi 10 2 tokyo delphi 10 1 berlin delphi 10 seattle support dmvcframework are you using dmvcframework do you want to say thanks a href https www patreon com bepatron u 72182967 data patreon widget type become patron button become a patron a delphimvcframework main features dmvcframwork is simple to use has a lot of examples https github com danieleteti delphimvcframework tree master samples is documented and there are a lot of tutorials https www youtube com results search query delphimvcframework available dmvcframework is very well documented and the book dmvcframework the official guide http www danieleteti it books is available to fastly get a solid knowledge available as e book and hardcopy project roadmap roadmap md is always public there are more than 40 samples to learn all the features and be proficient and productive commercially supported by bit time professionals http www bittimeprofessionals it training consultancy custom development etc restful rmm level 3 compliant json rpc 2 0 support with automatic objects remotization check sample https github com danieleteti delphimvcframework tree master samples jsonrpc with published objects dmvcframework mvcactiverecord allows an easy and fast database access stable and solid used by small mid big projects since 2010 very fast 2 x was pretty fast and now 3 x is 60 faster than the 2 x support group at https www facebook com groups delphimvcframework with more than 4900 active members can be used in load balanced environment wizard for the delphi ide it makes delphimvcframework even more simple to use optional session support json web token support jwt check sample https github com danieleteti delphimvcframework tree master samples jsonwebtoken extendable using middleware simple hooks to handle request response check sample https github com danieleteti delphimvcframework tree master samples middleware flexible yet simple to use authorization authentication framework based on industry standards http basic authentication jwt authentication custom authentication cors support controllers inheritance you can define your own base controller and inherit from it fancy url with parameter mappings specialized renders to generate text html json powerful and customizable mapper to serialize deserialize data can be packaged as stand alone server apache module xe6 or better and isapi dll integrated rest client works on linux delphi 10 2 tokyo or better completely unit tested more than 250 unit tests there is a sample for each functionality check the dmvcframework yourversion samples zip https github com danieleteti delphimvcframework releases server side generated pages using mustache for delphi https github com synopse dmustache or templatepro https github com danieleteti templatepro specific trainings are available email to professionals bittime it for a date and a place push notifications support using serversentevents https github com danieleteti delphimvcframework tree master samples serversentevents automatic documentation through system describeserver info driven by its huge community facebook group https www facebook com groups delphimvcframework semantic versioning to get a fast introduction to dmvcframework read the slides from itdevcon conference docs itdevcon 202013 20 20introduction 20to 20delphimvcframework pdf install the latest stable version if you are not involved in development or testing do not clone the repo use the github release the last stable version available here https github com danieleteti delphimvcframework releases latest just download latest release as a zip file and you are ok samples are available as separate zip file downloadable from the same page where you download the release book delphimvcframework the official guide the official guide for dmvcframework is available dmvcframework has a lot functionalities and can really help your business however many developers don t use it at its full potential why don t get more when is easily available the dmvcframework lead developer and project coordinator daniele teti wrote the official guide for this great framework delphimvcframework the official guide https raw githubusercontent com danieleteti delphimvcframework master docs logoproject dmvcframework the official guide very small png buy your copy https leanpub com delphimvcframework and improve your dmvcframework knowledge now dmvcframework the official guide is available as e book https leanpub com delphimvcframework and hardcopy https www lulu com en en shop daniele teti and jim mckeeth delphimvcframework the official guide hardcover product r26e8e html pick what you prefer while a huge work has been done by the author and the reviews to make the book and the examples well written complete and effective things can be always improved for any suggestions complains or requests there is the official github book project https github com danieleteti dmvcframeworktheofficialguide where you can fill an issue and get in touch directly with the author book translations given the success of dmvcframework in the delphi community the official dmvcframework guide has been translated also in the following languages brazilian portuguese https leanpub com delphimvcframework br translated by diego farisato spanish https leanpub com delphimvcframework es translated by jos davalos please if you use dmvcframework star this project in github it cost nothing to you but helps other developers to reference the code https raw githubusercontent com danieleteti delphimvcframework master docs starproject png how to partecipate to dmvcframework development and or tests only if you want to participate to the testing phase which usually contains brand new features but can sometimes be instable you can get the development version clonig this repo or downloading the master repository zip file https github com danieleteti delphimvcframework archive master zip take in mind that even if development version is usually very stable it isn t not ready for production utilization sponsors while dmvcframework is born from the head of daniele teti from bit time professionals it wouldn t what is now without the support and work of many people all around the world the following companies sponsored some specific part of dmvcframework so they wort a special mention gold sponsors company name logo bit time professionals https www bittimeprofessionals com docs sponsorlogos bittimeprofessionals png bit time software https www bittime it docs sponsorlogos bittimesoftware png silver sponsor company name logo centro software https www centrosoftware com docs sponsorlogos centrosoftware png delphi studio es http www delphistudio es docs sponsorlogos delphistudio png orion law https orionlaw com docs sponsorlogos orionlaw png vivaticket https www vivaticket com docs sponsorlogos vivaticket png what users say about dmvcframework our wishes are coming true one delphi programmer after a small dmvcframework demo for an it department of a very important national research institute i m still amazed by the delphimvcframework code and documentation thank you very much and i am amazed by your quick feedback benjamin yang https www linkedin com in benjamin yang 4b0609159 director of sqlgate https www sqlgate com dmvcframework and the entity utility are fantastic roberto dmvcframework is a great framework it s very intuitive fast easy to use actually there is nothing more to ask for samir wow to do that in j2ee it takes 2 days a training participant after a 5 minutes demo i m starting with the dmvcframework and i m finding it fantastic congratulations for the project rafael i m looking at dmvcframework project in it works great for my use case scenarios is much better than similar commercial product luka it s fantastic just define your entities and you are up and running in 5 minutes nothing comparable on the market marco the best framework for creating web servers with delphi it is very easy to create delphi servers and publish apis and rest resources congratulations to daniele teti and all the staff for the excellent work marcos n we started the process of migrating our systems to micro services and are loving the dmvcframework dmvcframework is definitely part of our lives right now e costa thank you for the great framework we are very happy with this andreas i managed to generate an api for my application thanks to this framework it is truly useful and efficient j urbani what s new in delphimvcframework 3 4 0 neon stable version deeper analisys of what s new in delphimvcframework 3 4 0 neone is available on daniele teti blog http www danieleteti it post delphimvcframework 3 4 0 neon added support for dotenv added msheap memory manager for win32 and win64 https github com rdp1974 delphimsheap fix issue 664 https github com danieleteti delphimvcframework issues 664 thanks to mpannier https github com mpannier fix issue 667 https github com danieleteti delphimvcframework issues 667 fix issue 680 https github com danieleteti delphimvcframework issues 680 fix issue 682 https github com danieleteti delphimvcframework issues 682 thanks to wuhao13 https github com wuhao13 fix wrong comparison in checks for ro rw pk fields in tmvcactiverecord fix wrong default initialization for jwt thanks to flavio basile wizard updated to be dotenv aware added htmx https htmx org server side support through unit samples htmx mvcframework htmx pas and the relative sample thanks to david moorhouse https github com fastbike this unit provides class helper for tmvcwebrequest and tmvcwebresponse classes to easily work with htmx if you want to use this unit just download the samples and add it to your project or put dmvchome samples htmx in your library path added load style methods to tmvcactiverecord more info https github com danieleteti delphimvcframework issues 675 tmvcactiverecord support factory style and load style methods when loads data from database using factory style methods available from the first version the result list is returned by the loader method as shown in this piece of code from the activerecord showcase sample delphi log rql query 2 crql2 lcustlist tmvcactiverecord selectrql tcustomer crql2 20 try log lcustlist count tostring record s found for lcustomer in lcustlist do begin log format 5s s s lcustomer code valueordefault lcustomer companyname valueordefault lcustomer city end finally lcustlist free end for some scenarios would be useful to have also load style methods where the list is filled by the loader method not instantiated internally delphi log rql query 2 crql2 lcustlist tobjectlist tcustomer create try lreccount tmvcactiverecord selectrql tcustomer crql2 20 lcustlist new in 3 4 0 neon log lreccount tostring record s found for lcustomer in lcustlist do begin log format 5s s s lcustomer code valueordefault lcustomer companyname valueordefault lcustomer city end finally lcustlist free end better error message in case of serialization of tarray tobject improved cors handling issue 679 https github com danieleteti delphimvcframework issues 679 thanks to david moorhouse https github com fastbike improved serialization of tobjectlist tdataset however objectdict is still the preferred way to serialize multiple datasets added static method for easier cloning of firedac dataset into tfdmemtable in the class emvcexception the property httperrorcode has been renamed in httpstatuscode functional actions in addition to the classic procedure based actions now it s possibile to use functions as actions the result variable is automatically rendered and if it is an object its memory is freed pascal type mvcnamecase nccamelcase tpersonrec record firstname lastname string age integer class function create tpersonrec static end mvcnamecase nccamelcase tperson class private fage integer ffirstname flastname string public property firstname string read ffirstname write ffirstname property lastname string read flastname write flastname property age integer read fage write fage end mvcpath api tmycontroller class tmvccontroller public actions returning a simple type mvcpath sumsasinteger a b function getsum const a b integer integer mvcpath sumsasfloat a b function getsumasfloat const a b extended extended actions returning records mvcpath records single function getsinglerecord tpersonrec mvcpath records multiple function getmultiplerecords tarray tpersonrec actions returning objects mvcpath objects single function getsingleobject tperson mvcpath objects multiple function getmultipleobjects tobjectlist tperson actions returning datasets mvcpath datasets single function getsingledataset tdataset mvcpath datasets multiple function getmultipledataset tenumerable tdataset mvcpath datasets multiple2 function getmultipledataset2 imvcobjectdictionary customize response headers mvcpath headers function getwithcustomheaders tobjectlist tperson end check sample function actions showcase dproj for more info improved tmvcresponse type to better suits the new functional actions tmvcresponse can be used with message based responses and also with data based responses with single object with a list of objects or with a dictionary of objects more info here http www danieleteti it post delphimvcframework 3 4 0 neon removed statuscode reasonstring and all the field with a default value from exception s json rendering all the high level rendering methods will emit standard reasonstring before json apperrorcode 0 statuscode 404 reasonstring not found classname emvcexception data null detailedmessage items message emvcexception not found now json classname emvcexception message not found new sql and rql named queries support for tmvcactiverecord here s all the new methods available for named queries delphi class function selectbynamedquery t tmvcactiverecord constructor const queryname string const params array of variant const paramtypes array of tfieldtype const options tmvcactiverecordloadoptions tobjectlist t overload class function selectbynamedquery const mvcactiverecordclass tmvcactiverecordclass const queryname string const params array of variant const paramtypes array of tfieldtype const options tmvcactiverecordloadoptions tmvcactiverecordlist overload class function selectrqlbynamedquery t constructor tmvcactiverecord const queryname string const params array of const const maxrecordcount integer tobjectlist t overload class function selectrqlbynamedquery const mvcactiverecordclass tmvcactiverecordclass const queryname string const params array of const const maxrecordcount integer tmvcactiverecordlist overload class function deleterqlbynamedquery t tmvcactiverecord constructor const queryname string const params array of const int64 class function countrqlbynamedquery t tmvcactiverecord constructor const queryname string const params array of const int64 mvcnamedsqlquery allows to define a named query which is well a sql query with a name then such query can be used by the method selectbynamedquery t or selectbynamedquery moreover in the attribute it is possible to define on which backend engine that query is usable in this way you can define optimized query for each supported dmbs you need more info here http www danieleteti it post delphimvcframework 3 4 0 neon older versions what s new in dmvcframework 3 3 0 fluorine support for delphi 11 3 alexandria ability to use records in swagger param and response attributes issue 649 https github com danieleteti delphimvcframework issues 649 improved wizard now it produces commented code to show how to use contextevents improved compatibility with delphi 10 2 tokyo and older versions thanks mark lobanov added sample and middleware for prometheus using https github com marcobreveglieri prometheus client delphi added profiler logsonlyifoverthreshold which logs only if over the defined threshold fix issue 648 https github com danieleteti delphimvcframework issues 648 thanks to sf spb https github com sf spb fix issue 652 https github com danieleteti delphimvcframework issues 652 thanks to bssdts https github com bssdts pr 651 https github com danieleteti delphimvcframework pull 651 thanks to francisco zanini https github com zaniniflz what s new in dmvcframework 3 2 3 radium default error responses contains the official reason string associated to the http status code this can be a breaking change for some generic client which doesn t correctly interpret the http status code added static method http status reasonstringfor httpstatuscode wich returns the standard reasonstring for a given http status code improved handling of tmvcerrorresponse information mid air collision handling now uses sha1 instead of md5 added mvcframework commons mvc http status codes const array containing all the http status codes with its reasonstring support for tobject descendants in jsonrpc apis not only for jsonobject and jsonarray new global configuration variable mvcserializenulls when mvcserializenulls true default empty nullables and nil are serialized as json null when mvcserializenulls false empty nullables and nil are not serialized at all nullable types now have equal method support the new method tryhasvalue out value works like hasvalue but returns the contained value if present also there is a better equality check strategy unit tests now are always executed for win32 and win64 bit both client and server added tmvcactiverecord refresh method unit test suites generates one nunit xml output file for each platform new built in profiler usable with delphi 10 4 to profile a block of code write the following delphi procedure tmycontroller profilersample1 begin notprofiled this line is not profiled the following begin end block will be profiled timing will be saved in a profiler log begin var lprof profiler start context actionqualifiedname dosomething dosomethingelse render just executed context actionqualifiedname end profiler writes automatically to the log notprofiled this line is not profiled end procedure tmycontroller dosomething begin begin var lprof profiler start dosomething sleep 100 end end procedure tmycontroller dosomethingelse begin begin var lprof profiler start dosomethingelse sleep 100 dosomething end end procedure tmycontroller notprofiled begin sleep 100 end the log contains the following lines check the caller called relationship shown using and and the deep level 1 maincontrolleru tmycontroller profilersample1 profiler 2 dosomething profiler 2 dosomething elapsed 00 00 00 1088214 profiler 2 dosomethingelse profiler 3 dosomething profiler 3 dosomething elapsed 00 00 00 1096617 profiler 2 dosomethingelse elapsed 00 00 00 2188468 profiler 1 maincontrolleru tmycontroller profilersample1 elapsed 00 00 00 3277806 profiler to get more info check the profiling example all profiler logs are generated with a log level info if measured time is greater than warningthreshold the log level is warning warningthreshold is expressed in milliseconds and by default is equals to 1000 new context property named actionqualifiedname which contains the currently executed action in the form unitname classname actionname it is available where the context property is available obviously is not available in the onbeforerouting middleware events added objectpool and intfobjectpool and related unit tests thanks to our sponsor vivaticket s p a https corporate vivaticket com method procedure render const aerrorcode integer const aerrormessage string has been renamed to renderstatusmessage with a better parameter names imvcjsonrpcexecutor supports async call thanks to our sponsor orion law https orionlaw com check the new async sample in samples jsonrpc with published objects removed fotransient if tmvcactiverecord fieldoptions it became obsolete after introduction of foreadonly and fowriteonly improved tmvcactiverecordmiddleware now it can handle multiple connections for the same request also you can completely omit the default connection and just specify wich connection you want to use before starting to create your tmvcactiverecord inherited entities bug fix in 3 2 3 radium fixed a rendering problem in swagger interface format in case of specific json structure fix issue 594 https github com danieleteti delphimvcframework issues 594 thanks to biware repo https github com biware repo fix issue 595 https github com danieleteti delphimvcframework issues 595 fix issue 590 https github com danieleteti delphimvcframework issues 590 fix issue 490 https github com danieleteti delphimvcframework issues 490 fix issue 583 https github com danieleteti delphimvcframework issues 583 thanks to marcelo jaloto https github com marcelojaloto fix issue 585 https github com danieleteti delphimvcframework issues 585 more details about dmvcframework 3 2 3 radium fixes here https github com danieleteti delphimvcframework milestone 8 closed 1 what s new in delphimvcframework 3 2 2 nitrogen new support for delphi 11 x alexandria new tmvcrestclient implementation based on net components the previous one was based on indy components thanks to jo o ant nio duarte https github com joaoduarte19 new mvcjsonrpcallowget attribute allows a remote json rpc published object or a specific method to be called using get http verb as well as post http verb post is always available get is available only if explicitly allowed imvcjsonrpcexecutor allows to specify which http verb to use when call the server json rpc methods the default verb can be injected in the constructor and each executerequest executenotification allows to override od adhere to the instance default new elua server side view support added the view engine requires lua s dlls so it is not included in the main package but in a sampl project check serversideviews lua sample improved under some heavy load circumnstances the logger queue can get full now tthreadsafequeue class uses a cubic function instead of a linear one to wait in case of very high concurrency this allows a better resiliency in case of high load improved internal architecture of custom type serializers in case of dynamic linked packages improved swagger openapi support for system controllers and improved support for param models new tmvclrucache implementation very efficient implementation of lru cache borrowed directly from dmscontainer http dmscontainer bittimeprofessionals com new tmvcredirectmiddleware to handle http redirections in a very simple and flexible way new tmvcactiverecord supports xml field type in postgresql in addition to json and jsonb new oncontextcreate and oncontextdetroyed events for tmvcengine new added parameter rootnode in bodyfor t and bodyforlistof t methods just like the bodyas methods new added nullabletguid in mvcframework nullables pas new property customintfobject iinterface in twebcontext this property can be used to inject custom services factory delphi procedure tmywebmodule webmodulecreate sender tobject begin fmvc tmvcengine create self procedure config tmvcconfig begin configuration code end fmvc addcontroller tmycontroller fmvc onwebcontextcreate procedure const ctx twebcontext begin ctx customintfobject tservicesfactory create implements an interface end fmvc onwebcontextdestroy procedure const ctx twebcontext begin do nothing here end end added parameter to set local timestamp as utc improved openapi swagger support improved support for openapi swagger api versioning check swagger api versioning primer sample improved the unit tests fully test postgresql firebirdsql and sqlite while testing mvcactiverecord framework the other engines are tested using activerecord showcase sample project improved mvcactiverecord does a better job to handle tdate ttime tdatetime types for sqlite it is automatic because sqlite doesn t support date time types improved postgresql firebirdsql interbase and sqlite now support tablename and fields with spaces improved nullable types now it s possible to assign nil to a nullable type and to check its state using the new property isnull which is the negation of the already available property hasvalue improved now tmvcstaticfilemiddleware is able to manage high level criteria to show hide mask specific files in the document web root check issue 548 https github com danieleteti delphimvcframework issues 548 and the updated sample samples middleware staticfiles for more info improved in case of multiple mvcpath swagger consider only the first one thanks to v ferri and our sponsors new mechanism to customize the jwt claims setup using the client request as suggested in issue495 https github com danieleteti delphimvcframework issues 495 new added tmvcactiverecord merge t currentlistoft changesoft to allow merge between two lists of tmvcactiverecord descendants using unitofwork design pattern check the button merge in demo activerecord showcase new added default filtering for tmvcactiverecord descendants check activerecord showcase sample project new serialization and deserialization for pascal set thanks to rshuck https github com rshuck for his suggestions new added partitioning for tmvcactiverecord descendants more info asap dramatically improved all json to dataset operations 1 order of magnitude c a thanks to mpannier https github com mpannier and david moorhouse https github com fastbike for their detailed analysis more info here https github com danieleteti delphimvcframework issues 553 improved after a big refactoring i love to delete code cit daniele teti support a new sqlgenerator is just 2 two methods away just as example this is the current version of tmvcsqlgeneratorpostgresql delphi type tmvcsqlgeneratorpostgresql class tmvcsqlgenerator protected function getcompilerclass trqlcompilerclass override public function createinsertsql const tablename string const map tfieldsmap const pkfieldname string const pkoptions tmvcactiverecordfieldoptions string override function getsequencevaluesql const pkfieldname string const sequencename string const step integer 1 string override end new added new default parameter to tmvcactiverecord removedefaultconnection and tmvcactiverecord removeconnection to avoid exceptions in case of not initialized connection new added the new mvcowned attribute which allows to auto create nested objects in the deserialization phase this will not change the current behavior you ned to explocitly define a property or a field as mvcowned to allows the serialization to create or destroy object for you improved context data property is now created on demand using a lazy loading approach expect an overall speed improvement added logexception function in mvcframework logger pas to easily log exception in standard way improved mvcarentitiesgenerator project now it can better handle border cases field names which collide with delphi keywords and a big number of tables improved error handling for json rpc apis thanks to david moorhouse https github com fastbike more info here https github com danieleteti delphimvcframework issues 538 improved parameter handling for enum and set in json rpc apis new added activerecordconnectionregistry adddefaultconnection const aconnetiondefname string the connection definition must be known by firedac this method simplifies the most common scenario shown below delphi activerecordconnectionregistry adddefaultconnection mycondefname try use active record classes finally activerecordconnectionregistry removedefaultconnection end new added tojsonobject and tojsonarray to the imvcrestresponse these methods automatically parse the response body and return a tjsonobject or a tjsonarray respectively these methods work as a factory the client code need to handle returned istances is the body is not compatible with the request a k a is not a jsonobject in case of tojsonobject or is not a jsonarray in case of tojsonarray an exception is raised new added support for primary guid uuid primary keys attributes and serialization more info at issue 552 https github com danieleteti delphimvcframework issues 552 thanks to marcelo jaloto https github com marcelojaloto for its important collaboration new added tmvcjwtblacklistmiddleware to allow black listing and a sort of logout for a jwt based authentication this middleware must be registered after the tmvcjwtauthenticationmiddleware this middleware provides 2 events named onaccepttoken invoked when a request contains a token need to returns true false if the token is still accepted by the server or not and onnewjwttoblacklist invoked when a client ask to blacklist its current token there is a new sample available which shows the funtionalities samples middleware jwtblacklist new mvcfrombody attribute useful to automatically inject the request body as actual object in the action paramaters for instance in the following action the body request is automatically deserialized as an object of class tperson delphi interface mvchttpmethod httppost mvcpath people procedure createperson const mvcfrombody person tperson implementation procedure trendersamplecontroller createperson const person tperson begin here you can directly use person without call context request bodyas tperson the person object lifecycle is automatically handled by dmvcframework so don t destroy if the request body doesn t exist or cannot be deserialized an exception is raised end mvcfrombody can be used also with collection like data structures interface delphi mvcdoc creates new articles from a list and returns 201 created mvcpath bulk mvchttpmethod httppost procedure createarticles const mvcfrombody articlelist tobjectlist tarticle implementation procedure tarticlescontroller createarticles const articlelist tobjectlist tarticle var larticle tarticle begin for larticle in articlelist do begin getarticlesservice add larticle end render 201 articles created end new mvcfromquerystring attribute useful to automatically inject a query string paramater an action paramater for instance in the following action the query string params fromdate is automatically deserialized as a tdate value and injected in the action delphi interface mvchttpmethod httpget mvcpath invoices procedure getinvoices const mvcfromquerystring fromdate fromdate tdate implementation procedure trendersamplecontroller getinvoices const fromdate tdate begin here fromdate is a valid tdate value deserialized from the querystring paramater named fromdate if the query string parameter doesn t exist or cannot be deserialized an exception is raised end new mvcfromheader attribute useful to automatically inject a header value as an action parameter for instance in the following action the header params xmycoolheader is automatically deserialized as string value and injected in the action delphi interface mvchttpmethod httpget mvcpath invoices procedure getinvoices const mvcfromquerystring fromdate fromdate tdate const mvcfromheader x my cool header xmycoolheader string implementation procedure trendersamplecontroller getinvoices const fromdate tdate const xmycoolheader string begin here xmycoolheader is a string read from the x my cool header request header if the header doesn t exist or cannot be deserialized an exception is raised end new mvcfromcookie attribute useful to automatically inject a cookie value as an action parameter for instance in the following action the cookie mycoolcookie is automatically deserialized as tdate value and injected in the action delphi interface mvchttpmethod httpget mvcpath invoices procedure getinvoices const mvcfromquerystring fromdate fromdate tdate const mvcfromheader x my cool header xmycoolheader string const mvcfromcookie mycoolcookie mycoolcookie tdate implementation procedure trendersamplecontroller getinvoices const fromdate tdate const xmycoolheader string const mycoolcookie tdate begin here mycoolcookie is a tdate read from mycoolcookie cookie available in the request if the cookie doesn t exist or cannot be deserialized an exception is raised end improved while not strictly required nor defined dmvcframework supports sending body data for all http verbs see https developer mozilla org en us docs web http methods get new automated support to avoid mid air collisions new methods setetag and checkifmatch allows a better security without adding complexity to the controller code check avoid mid air collisions sample dproj sample and see https developer mozilla org en us docs web http headers etag avoiding mid air collisions for more info about mid air collisions improved ignored fields handling now is much better in renders method and in objdict as well see issue 528 https github com danieleteti delphimvcframework issues 528 bug fixes in 3 2 2 nitrogen fix https github com danieleteti delphimvcframework issues 484 thanks to jo o ant nio duarte https github com joaoduarte19 fix https github com danieleteti delphimvcframework issues 472 thanks to jo o ant nio duarte https github com joaoduarte19 fix https github com danieleteti delphimvcframework issues 470 thanks to jo o ant nio duarte https github com joaoduarte19 fix https github com danieleteti delphimvcframework issues 453 thanks to jo o ant nio duarte https github com joaoduarte19 fix https github com danieleteti delphimvcframework issues 455 thanks to jo o ant nio duarte https github com joaoduarte19 fix https github com danieleteti delphimvcframework issues 461 fix https github com danieleteti delphimvcframework issues 489 thanks to drcrck https github com drcrck for his initial analisys fix https github com danieleteti delphimvcframework issues 493 thanks to delphiman68 https github com delphiman68 for his initial analisys fix https github com danieleteti delphimvcframework issues 451 fix https github com danieleteti delphimvcframework issues 539 fix https github com danieleteti delphimvcframework issues 560 thanks to david moorhouse https github com fastbike fix https github com danieleteti delphimvcframework issues 335 thanks to jo o ant nio duarte https github com joaoduarte19 fix https github com danieleteti delphimvcframework issues 564 fix https github com danieleteti delphimvcframework issues 570 thanks marcos nielsen https github com marcosnielsen fix https github com danieleteti delphimvcframework issues 565 merged pr 543 https github com danieleteti delphimvcframework pull 543 now the pathinfo is trimmed so the router convert this http myserver com one to this http myserver com one fix for nil objects in lists during serialization fix a very subtle bug in maxrecordcount parameter for rql based methods in tmvcactiverecord uniformed behavior in update and delete method in tmvcactiverecord now these methods raise an exception if the record doesn t exists anymore in the table update or delete statements return affectedrows 0 the behavior can be altered using the new parameter in the call which by default is true warning this change could raise some incompatibilities with the previous version however this is the correct behavior consider the previous one a incorrect behavior to fix fix https github com danieleteti delphimvcframework issues 489 fix https github com danieleteti delphimvcframework issues 518 thanks to microcom bjarne https github com microcom bjarne fix https github com danieleteti delphimvcframework issues 526 thanks to david moorhouse https github com fastbike fix https github com danieleteti delphimvcframework issues 544 thanks to david moorhouse https github com fastbike fix https github com danieleteti delphimvcframework issues 542 thanks to lamberto lodi https github com llodi csw for the hints fix https github com danieleteti delphimvcframework issues 485 fixed fileupload sample fixed an ifdef compatibility problem on mobile platforms thanks to marco cotroneo samples are syntax compatible with delphi 10 1 berlin or better thanks to mark lobanov fix https github com danieleteti delphimvcframework issues 573 thanks to deddyh https github com deddyh the hints fix https github com danieleteti delphimvcframework issues 574 thanks to deddyh https github com deddyh the hints breaking changes in 3 2 2 nitrogen removed deprecated constructor for tmvcjwtauthenticationmiddleware was deprecated since 2019 just use the other one as suggested changed signature of method imvcmiddleware onaftercontrolleraction what s new in delphimvcframework 3 2 1 carbon this version is the version referenced by the delphimvcframework the official guide http www danieleteti it books book available in english portuguese and spanish this version introduced new features in many different areas swagger server side view mvcactiverecord renders etc however there is no a single big feature this version contains also a good number of bugfixes it s not a critical updated but this is the best version ever at least so far and is the suggested version for starting new projects enjoy improvements docexpansion parameter for swagger https github com danieleteti delphimvcframework issues 408 new context twebcontext parameter in json rpc hooks delphi called before any actual routing procedure onbeforeroutinghook const context twebcontext const json tjsonobject called after routing and before the actual remote method invocation procedure onbeforecallhook const context twebcontext const json tjsonobject called after actual remote method invocation even if the method raised an exception procedure onaftercallhook const context twebcontext const json tjsonobject when a json rpc request returns a system boolean the result will be a json true or false and no 1 or 0 as it was in the 3 2 0 boron imvcjsonrpcexecutor executenotification returns a ijsonrpcresponse in case of error response contains information about the error in case of successful execution the response is a null object https en wikipedia org wiki null object pattern new react demo thanks to angelo sobreira da silva https github com angelosobreira serialization support for tlist integer tlist string tlist boolean and for all tlist t of simple types added method metadataasjsonobject fieldnamecase tmvcnamecase nclowercase tjsonobject in tdatasethelper this method returns the dataset field definitions while this is valid only for delphi datasets can be useful to describe a dataset to a delphi client json fielddefs datatype 3 displayname id fieldname id precision 0 size 0 datatype 24 displayname code fieldname code precision 0 size 5 datatype 24 displayname description fieldname description precision 0 size 200 datatype 37 displayname price fieldname price precision 18 size 2 the static method class procedure tfiredacutils createdatasetfrommetadata afdmemtable tfdmemtable ameta tjsonobject gets the previous structure and initialize the fields of afdmemtable with it when a tfdmemtable is initialized using this approach the data can be directly loaded from a jsonarray of jsonobject with the same field names warning this mechanism works only for delphi clients check sample articles crud vcl client meta dproj to understand the involved mechanisms added foreadonly and fowriteonly as field options in mvctablefield attribute used by tmvcactiverecord currently available field options are foprimarykey it s the primary key of the mapped table foautogenerated not written read similar to foreadonly but is reloaded after insert and update foreadonly not written read fowriteonly written not read now it is possible to declare entities like the followings or with any other combinations delphi mvcnamecase nclowercase mvctable articles tarticlewithwriteonlyfields class tcustomentity private mvctablefield id foprimarykey foautogenerated fid nullableint32 mvctablefield description fowriteonly fdescription string mvctablefield price fowriteonly fprice integer public property id nullableint32 read fid write fid property description string read fdescription write fdescription property price integer read fprice write fprice end mvcnamecase nclowercase mvctable articles tarticlewithreadonlyfields class tcustomentity private mvctablefield id foprimarykey foreadonly fid nullableint32 fcode nullablestring mvctablefield description foreadonly fdescrizione string mvctablefield price foreadonly fprice currency public property id nullableint32 read fid write fid property code nullablestring read fcode write fcode property description string read fdescription write fdescription property price currency read fprice write fprice end added the ability to deserialize an object or alist of objects starting from an arbitrary node in the json or other format present in the request body works for bodyas t and for bodyaslistof t thanks to rapha l emourgeon https github com osaris for the bodyaslistof t implementation https github com danieleteti delphimvcframework issues 415 delphi procedure tbookscontroller createbook var lbook tbook begin this call deserialize a tbook instance starting from the book node of the request body lbook context request bodyas tbook book try lbook insert render201created api books lbook id tostring finally lbook free end end improved the primary key type handling for manual handling in mvcactiverecord delphi procedure tmybaseentity onbeforeinsert begin inherited regardless the name of the pk field the following code fills the pk with a guid inheriting the other entities from this all will inherit this behavior setpk tvalue from nullablestring tguid newguid tostring if the pk was a simple string the code should be like the following setpk tguid newguid tostring end improved activerecord showcase sample improved tmvcstaticfilesmiddleware now is able to correctly serve spa applications from any subfolder added property context hostingframeworktype this property is of type tmvchostingframeworktype and can assume one of the following values hftindy if the service is using the built in indy http server hftapache if the project is compiled as apache module or hftisapi if the project is compiled as isapi module tmvcnamecase got the new ncsnakecase among the possibles casing thanks to jo o ant nio duarte https github com joaoduarte19 for its work in this area now the tmvcnamecase declaration is the following delphi tmvcnamecase ncasis ncuppercase nclowercase nccamelcase ncpascalcase ncsnakecase here s how the new ncsnakecase works original snakecase onetwo one two one two one two onetwo03 one two 03 support for mustache https mustache github io partials thanks to david moorhouse https github com fastbike and his work about issue 221 https github com danieleteti delphimvcframework issues 221 sample samples serversideviews mustache has been updated to show how to use partials added dynamic properties access to tmvcactiverecord descendants indexed property attributes is index using the property name and set get a tvalue representing the property value delphi procedure tmainform btnattributesclick sender tobject var lcustomer tcustomer lid integer begin lcustomer tcustomer create try lcustomer attributes companyname google inc lcustomer attributes city montain view ca lcustomer attributes note hello there lcustomer attributes code xx123 lcustomer attributes rating 3 lcustomer insert lid lcustomer id finally lcustomer free end lcustomer tmvcactiverecord getbypk tcustomer lid try assert google inc lcustomer attributes companyname astype nullablestring value assert montain view ca lcustomer attributes city asstring assert xx123 lcustomer attributes code astype nullablestring value assert hello there lcustomer attributes note asstring lcustomer update finally lcustomer free end breaking change tmvcstaticfilemiddleware cannot be registered to anymore the suggested solution is to create a simple redirection controller which redirect to the proper path check this example https github com danieleteti delphimvcframework blob master samples middleware staticfiles sparedirectcontroller pas breaking change documentroot of tmvcstaticfilemiddleware must be a valid folder if documentroot doesn t exist an exception is raised fix for issue 421 https github com danieleteti delphimvcframework issues 421 fix for issue 424 https github com danieleteti delphimvcframework issues 424 fix for issue436 https github com danieleteti delphimvcframework issues 436 fix for issue438 https github com danieleteti delphimvcframework issues 438 thanks to jadeade https github com jadeade fix for issue432 https github com danieleteti delphimvcframework issues 432 fix for issue435 https github com danieleteti delphimvcframework issues 435 thanks to sonjli https github com sonjli for its initial work fix for issue434 https github com danieleteti delphimvcframework issues 434 thanks to david moorhouse https github com fastbike for his detailed analysis fix for issue221 https github com danieleteti delphimvcframework issues 221 fix for issue430 https github com danieleteti delphimvcframework issues 430 thanks to sonjli https github com sonjli for its initial work fix for issue444 https github com danieleteti delphimvcframework issues 444 fix for issue408 https github com danieleteti delphimvcframework issues 408 a k a docexpansion parameter for swagger check all the issues closed in this release https github com danieleteti delphimvcframework issues q is 3aissue milestone 3a3 2 1 carbon what s new in 3 2 0 boron new support for delphi 10 4 sydney new added nullable support in mvcactiverecord nullables defined in mvcframework nullables pas check activerecord showcase sample new added non autogenerated primary keys in mvcactiverecord check activerecord showcase sample new complete support for nullable types in the default serializer nullables defined in mvcframework nullables pas new added nccamelcase and ncpascalcase to the available attribute formatters mvcnamecase property field name rendered name ncuppercase cod article cod article nclowercase cod article cod article ncpascalcase cod article codarticle ncpascalcase codarticle codarticle ncpascalcase with underscores withunderscores nccamelcase cod article codarticle nccamelcase codarticle codarticle nccamelcase with underscores withunderscores new added swagger support thanks to jo o ant nio duarte https github com joaoduarte19 and geoffrey smith https github com geoffsmith82 new attribute mvcdonotdeserialize if marked with this rtti attribute a property or a field is not deserialized and its value remain the same as was before the object deserialization new added sqlgenerator and rql compiler for postgresql sqlite and mssqlserver in addition to mysql mariadb firebird and interbase new mvcnameas attribute got the param fixed default false if fixed is true then the name is not processed by the mvcnamecase attribute assigned to the owner type new added support for interfaces serialization now it is possible to serialize spring4d collections thanks to jo o ant nio duarte https github com joaoduarte19 new added support for rendering spring4d nullable types thanks to jo o ant nio duarte https github com joaoduarte19 new added onrouterlog event to log custom information for each request thanks to andrea ciotti https github com andreaciotti for the first implementation and its pr new optionally load system controllers those who provide describeserver info describeplatform info and serverconfig info system actions setting config tmvcconfigkey loadsystemcontrollers false in the configuration block improved now the router consider accept compatible for every mvcproduces values improved greatly improved support for hateoas https en wikipedia org wiki hateoas in renders check trendersamplecontroller getpeople asobjectlist hateos and all the others actions end with hateos in renders dproj sample delphi now is really easy to add links property automatically for each collection element while rendering render tperson people true procedure const aperson tperson const links imvclinks begin links addreflink add hateoas href people aperson id tostring add hateoas rel self add hateoas type application json add title details for aperson fullname links addreflink add hateoas href people add hateoas rel people add hateoas type application json end datasets have a similar anon method to do the same thing render ldm qrycustomers false procedure const ds tdataset const links imvclinks begin links addreflink add hateoas href customers ds fieldbyname cust no asstring add hateoas rel self add hateoas type application json links addreflink add hateoas href customers ds fieldbyname cust no asstring orders add hateoas rel orders add hateoas type application json end single object rendering allows hateoas too render lperson false procedure const aobject tobject const links imvclinks begin links addreflink add hateoas href people tperson aobject id tostring add hateoas rel self add hateoas type tmvcmediatype application json links addreflink add hateoas href people add hateoas rel people add hateoas type tmvcmediatype application json end better packages organization check packages folder new tmvcactiverecord count method e g tmvcactiverecord count tcustomer returns the number of records for the entity mapped by the class tcustomer change tmvcactiverecord getbypk t raises an exception by default if the record is not found optionally can returns nil using new parameter raiseexceptionifnotfound new contains clause has been added in the rql compiler for firebird and interbase new added support out operator in rql parser the rql out operator is equivalent to the sql not in operator new tmvcanalyticsmiddleware to do automatic analytics on the api generates a csv file based on an idea by nirav kaku https www facebook com nirav kaku check the sample in samples middleware analytics new tmvcactiverecord deleteall deletes all the records from a table new tmvcactiverecord deleterql deletes records using an rql expression as where clause new tmvcactiverecord store which automatically executes insert or update considering primary key value new tmvcactiverecord allows to use table name and field name with spaces currently supported only by the postgresql compiler new microsoft sqlserver support in mvcactiverecord and rql thanks to one of the biggest delphi based company in italy which heavily uses dmvcframework and dmscontainer http www bittimeprofessionals it prodotti dmscontainer new sqlite support in mvcactiverecord and rql so that mvcactiverecord can be used also for delphi mobile projects default json serializer can verbatim pass properties with type jsondataobjects tjsonobject without using string as carrier of json improved activerecordshowcase sample is much better now improved all activerecord methods which retrieve records can now specify the data type of each parameter using delphi s tfieldtype enumeration improved in case of unhandled exception tmvcengine is compliant with the default response content type usually it did would reply using text plain added new overloads for all the log calls now it is possible to call logd lmyobject to get logged lmyobject as json custom type serializers not supported in log new strdict array of string array of string function allows to render a dictionary of strings in a really simple way see the following action sample delphi procedure tmy getpeople const value integer begin if value mod 2 0 then begin raise emvcexception create http status notacceptable we don t like odd numbers end render strdict id message 123 we like even numbers thank you for your value tostring end new custom exception handling based on work of david moorhouse https github com fastbike sample custom exception handling show how to use it improved exceptions rendering while using mime types different to application json ssl server support for tmvclistener thanks to sven harazim https github com landrix improved datasets serialization speed improvement in some case the performance improves of 2 order of magnitude https github com danieleteti delphimvcframework issues 205 issuecomment 479513158 thanks to https github com pedrooliveira01 new added in operator in rql parser thank you to jo o ant nio duarte https github com joaoduarte19 for his initial work on this new added tmvcactiverecord count t rql to count record based on rql criteria new tmvcactiverecord can handle non autogenerated primary key new added support for x http method override to work behind corporate firewalls new sample server in dll added new method in the dataset helper to load data into a dataset from a specific jsonarray property of a jsonobject procedure tdatasethelper loadjsonarrayfromjsonobjectproperty const ajsonobjectstring string const apropertyname string improved new constants defined in http status to better describe the http status response improved now firebird rql sqlgenerator can include primary key in createinsert if not autogenerated new added support for tarray string tarray integer and tarray double in default json serializer thank you pedro oliveira https github com pedrooliveira01 improved jwt standard compliance thanks to vinicius sanchez https github com viniciussanchez for his work on issue 241 https github com danieleteti delphimvcframework issues 241 improved dmvcframework now has 180 unit tests that checks its functionalities at each build improved better exception handling in onbeforedispatch thanks to spinettaro https github com spinettaro new strtojsonobject function to safely parse a string into a json object new serialization callback for custom tdataset descendants serialization in tmvcjsondataobjectsserializer delphi procedure tmainform btndatasettojsonarrayclick sender tobject var lser tmvcjsondataobjectsserializer ljarray tjsonarray begin fdquery1 open lser tmvcjsondataobjectsserializer create try ljarray tjsonarray create try lser datasettojsonarray fdquery1 ljarray tmvcnamecase nclowercase procedure const afield tfield const ajsonobject tjsonobject var handled boolean begin if sametext afield fieldname created at then begin ajsonobject s year and month formatdatetime yyyy mm tdatetimefield afield value handled true end end the json objects will not contains created at anymore but only year and month memo1 lines text ljarray tojson false finally ljarray free end finally lser free end end new shortcut render methods which simplify restful api development procedure render201created const location string const reason string created virtual procedure render202accepted const href string const id string const reason string accepted virtual procedure render204nocontent const reason string no content virtual added de serializing iterables e g generic lists support without mvclistof attribute thank you to jo o ant nio duarte https github com joaoduarte19 it is now possible to deserialize a generic class like this delphi tgenericentity t class class private fcode integer fitems tobjectlist t fdescription string public constructor create destructor destroy override property code integer read fcode write fcode property description string read fdescription write fdescription mvclistof t no need property items tobjectlist t read fitems write fitems end before it was not possible because you should add the mvclistof attribute to the tobjectlist type property new added serialization support for thanks to dockerandy https github com dockerandy for his initial work tarray string tarray integer tarray int64 tarray double new the mvcarentitiesgenerator can optionally register all the generated entities also in the activerecordmappingregistry thanks to fabrizio bitti https twitter com fabriziobitti from bit time software http www bittime it new experimental alpha stage support for android servers new children objects lifecycle management in tmvcactiverecord methods addchildren and removechildren really useful to manage child objects such relations or derived properties and are safe in case of multiple addition of the same object as children delphi having the following declaration type mvcnamecase nccamelcase mvctable authors tauthor class tpersonentitybase private fbooks tenumerable tbookref mvctablefield full name ffullname string function getbooks tenumerable tbookref public mvcnameas full name property fullname string read ffullname write ffullname property books tenumerable tbookref read getbooks end method getbooks can be implemented as follows implementation function tauthor getbooks tenumerable tbookref begin if fbooks nil then begin fbooks tmvcactiverecord where tbookref author id id addchildren fbooks fbooks will be freed when self will be freed end result fbooks end json rpc improvements new added tmvcjsonrpcexecutor confighttpclient to fully customize the inner thttpclient e g connectiontimeout responsetimeout and so on improved jsonrpc automatic object publishing can not invoke inherited methods if not explicitly defined with mvcinheritable attribute new calling jsonrpcendpoint describe returns the methods list available for that endpoint new full support for named parameters in json rpc call server and client positional parameters example delphi procedure tmainform btnsubtractclick sender tobject var lreq ijsonrpcrequest lresp ijsonrpcresponse begin lreq tjsonrpcrequest create lreq method subtract lreq requestid random 1000 lreq params add strtoint edtvalue1 text lreq params add strtoint edtvalue2 text lresp fexecutor executerequest lreq edtresult text lresp result asinteger tostring end named parameters example delphi procedure tmainform btnsubtractwithnamedparamsclick sender tobject var lreq ijsonrpcrequest lresp ijsonrpcresponse begin lreq tjsonrpcrequest create lreq method subtract lreq requestid random 1000 lreq params addbyname value1 strtoint edit1 text lreq params addbyname value2 strtoint edit2 text lresp fexecutor executerequest lreq edit3 text lresp result asinteger tostring end check official jsonrpc 2 0 documentation https www jsonrpc org specification examples for more examples new jsonrpc hooks for published objects delphi called before as soon as the http arrives procedure tmypublishedobject onbeforerouting const json tjdojsonobject called before the invoked method procedure tmypublishedobject onbeforecall const jsonrequest tjdojsonobject called just before to send response to the client procedure tmypublishedobject onbeforesendresponse const jsonresponse tjdojsonobject deprecated tdatasetholder is deprecated use the shining new objectdict boolean instead new objectdict function is the suggested way to render all the most common data types it returns a imvcobjectdictionary which is automatically rendered by the renders check the renders dproj sample here s some example of the shining new objectdict example 1 rendering a list of objects not freeing them after rendering classic delphi procedure trendersamplecontroller getlotofpeople begin render tperson getpeoplelist false end new approach with objectdict delphi procedure trendersamplecontroller getlotofpeople begin render objectdict false add data getpeoplelist end example 2 rendering a list of objects and automatically free them after rendering classic delphi procedure trendersamplecontroller getlotofpeople begin render tperson getpeoplelist end new approach with objectdict delphi procedure trendersamplecontroller getlotofpeople begin render objectdict add data getpeoplelist end example 3 rendering a list of objects adding links for hateoas support classic delphi procedure trendersamplecontroller getpeople asobjectlist hateoas var p tperson people tobjectlist tperson begin people tobjectlist tperson create true region fake data p tperson create p firstname daniele p lastname teti p dob encodedate 1979 8 4 p married true people add p p tperson create p firstname john p lastname doe p dob encodedate 1879 10 2 p married false people add p p tperson create p firstname jane p lastname doe p dob encodedate 1883 1 5 p married true people add p endregion render tperson people true procedure const aperson tperson const links imvclinks begin links addreflink add hateoas href people aperson id tostring add hateoas rel self add hateoas type application json add title details for aperson fullname links addreflink add hateoas href people add hateoas rel people add hateoas type application json end end new approach with objectdict delphi procedure trendersamplecontroller getpeople asobjectlist hateoas var p tperson people tobjectlist tperson begin people tobjectlist tperson create true region fake data p tperson create p firstname daniele p lastname teti p dob encodedate 1979 8 4 p married true people add p p tperson create p firstname john p lastname doe p dob encodedate 1879 10 2 p married false people add p p tperson create p firstname jane p lastname doe p dob encodedate 1883 1 5 p married true people add p endregion render objectdict add data people procedure const aperson tobject const links imvclinks begin links addreflink add hateoas href people tperson aperson id tostring add hateoas rel self add hateoas type application json add title details for tperson aperson fullname links addreflink add hateoas href people add hateoas rel people add hateoas type application json end end objectdict is able to render multiple data sources datasets objectlists objects or strdict at the same time using different casing hateoas callbacks and modes delphi procedure ttestservercontroller testobjectdict var ldict imvcobjectdictionary begin ldict objectdict false add ncuppercase list getdataset nil dstallrecords ncuppercase add nclowercase list getdataset nil dstallrecords nclowercase add nccamelcase list getdataset nil dstallrecords nccamelcase add ncpascalcase list getdataset nil dstallrecords ncpascalcase add ncuppercase single getdataset nil dstsinglerecord ncuppercase add nclowercase single getdataset nil dstsinglerecord nclowercase add nccamelcase single getdataset nil dstsinglerecord nccamelcase add ncpascalcase single getdataset nil dstsinglerecord ncpascalcase add meta strdict page 1 render ldict end objectdict is the suggested way to renders data however the other ones are still there and works as usual added ability to serialize deserialize types enumerated by an array of mapped values thanks to jo o ant nio duarte https github com joaoduarte19 delphi type tmonthenum mejanuary mefebruary memarch meapril tentitywithenums class private fmonthmappednames tmonthenum fmonthenumname tmonthenum fmonthorder tmonthenum public list items separated by comma or semicolon mvcenumserializationtype estenummappedvalues january february march april property monthmappednames tmonthenum read fmonthmappednames write fmonthmappednames mvcenumserializationtype estenumname property monthenumname tmonthenum read fmonthenumname write fmonthenumname mvcenumserializationtype estenumord property monthorder tmonthenum read fmonthorder write fmonthorder end new installation procedure open the project group select the correct one from the following table build all install the design time package dmvcframeworkdt add the following paths in the delphi library path here c dev dmvcframework is the dmvcframework main folder c dev dmvcframework sources c dev dmvcframework lib loggerpro c dev dmvcframework lib swagdoc source c dev dmvcframework lib dmustache delphi version project group delphi 10 4 sydney packages d104 dmvcframework group groupproj delphi 10 3 rio packages d103 dmvcframework group groupproj delphi 10 2 tokyo packages d102 dmvcframework group groupproj delphi 10 1 berlin packages d101 dmvcframework group groupproj delphi 10 0 seattle packages d100 dmvcframework group groupproj breaking changes in 3 2 0 boron in mvcactiverecord attribute mvcprimarykey has been removed and merged with mvctablefield so now tmvcactiverecordfieldoption is a set of foprimarykey foautogenerated fotransient check activerecord showcase dproj sample middleware onaftercontrolleraction are now invoked in the same order of onbeforecontrolleraction previously were invoked in reversed order tmvcengine is no more responsible for static file serving if you need static files used the new tmvcstaticfilesmiddleware check the sample as consequence tmvcconfigkey documentroot tmvcconfigkey indexdocument and tmvcconfigkey fallbackresource are no more available tmvcengine config property is now read only can be changed only in the anonymous method injected in the constructor delphi this is valid fmvc tmvcengine create self procedure config tmvcconfig begin session timeout 0 means session cookie config tmvcconfigkey sessiontimeout 0 other configurations end fmvc addcontroller tmycontroller this is not valid exception is raised fmvc tmvcengine create self fmvc config tmvcconfigkey sessiontimeout 0 run time error here fmvc addcontroller tmycontroller bug fixes in 3 2 0 boron fixed issue38 https github com danieleteti delphimvcframework issues 38 fixed issue140 https github com danieleteti delphimvcframework issues 140 fixed issue161 https github com danieleteti delphimvcframework issues 161 fixed issue184 https github com danieleteti delphimvcframework issues 184 fixed issue278 https github com danieleteti delphimvcframework issues 278 fixed issue164 https github com danieleteti delphimvcframework issues 164 fixed issue182 https github com danieleteti delphimvcframework issues 182 fixed issue232 https github com danieleteti delphimvcframework issues 232 thanks to jo o ant nio duarte https github com joaoduarte19 fixed issue239 https github com danieleteti delphimvcframework issues 239 fixed issue289 https github com danieleteti delphimvcframework issues 289 thanks to jo o ant nio duarte https github com joaoduarte19 fixed issue291 https github com danieleteti delphimvcframework issues 291 thanks to jo o ant nio duarte https github com joaoduarte19 fixed issue305 https github com danieleteti delphimvcframework issues 305 thanks to jo o ant nio duarte https github com joaoduarte19 fixed issue312 https github com danieleteti delphimvcframework issues 312 fixed issue330 https github com danieleteti delphimvcframework issues 330 fixed issue333 https github com danieleteti delphimvcframework issues 333 fixed issue334 https github com danieleteti delphimvcframework issues 334 fixed issue336 https github com danieleteti delphimvcframework issues 336 fixed issue337 https github com danieleteti delphimvcframework issues 337 fixed issue338 https github com danieleteti delphimvcframework issues 338 fixed issue239 https github com danieleteti delphimvcframework issues 239 fixed issue345 https github com danieleteti delphimvcframework issues 345 fixed issue349 https github com danieleteti delphimvcframework issues 349 fixed issue350 https github com danieleteti delphimvcframework issues 350 fixed issue355 https github com danieleteti delphimvcframework issues 355 fixed issue356 https github com danieleteti delphimvcframework issues 356 fixed issue362 https github com danieleteti delphimvcframework issues 362 fixed issue363 https github com danieleteti delphimvcframework issues 363 fixed issue364 https github com danieleteti delphimvcframework issues 364 thanks to jo o ant nio duarte https github com joaoduarte19 fixed issue366 https github com danieleteti delphimvcframework issues 366 fixed issue376 https github com danieleteti delphimvcframework issues 376 thanks to jo o ant nio duarte https github com joaoduarte19 fixed issue379 https github com danieleteti delphimvcframework issues 379 thanks to jo o ant nio duarte https github com joaoduarte19 and maincosi https github com maiconsi for their work fixed issue386 https github com danieleteti delphimvcframework issues 386 thanks to rafael dipold https github com dipold fixed issue388 https github com danieleteti delphimvcframework issues 388 fixed has been patched a serious security bug affecting deployment configurations which uses internal webserver to serve static files do not affect all apache iis or proxied deployments thanks to stephan munz to have discovered it update to dmvcframework 3 2 rc5 is required for all such kind of deployments what s new in 3 1 0 lithium tmvcactiverecord framework tmvcactiverecordcontroller with automatic restful interface generation and permissions handling entityprocessor for tmvcactiverecordcontroller to handle complex cases json rpc executor allows to configure http headers for json rpc requests and notifications tdatasetholder 404 and 500 status code returns always a text plain content type speed improvement max request size can now limit the size of the incoming http requests tmvcresponse can handle generic non error responses gzip compression support in addition to deflate in tcompressionmiddleware tcompressionmiddleware has been renamed in tmvccompressionmiddleware support for spring4d nullable types check samples renders spring4d nullables tmvcjsonrpcpublisher allows to easily expose plain delphi objects and even data modules through a json rpc 2 0 interface breaking change the json rpc client layer is now interface based what s new in 3 0 0 hydrogen first release of the 3 0 0 version what s new in 2 1 3 lithium fix https github com danieleteti delphimvcframework issues 64 added unit tests to avoid regressions what s new in 2 1 2 helium fix for delphi versions who don t have tjsonbool delphi xe8 or older added new conditional define in dmvcframework inc jsonbool defined for delphi seattle what s new in 2 1 1 hydrogen updated the ide expert to show the current version of the framework fix to the mapper about the datasets null values needs to be checked in old delphi versions added support for boolean values in datasets serialization added unit tests about mapper and dataset fields nullability the current version is available in constant dmvcframework version defined in mvcframework commons pas roadmap delphimvcframework roadmap is always updated as soon as the features planned are implemented check the roadmap here roadmap md trainings consultancy or custom development service as you know good support on open source software is a must for professional users if you need trainings consultancy or custom developments on delphimvcframework send an email to dmvcframework at bittime dot it alternatively you can send a request using the contacts forms http www bittimeprofessionals it contatti on bit time professionals website http www bittimeprofessionals it bit time professionals is the company behind delphimvcframework the lead developer works there samples and documentation dmvcframework is provided with a lot of examples focused on specific functionality all samples are in samples samples folder getting started 5 minutes guide dmvcframework allows to create powerful restful servers without effort you can create a full flagged restful server in a couple of clicks delphimvcframework installation dmvcframework must be installed using the github release https github com danieleteti delphimvcframework releases latest download the zip file unzip it the release zip in a folder named c dmvc or where you prefer launch rad studio and open c dmvc packages d104 dmvcframework group groupproj warning in the last path shown d104 is for delphi 10 4 sydney use the correct package for your delphi version install the package and close all now dmvcframework expert is installed and you are able to create dmvcframework project go to go to file new other select delphi project dmvc delphimvcframework project docs getting started 10 ide expert new project png from the resultant dialog leave all the default settings and click ok if you try to compile the project now you will get compiler errors because we ve to configure the library paths to let the compiler finds the needed source files go to tools options language delphi library and add in the library path the following paths c dmvc sources c dmvc lib dmustache c dmvc lib loggerpro c dmvc lib swagdoc source run the project f9 now a new console application is running serving you first dmvcframework server simple isn t it the wizard generated controller shown some basic setup and some actions you can start from here to create your application use the huge number of sample to understand how each dmvcframwork feature work sample controller below a basic sample of a dmvcframework controller with 2 action delphi unit userscontrolleru interface uses mvcframework type mvcpath users tuserscontroller class tmvccontroller public the following action will be with a get request like the following http myserver com users 3 mvcpath id mvcproduces application json mvchttpmethod httpget mvcdoc returns a user as a json object procedure getuser id integer the following action will be with a get request like the following http myserver com users mvcpath mvcproduces application json mvchttpmethod httpget mvcdoc returns the users list as a json array of json objects procedure getusers the following action will be with a put request like the following http myserver com users 3 and in the request body there should be a serialized tuser mvcpath id mvcproduce application json mvchttpmethod httpput mvcdoc update a user procedure updateuser id integer the following action will respond to a post request like the following http myserver com users and in the request body there should be the new user to create as json mvcpath mvcproduce application json mvchttpmethod httppost mvcdoc create a new user returns the id of the new user procedure createuser end implementation uses mytransactionscript contains actual data access code tuserscontroller procedure tuserscontroller getusers var users tobjectlist tuser begin users getusers render users end procedure tuserscontroller getuser id integer var user tuser begin user getuserbyid id render user end procedure tuserscontroller updateuser id integer var user tuser begin user context request bodyas tuser updateuser id user render user end procedure tuserscontroller createuser var user tuser begin user context request bodyas tuser createuser user render user end end now you have a performant restful server wich respond to the following urls get users id eg users 1 users 45 etc put users id eg users 1 users 45 etc with the json data in the request body post users the json data must be in the request body how to create a dmvcframework servers container if you don t plan to deploy your dmvcframework server behind a webserver apache or iis you can also pack more than one listener application server into one single executable in this case the process is a bit different and involves the creation of a listener context however create a new server is a simple task delphi uses mvcframework server mvcframework server impl var lserverlistener imvclistener begin lserverlistener tmvclistener create tmvclistenerproperties new setname listener1 setport 5000 setmaxconnections 1024 setwebmoduleclass yourserverwebmoduleclass lserverlistener start lserverlistener stop end if you want to add a layer of security in its webmodule you should add the security middleware delphi uses mvcframework server mvcframework server impl mvcframework middleware authentication procedure ttestwebmodule webmodulecreate sender tobject begin fmvcengine tmvcengine create self add yours controllers fmvcengine addcontroller tyourcontroller add security middleware fmvcengine addmiddleware tmvcbasicauthenticationmiddleware create tmvcdefaultauthenticationhandler new setonauthentication procedure const ausername apassword string auserroles tlist string var isvalid boolean const asessiondata tdictionary string string begin isvalid ausername equals dmvc and apassword equals 123 end end in stand alone mode you can work with a context that supports multiple listeners servers delphi uses mvcframework server mvcframework server impl var lserverlistenerctx imvclistenerscontext begin lserverlistenerctx tmvclistenerscontext create lserverlistenerctx add tmvclistenerproperties new setname listener1 setport 6000 setmaxconnections 1024 setwebmoduleclass webmoduleclass1 lserverlistenerctx add tmvclistenerproperties new setname listener2 setport 7000 setmaxconnections 1024 setwebmoduleclass webmoduleclass2 lserverlistenerctx startall end rql introduction resource query language rql is a query language designed for use in uris with object style data structures dmvcframework supports rql natively and the included mvcactiverecord micro framework implement a large subset of the rql specs rql can be thought as basically a set of nestable named operators which each have a set of arguments rql is designed to have an extremely simple but extensible grammar that can be written in a url friendly query string a simple rql query with a single operator that indicates a search for any resources with a property of foo that has value of 5 could be written sparql eq foo 5 a more complex filter can include an arbitrary number of chained functions sparql or and eq name daniele eq surname teti and eq name peter eq surname parker sort name which is translated details depends from the rdbms in the following sql sql select name surname other fields from people where name daniele and surname teti or name peter and surname parker order by name asc rql as implemented by dmvcframework rql is designed for modern application development it is built for the web ready for nosql and highly extensible with simple syntax here is a definition of the common operators as implemented in dmvcframework activerecord eq property value filters for objects where the specified property s value is equal to the provided value lt property value filters for objects where the specified property s value is less than the provided value le property value filters for objects where the specified property s value is less than or equal to the provided value gt property value filters for objects where the specified property s value is greater than the provided value ge property value filters for objects where the specified property s value is greater than or equal to the provided value ne property value filters for objects where the specified property s value is not equal to the provided value and query query applies all the given queries or query query the union of the given queries sort property sorts by the given property in order specified by the prefix for ascending for descending limit count start maxcount returns the given range of objects from the result set contains property value expression filters for objects where the specified property s value is an array and the array contains any value that equals the provided value or satisfies the provided expression in property array of values filters for objects where the specified property s value is in the provided array out property array of values filters for objects where the specified property s value is not in the provided array not yet availables select property property trims each object down to the set of properties defined in the arguments values property returns an array of the given property value for each object aggregate property function aggregates the array grouping by objects that are distinct for the provided properties and then reduces the remaining other property values using the provided functions distinct returns a result set with duplicates removed excludes property value expression filters for objects where the specified property s value is an array and the array does not contain any of value that equals the provided value or satisfies the provided expression rel relation name query applies the provided query against the linked data of the provided relation name sum property finds the sum of every value in the array or if the property argument is provided returns the sum of the value of property for every object in the array mean property finds the mean of every value in the array or if the property argument is provided returns the mean of the value of property for every object in the array max property finds the maximum of every value in the array or if the property argument is provided returns the maximum of the value of property for every object in the array min property finds the minimum of every value in the array or if the property argument is provided returns the minimum of the value of property for every object in the array recurse property recursively searches looking in children of the object as objects in arrays in the given property value first returns the first record of the query s result set one returns the first and only record of the query s result set or produces an error if the query s result set has more or less than one record in it count returns the count of the number of records in the query s result set dotenv syntax since 3 4 0 neon dmvcframework supports dotenv configuration files tl dr read key value pairs from a env file and set them as environment variables the format is not formally specified and still improves over time that being said env files should mostly look like bash files keys can be unquoted or single quoted values can be unquoted single or double quoted spaces before and after keys equal signs and values are ignored values can be followed by a comment variable expansion dmvcframework dotenv can interpolate variables using posix variable expansion this is a valid env file bash env file mode dev db name dbhostname my product db dev the db username dbuser my user the db password in this example is read from an envvariable dbpassword xyz username db hostname dbhostname 127 0 0 1 user preferences user preferences path appdata email template this is a mode email template second template email line third template email line utilization delphi var dotenv newdotenv withstrategy tmvcdotenvpriority envthenfile useprofile prod build mmvars clear mmvars lines addstrings dotenv toarray links feel free to ask questions on the delphi mvc framework facebook group https www facebook com groups delphimvcframework | restful restful-api restful-webservices application-server native delphi serialization-library speed jsonwebtoken jwt-authentication delphimvcframework json-rpc api jwt rest object-pascal pascal | front_end |
Graph-LLM | exploring the potential of large language models llms in learning on graphs update the pt file for citeseer has some problems please use the latest version instead of the version inside small data zip this is the official code repository for our paper exploring the potential of large language models llms in learning on graphs https arxiv org abs 2307 03393 introduction learning on graphs has attracted immense attention due to its wide real world applications the most popular pipeline for learning on graphs with textual node attributes primarily relies on graph neural networks gnns and utilizes shallow text embedding as initial node representations which has limitations in general knowledge and profound semantic understanding in recent years large language models llms have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data in this paper we aim to explore the potential of llms in graph machine learning especially the node classification task and investigate two possible pipelines llms as enhancers and llms as predictors the former leverages llms to enhance nodes text attributes with their massive knowledge and then generate predictions through gnns the latter attempts to directly employ llms as standalone predictors we conduct comprehensive and systematical studies on these two pipelines under various settings from comprehensive empirical results we make original observations and find new insights that open new possibilities and suggest promising directions to leverage llms for learning on graphs we provide the implementation of the following pipelines llms as predictors check ego graph py and directly use chatgpt to do zero shot few shot predictions llms as predictors https github com currytang graph llm blob master imgs llm as predictor png llms as enhancers check baseline py various kinds of embedding visible llms like llama sentencebert or text ada embedding 002 can be used to generate embeddings as node features llms as enhancers https github com currytang graph llm blob master imgs llm as enhancer png new project llms as annotators check out our new project here label free node classification on graphs with large language models llms https github com currytang llmgnn citation article chen2023exploringtp title exploring the potential of large language models llms in learning on graphs author zhikai chen and haitao mao and hang li and wei jin and haifang wen and xiaochi wei and shuaiqiang wang and dawei yin and wenqi fan and hui liu and jiliang tang journal arxiv year 2023 volume abs 2307 03393 0 environment setup package installation assume your cuda version is 11 8 conda create name llmgnn python 3 10 conda activate llmgnn conda install pytorch 2 0 0 cudatoolkit 11 8 c pytorch conda install c pyg pytorch sparse conda install c pyg pytorch scatter conda install c pyg pytorch cluster conda install c pyg pyg pip install ogb conda install c dglteam label cu118 dgl pip install transformers pip install upgrade accelerate pip install openai pip install langchain pip install gensim pip install google generativeai pip install u sentence transformers pip install editdistance pip install instructorembedding pip install optuna pip install tiktoken pip install pytorch warmup dataset we have provided the processed datasets via the following google drive link https drive google com drive folders 1 lana6esq6m5td2lvsep3il9qf6bc1kv usp sharing to unzip the files you need to 1 unzip the small data zip into preprocessed data new 2 if you want to use ogbn products unzip big data zip info preprocessed data new 3 download and move explanation pt and pl pt into preprocessed data new these files are related to tape 4 unzip the ada zip into 5 move entity pt into 5 put ogb arxiv csv into preprocessed data get ft and no ft lm embeddings refer to the following scripts bash for setting in random do for data in cora pubmed do wandb disabled true cuda visible devices 3 python3 lmfinetune py dataset data split setting batch size 9 label smoothing 0 3 seed num 5 wandb disabled true cuda visible devices 3 python3 lmfinetune py dataset data split setting batch size 9 label smoothing 0 3 seed num 5 use explanation 1 done done generate pt files for all data formats run python python3 generate pyg data py 1 experiments for llm as enhancers for feature level llm as enhancers you may replicate the experiments using files baseline py and lmfinetune py for example you may run param sweep with the following script bash for model in gcn gat mlp do for data in cora pubmed do for setting in random do add more formats here for format in ft do cuda visible devices 1 python3 baseline py model name model seed num 5 sweep round 40 mode sweep dataset data split setting data format format echo model data setting format done done done done done run with a specific group of hyperparameters bash python3 baseline py data format sbert split random dataset pubmed lr 0 01 seed num 5 feature ensemble separate each ensemble format with bash cuda visible devices 1 python3 baseline py model name gcn num split 1 seed num 5 sweep split 1 sweep round 5 mode sweep dataset pubmed split random ensemble string sbert know sep sb ft pl know exp ft batch version for ogbn products bash cuda visible devices 7 python3 baseline py model name sage epochs 10 num split 1 batchify 1 dataset products split fixed data format ft normalize 1 norm batchnorm mode main lr 0 003 dropout 0 5 weight decay 0 hidden dimension 256 num layers 3 to replicate the results for revgat you need to first run once with the default features to generate the dgl data bash python dgl main py data root dir dgldata pretrain path preprocessed data new arxiv fixed sbert pt use norm use labels n label iters 1 no attn dst edge drop 0 3 input drop 0 25 n layers 2 dropout 0 75 n hidden 256 save kd backbone rev group 2 mode teacher python dgl main py data root dir dgldata pretrain path preprocessed data new arxiv fixed sbert pt use norm use labels n label iters 1 no attn dst edge drop 0 3 input drop 0 25 n layers 2 dropout 0 75 n hidden 256 save kd backbone rev group 2 mode student alpha 0 95 temp 0 7 to replicate the results for sagn https github com thudm scr tree main ogbn products and glem https github com andyjzhao glem you may check their repositories and put the processed pt file into their pipelines 2 experiments for llm as predictors just run bash python3 ego graph py 3 update further experiments on ood prompts in two recent studies titled can llms effectively leverage graph structural information when and why https arxiv org pdf 2309 16595 pdf and explanations as features llm based features for text attributed graphs https arxiv org pdf 2305 19523 researchers probed a specific prompt tailored for the arxiv dataset containing data from post 2023 data which chatgpt s pre training corpus doesn t cover notably the results showed no decline in performance compared to the original dataset this intriguing outcome prompts us to delve deeper into creating efficacious prompts across varied domains out of distribution ood generalization commonly known as graph ood is a fervent area of discussion recent benchmarks such as good https github com divelab good tree goodv1 good indicate that gnns don t fare well during structural and feature shifts we embarked on an experiment using the arxiv dataset to assess the potential of llms as predictors leveraging a prompt from explanations as features llm based features for text attributed graphs https arxiv org pdf 2305 19523 which exhibited superior performance all avg val test best baseline test concept degree 73 91 0 63 73 01 72 79 63 00 covariate degree 75 75 3 6 70 23 68 21 59 08 concept time 74 29 0 96 72 66 71 98 67 45 covariate time 72 69 1 53 74 28 74 37 71 34 concept shift where p y x varies yet its construct remains anchored to covariate shift by adjusting the ratios in each domain covariate shift while p x shifts p y x remains consistent for the covariate shift there are configurations of 10 1 1 environments train val test and for the concept shift it s 3 1 1 train val test the term all avg represents the mean performance across all environments one discernible merit of using llms as predictors is their heightened resilience to ood shifts | ai |
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carbon-flutter | flutter carbon cover md assets cover png carbon is ibm s open source design system for products and experiences with the ibm design language as its foundation the system consists of working code design tools and resources human interface guidelines and a vibrant community of contributors the goals of the design system include improving ui consistency and quality making the design and development process more efficient and focused establishing a shared vocabulary between designer and developer and providing clear discoverable guidance around design and development best practices carbon flutter flutter carbon is an unofficial implementation of carbon design system using flutter framework it provides flutter developers a collection of widgets to build apps and user interfaces adopting the package enables developers to use consistent styles and behavior in prototype and production work documentation the full documentation of the project can be found here https nour eldin shobier gitbook io carbon flutter screenshots toggle md assets toggle png overflow menu md assets overflow menu png textfield md assets textfield png breadcrumb md assets breadcrumb png notification md assets notification png forms md assets forms png buttons md assets buttons png h3 align center made with heart in egypt h3 | flutter carbon-design-system flutter-package carbon carbon-flutter | os |
pillars | for reasons of time and advancement in technologies especially js this project is no longer maintained thank you for your support and interest in this project we personally think this is a good example of simplifying js code in node for these purposes and hopefully we ll have enough time to come back to this idea or something similar this project has favored great relationships and has provided a lot of learning for its participants see you later 3 br br br br br br br br br br br br br br br license https img shields io badge license mit blue svg build status https img shields io travis pillarsjs pillars master svg https travis ci org pillarsjs pillars npm version https img shields io npm v pillars svg https www npmjs com package pillars npm downloads https img shields io npm dm pillars svg https www npmjs com package pillars pillars make it easy http pillarsjs com img pillars png http pillarsjs com welcome to pillars js a modular framework for web development in node js with a simple and modular approach lets you introduce in node js with a soft learning curve if you re an advanced js node developer get an organized and efficient environment pillars has a powerful state control that lets you manage your application in a completely new way current status closed deprecated and no longer maintained pillars js is still an beta version now we are writing reference test tutorials we are working on the english version of the reference and web site sorry for the inconvenience if you have a good knowledge of spanish and english would be a wonderful help to us your contribution the entire site pillarsjs com http pillarsjs com is available spanish in pillars docs https github com pillarsjs pillars docs repository please contact us for english speakers sample codes are available fully commented in english overview https github com pillarsjs pillars blob master examples overview app js it is a review of some of the basic features of the framework scope http negotiation send files with compression cache control and automatic ranges byte serving compatible and recovery downloads transparent memcache system parameterized paths and multi language file upload management integrated with formidable full http headers parsing with priority management for headers as accept and languages request management by gangway wrap for request and response objects of node js automatic control error in handlers cors managed by route and lots of other utilities automatic compression in response cookies management controllers and environment dynamic environment in runtime allows change the structure while application is running nestable controllers by path give a better functionality organization in routing trees plugins allow expand controllers possibilities and modifying the system operation named controllers give organization and control over the environment inheritance system for controllers trees internationalization full i18n integration translations based in nodes attached to each message context and adapts the translation in each case from translation sheets translation sheets in js json cases functions and printf allowed language management of request automatic utils templated adds support to any template engine and cache managed centrally json decycled great utility for parse objets avoid circular references and much more crier allow create log groups and managing associated warehouses to each one scheduled schedule automated tasks procedure manage async blocks in declarative way no more callbacks hell getting started spanish comenzando con pillarsjs https github com pillarsjs pillars wiki comenzando con pillars js licence mit licence | front_end |
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Recursive-neural-networks-TensorFlow | recursive neural networks tensorflow this repository contains the implementation of a single hidden layer recursive neural network implemented in python using tensorflow used the trained models for the task of positive negative sentiment analysis this code is the solution for the third programming assignment from cs224d deep learning for natural language processing stanford university | recursive-neural-networks tensorflow natural-language-processing deep-learning cs224d | ai |
Awesome-LLM4RS-Papers | awesome llm4rs papers this is a paper list about large language model enhanced recommender system it also contains some related works keywords recommendation system large language models welcome to open an issue or make a pull request survey a survey on large language models for recommendation arxiv 2023 paper https arxiv org abs 2305 19860 how can recommender systems benefit from large language models a survey arxiv 2023 paper https arxiv org abs 2306 05817 recommender systems in the era of large language models llms arxiv 2023 paper https arxiv org abs 2307 02046 paper list chat rec towards interactive and explainable llms augmented recommender system arxiv 2023 paper https arxiv org abs 2303 14524 gpt4rec a generative framework for personalized recommendation and user interests interpretation arxiv 2023 paper https arxiv org abs 2304 03879 tallrec an effective and efficient tuning framework to align large language model with recommendation recsys 2023 paper https arxiv org abs 2305 00447 code https github com sai990323 tallrec privacy preserving recommender systems with synthetic query generation using differentially private large language models arxiv 2023 paper https arxiv org abs 2305 05973 uncovering chatgpt s capabilities in recommender systems arxiv 2023 paper https arxiv org abs 2305 02182 code https github com rainym00d llm4rs recommendation as instruction following a large language model empowered recommendation approach arxiv 2023 paper https arxiv org abs 2305 07001 a first look at llm powered generative news recommendation arxiv 2023 paper https arxiv org abs 2305 06566 sparks of artificial general recommender agr early experiments with chatgpt arxiv 2023 paper https arxiv org abs 2305 04518 zero shot next item recommendation using large pretrained language models arxiv 2023 paper https arxiv org abs 2304 03153 code https github com agi edgerunners llm next item rec do llms understand user preferences evaluating llms on user rating prediction arxiv 2023 paper https arxiv org abs 2305 06474 large language models are zero shot rankers for recommender systems arxiv 2023 paper https arxiv org abs 2305 08845 code https github com rucaibox llmrank is chatgpt fair for recommendation evaluating fairness in large language model recommendation arxiv 2023 paper https arxiv org abs 2305 07609 code https github com jizhi zhang fairllm leveraging large language models in conversational recommender systems arxiv 2023 paper https arxiv org abs 2305 07961 rethinking the evaluation for conversational recommendation in the era of large language models arxiv 2023 paper https arxiv org abs 2305 13112 code https github com rucaibox ievalm crs palr personalization aware llms for recommendation arxiv 2023 paper https arxiv org abs 2305 07622 prompt tuning large language models on personalized aspect extraction for recommendations arxiv 2023 paper https arxiv org abs 2306 01475 a preliminary study of chatgpt on news recommendation personalization provider fairness fake news arxiv 2023 paper https arxiv org abs 2306 10702 towards open world recommendation with knowledge augmentation from large language models arxiv 2023 paper https arxiv org abs 2306 10933 large language model for generative recommendation arxiv 2023 paper https arxiv org abs 2307 00457 genrec large language model for generative recommendation arxiv 2023 paper https arxiv org abs 2307 00457 generative job recommendations with large language model arxiv 2023 paper https arxiv org abs 2307 02157 exploring large language model for graph data understanding in online job recommendations arxiv 2023 paper https arxiv org abs 2307 05722 large language models are competitive near cold start recommenders for language and item based preferences recsys 2023 paper https arxiv org abs 2307 14225 llm rec personalized recommendation via prompting large language models arxiv 2023 paper https arxiv org abs 2307 15780 a bi step grounding paradigm for large language models in recommendation systems arxiv 2023 paper https arxiv org abs 2308 08434 llmrec benchmarking large language models on recommendation task arxiv 2023 paper https arxiv org abs 2308 12241 code https github com williamliujl llmrec recmind large language model powered agent for recommendation arxiv 2023 paper https arxiv org abs 2308 14296 zero shot recommendations with pre trained large language models for multimodal nudging arxiv 2023 paper https arxiv org abs 2309 01026 prompt distillation for efficient llm based recommendation cikm 2023 paper https lileipisces github io files cikm23 pod paper pdf code https github com lileipisces pod large language models as zero shot conversational recommenders cikm 2023 paper https arxiv org abs 2308 10053 code https github com aaronheee llms as zero shot conversational recsys leveraging large language models llms to empower training free dataset condensation for content based recommendation arxiv 2023 paper https arxiv org abs 2310 09874 zero shot recommendations with pre trained large language models for multimodal nudging arxiv 2023 paper https arxiv org abs 2309 01026 agent when large language model based agent meets user behavior analysis a novel user simulation paradigm arxiv 2023 paper https arxiv org abs 2306 02552 on generative agents in recommendation arxiv 2023 paper https arxiv org abs 2310 10108 code https github com lehengthu agent4rec agentcf collaborative learning with autonomous language agents for recommender systems arxiv 2023 paper https arxiv org abs 2310 09233 perspective generative recommendation towards next generation recommender paradigm arxiv 2023 paper https arxiv org abs 2304 03516 where to go next for recommender systems id vs modality based recommender models revisited sigir 2023 paper https arxiv org pdf 2303 13835 pdf code https github com westlake repl idvs morec exploring the upper limits of text based collaborative filtering using large language models discoveries and insights arxiv 2023 paper https arxiv org pdf 2305 11700 pdf exploring adapter based transfer learning for recommender systems empirical studies and practical insights arxiv 2023 paper https arxiv org abs 2305 15036 is chatgpt a good recommender a preliminary study arxiv 2023 paper https arxiv org abs 2304 10149 evaluating chatgpt as a recommender system a rigorous approach arxiv 2023 paper https arxiv org abs 2309 03613 universal representation learning github repository universal user representations for recommendation link https github com fajieyuan universal user representation parameter efficient transfer from sequential behaviors for user modeling and recommendation sigir 2020 paper https arxiv org abs 2001 04253 code https github com fajieyuan sigir2020 peterrec one person one model one world learning continual user representation without forgetting sigir 2021 paper https arxiv org pdf 2009 13724 pdf code https github com fajieyuan sigir2021 conure id agnostic user behavior pre training for sequential recommendation ccir 2022 paper https arxiv org abs 2206 02323 towards universal sequence representation learning for recommender systems kdd 2022 paper https arxiv org abs 2206 05941 code https github com rucaibox unisrec transrec learning transferable recommendation from mixture of modality feedback arxiv 2022 paper https arxiv org abs 2206 06190 learning vector quantized item representation for transferable sequential recommenders www 2023 paper https arxiv org abs 2210 12316 code https github com rucaibox vq rec one4all user representation for recommender systems in e commerce arvix 2021 paper https arxiv org abs 2106 00573 text is all you need learning language representations for sequential recommendation kdd 2023 paper https arxiv org abs 2305 13731 generative retrieval recommender systems with generative retrieval arvix 2023 paper https arxiv org abs 2305 05065 generative sequential recommendation with gptrec sigir 2023 workshop paper https arxiv org abs 2306 11114 pretrain language model and prompt learning survey paper pre train prompt and recommendation a comprehensive survey of language modelling paradigm adaptations in recommender systems arxiv 2023 paper https arxiv org abs 2302 03735 recommendation as language processing rlp a unified pretrain personalized prompt predict paradigm p5 arvix 2022 paper https arxiv org abs 2203 13366 code https github com jeykigung p5 rethinking reinforcement learning for recommendation a prompt perspective sigir 2022 paper https arxiv org abs 2206 07353 m6 rec generative pretrained language models are open ended recommender systems arvix 2022 paper https arxiv org abs 2205 08084 personalized prompt for sequential recommendation arvix 2022 paper https arxiv org abs 2205 09666 knowledge prompt tuning for sequential recommendation acm mm 2023 paper https arxiv org abs 2308 08459 code https github com zhaijianyang kp4sr dataset amazon m2 a multilingual multi locale shopping session dataset for recommendation and text generation arvix 2023 paper https arxiv org abs 2307 09688 kdd cup 2023 https kddcup23 github io pixelrec an image dataset for benchmarking recommender systems with raw pixels arvix 2023 paper https arxiv org abs 2309 06789 link https github com westlake repl pixelrec | large-language-models llm papers pre-training prompt-tuning recommender-system survey awesome-list gpt | ai |
LLMSurveySummary | llm survey summary a collection of survey papers and resources related to large language models llms table of contents llm survey summary llm survey summary table of contents table of contents collection of llms survey collection of llms survey acknowledgments acknowledgments update log update log collection of llms survey table class tg thead tr th class tg nrix align center rowspan 2 category th th class tg baqh align center rowspan 2 title th th class tg 0lax align center rowspan 2 venue th th class tg baqh align center rowspan 2 release date th th class tg 0lax align center rowspan 2 links th tr tr tr thead tbody tr td class tg nrix align center rowspan 9 comprehensive td td class tg baqh align center a comprehensive survey of ai generated content aigc a history of generative ai from gan to chatgpt td td class tg 0lax align center arxiv td td class tg baqh align center 7 mar 2023 td td class tg 0lax align center a href https arxiv org abs 2303 04226 paper a td tr tr td class tg baqh align center language model behavior a comprehensive survey td td class tg 0lax align center arxiv td td class tg baqh align center 20 mar 2023 td td class tg 0lax align center a href https arxiv org abs 2303 11504 paper a td tr tr td class tg baqh align center a survey of large language models td td class tg 0lax align center arxiv td td class tg baqh align center 31 mar 2023 td td class tg 0lax align center a href https arxiv org abs 2303 18223 paper a td tr tr td class tg baqh align center one small step for generative ai one giant leap for agi a complete survey on chatgpt in aigc era td td class tg 0lax align center arxiv td td class tg baqh align center 4 apr 2023 td td class tg 0lax align center a href https arxiv org abs 2304 06488 paper a td tr tr td class tg baqh align center summary of chatgpt related research and perspective towards the future of large language models td td class tg 0lax align center arxiv td td class tg baqh align center 4 apr 2023 td td class tg 0lax align center a href https arxiv org abs 2304 01852 paper a td tr tr td class tg baqh align center harnessing the power of llms in practice a survey on chatgpt and beyond td td class tg 0lax align center arxiv td td class tg baqh align center 26 apr 2023 td td class tg 0lax align center a href https arxiv org abs 2304 13712 paper a td tr tr td class tg baqh align center domain specialization as the key to make large language models disruptive a comprehensive survey td td class tg 0lax align center arxiv td td class tg baqh align center 30 may 2023 td td class tg 0lax align center a href https arxiv org abs 2305 18703 paper a td tr tr td class tg baqh align center large language models a comprehensive survey of its applications challenges limitations and future prospects td td class tg 0lax align center techrxiv td td class tg baqh align center 10 jul 2023 td td class tg 0lax align center a href https www techrxiv org articles preprint a survey on large language models applications challenges limitations and practical usage 23589741 paper a td tr tr td class tg baqh align center a comprehensive overview of large language models td td class tg 0lax align center arxiv td td class tg baqh align center 12 jul 2023 td td class tg 0lax align center a href https arxiv org abs 2307 06435 paper a td tr tr td class tg nrix align center rowspan 9 trustworthy td td class tg baqh align center survey of hallucination in natural language generation td td class tg 0lax align center acm td td class tg baqh align center 3 mar 2023 td td class tg 0lax align center a href https dl acm org doi 10 1145 3571730 paper a td tr tr td class tg baqh align center a survey of safety and trustworthiness of large language models through the lens of verification and validation td td class tg 0lax align center arxiv td td class tg baqh align center 19 may 2023 td td class tg 0lax align center a href https arxiv org abs 2305 11391 paper a td tr tr td class tg baqh align center tricking llms into disobedience understanding analyzing and preventing jailbreaks td td class tg 0lax align center arxiv td td class tg baqh align center 24 may 2023 td td class tg 0lax align center a href https arxiv org abs 2305 14965 paper a td tr tr td class tg baqh align center trustworthy llms a survey and guideline for evaluating large language models alignment td td class tg 0lax align center arxiv td td class tg baqh align center 10 aug 2023 td td class tg 0lax align center a href https arxiv org abs 2308 05374 paper a td tr tr td class tg baqh align center a survey on fairness in large language models td td class tg 0lax align center arxiv td td class tg baqh align center 20 aug 2023 td td class tg 0lax align center a href https arxiv org abs 2308 10149 paper a td tr tr td class tg baqh align center bias and fairness in large language models a survey td td class tg 0lax align center arxiv td td class tg baqh align center 2 sep 2023 td td class tg 0lax align center a href https arxiv org abs 2309 00770 paper a td tr tr td class tg baqh align center explainability for large language models a survey td td class tg 0lax align center arxiv td td class tg baqh align center 2 sep 2023 td td class tg 0lax align center a href https arxiv org abs 2309 01029 paper a td tr tr td class tg baqh align center siren s song in the ai ocean a survey on hallucination in large language models td td class tg 0lax align center arxiv td td class tg baqh align center 3 sep 2023 td td class tg 0lax align center a href https arxiv org abs 2309 01219 paper a td tr tr td class tg baqh align center a survey of hallucination in large foundation models td td class tg 0lax align center arxiv td td class tg baqh align center 12 sep 2023 td td class tg 0lax align center a href https arxiv org abs 2309 05922 paper a td tr tr td class tg nrix align center rowspan 4 multi modal br pre training td td class tg baqh align center a comprehensive survey on pretrained foundation models a history from bert to chatgpt td td class tg 0lax align center arxiv td td class tg baqh align center 18 feb 2023 td td class tg 0lax align center a href https arxiv org abs 2302 09419 paper a td tr tr td class tg baqh align center the contribution of knowledge in visiolinguistic learning a survey on tasks and challenges td td class tg 0lax align center arxiv td td class tg baqh align center 4 mar 2023 td td class tg 0lax align center a href https arxiv org abs 2303 02411 paper a td tr tr td class tg baqh align center a survey on multimodal large language models td td class tg 0lax align center arxiv td td class tg baqh align center 23 jun 2023 td td class tg 0lax align center a href https arxiv org abs 2306 13549 paper a td tr tr td class tg baqh align center examining user friendly and open sourced large gpt models a survey on language multimodal and scientific gpt models td td class tg 0lax align center arxiv td td class tg baqh align center 27 aug 2023 td td class tg 0lax align center a href https arxiv org abs 2308 14149 paper a td tr tr td class tg nrix align center rowspan 3 bioinformatics td td class tg baqh align center the shaky foundations of clinical foundation models a survey of large language models and foundation models for emrs td td class tg 0lax align center arxiv td td class tg baqh align center 22 mar 2023 td td class tg 0lax align center a href https arxiv org abs 2303 12961 paper a td tr tr td class tg baqh align center large language models in medicine td td class tg 0lax align center nature medicine td td class tg baqh align center align center 17 july 2023 td td class tg 0lax align center a href https www nature com articles s41591 023 02448 8 paper a td tr tr td class tg baqh align center a survey of large language models for healthcare from data technology and applications to accountability and ethics td td class tg 0lax align center arxiv td td class tg baqh align center align center 9 oct 2023 td td class tg 0lax align center a href https arxiv org abs 2310 05694 paper a td tr tr td class tg nrix align center rowspan 2 recsys br ir td td class tg baqh align center a survey on large language models for recommendation td td class tg 0lax align center arxiv td td class tg baqh align center 31 may 2023 td td class tg 0lax align center a href https arxiv org abs 2305 19860 paper a td tr tr td class tg baqh align center large language models for information retrieval a survey td td class tg 0lax align center arxiv td td class tg baqh align center 14 aug 2023 td td class tg 0lax align center a href https arxiv org abs 2308 07107 paper a td tr tr td class tg nrix align center rowspan 3 graphs td td class tg baqh align center unifying large language models and knowledge graphs a roadmap td td class tg 0lax align center arxiv td td class tg baqh align center 14 jun 2023 td td class tg 0lax align center a href https arxiv org abs 2306 08302 paper a td tr tr td class tg baqh align center chatgpt is not enough enhancing large language models with knowledge graphs for fact aware language modeling td td class tg 0lax align center arxiv td td class tg baqh align center 20 jun 2023 td td class tg 0lax align center a href https arxiv org abs 2306 11489 paper a td tr tr td class tg baqh align center large language models and knowledge graphs opportunities and challenges td td class tg 0lax align center arxiv td td class tg baqh align center 11 aug 2023 td td class tg 0lax align center a href https arxiv org abs 2308 06374 paper a td tr tr td class tg nrix align center rowspan 2 software engineering td td class tg baqh align center large language models for software engineering a systematic literature review td td class tg 0lax align center arxiv td td class tg baqh align center 21 aug 2023 td td class tg 0lax align center a href https arxiv org abs 2308 10620 paper a td tr tr td class tg baqh align center large language models for software engineering survey and open problems td td class tg 0lax align center arxiv td td class tg baqh align center 5 oct 2023 td td class tg 0lax align center a href https arxiv org abs 2310 03533 paper a td tr tr td class tg nrix align center rowspan 2 robotics td td class tg baqh align center a survey on large language model based autonomous agents td td class tg 0lax align center arxiv td td class tg baqh align center 22 aug 2023 td td class tg 0lax align center a href https arxiv org abs 2308 11432 paper a td tr tr td class tg baqh align center the rise and potential of large language model based agents a survey td td class tg 0lax align center arxiv td td class tg baqh align center 14 sep 2023 td td class tg 0lax align center a href https arxiv org abs 2309 07864 paper a td tr tr td class tg nrix align center rowspan 2 prompting td td class tg baqh align center pre train prompt and predict a systematic survey of prompting methods in natural language processing td td class tg 0lax align center arxiv td td class tg baqh align center 28 jul 2021 td td class tg 0lax align center a href https arxiv org abs 2107 13586 paper a td tr tr td class tg baqh align center a survey on in context learning td td class tg 0lax align center arxiv td td class tg baqh align center 31 dec 2022 td td class tg 0lax align center a href https arxiv org abs 2301 00234 paper a td tr tr td class tg nrix align center rowspan 2 reasoning td td class tg baqh align center augmented language models a survey td td class tg 0lax align center arxiv td td class tg baqh align center 15 feb 2023 td td class tg 0lax align center a href https arxiv org abs 2302 07842 paper a td tr tr td class tg baqh align center towards reasoning in large language models a survey td td class tg 0lax align center acl td td class tg baqh align center jul 2023 td td class tg 0lax align center a href https aclanthology org 2023 findings acl 67 paper a td tr tr td class tg nrix align center rowspan 6 others td td class tg baqh align center the science of detecting llm generated texts td td class tg 0lax align center arxiv td td class tg baqh align center 4 feb 2023 td td class tg 0lax align center a href https arxiv org abs 2303 07205 paper a td tr tr td class tg baqh align center the life cycle of knowledge in big language models a survey td td class tg 0lax align center arxiv td td class tg baqh align center 14 mar 2023 td td class tg 0lax align center a href https arxiv org abs 2303 07616 paper a td tr tr td class tg baqh align center bridging the gap a survey on integrating human feedback for natural language generation td td class tg 0lax align center arxiv td td class tg baqh align center 1 may 2023 td td class tg 0lax align center a href https arxiv org abs 2305 00955 paper a td tr tr td class tg baqh align center large language models meet nl2code a survey td td class tg 0lax align center arxiv td td class tg baqh align center jul 2023 td td class tg 0lax align center a href https aclanthology org 2023 acl long 411 paper a td tr tr td class tg baqh align center a survey on evaluation of large language models td td class tg 0lax align center arxiv td td class tg baqh align center 6 jul 2023 td td class tg 0lax align center a href https arxiv org abs 2307 03109 paper a td tr tr td class tg baqh align center efficient methods for natural language processing a survey td td class tg 0lax align center acl td td class tg baqh align center 12 july 2023 td td class tg 0lax align center a href https direct mit edu tacl article doi 10 1162 tacl a 00577 116725 paper a td tr tbody table acknowledgments update log 2023 10 04 released the first draft of the survey summary for llms 2023 10 07 added three survey papers about llms for graphs 2023 10 10 added one survey paper about llms for healthcare 2023 10 19 added two survey papers about llms for software engineering | ai |
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Awesome-Differential-Privacy | differential privacy differential privacy learning and integration 1 intuitive explanation differential privacy explaining https aircloak com explaining differential privacy differential privacy simply explained https www youtube com watch v gi0wk1cxlsq differential privacy a primer for a non technical audience http www jetlaw org journal archives volume 21 volume 21 issue 1 differential privacy a primer for a non technical audience differential privacy introduction reading not so much mathematics but intuition https desfontain es privacy laplacian noisy counting mechanism illustratioin https georgianpartners shinyapps io interactive counting 2 academic paper 2 1 survey the algorithm foundation of differential privacy the algorithm foundation of differential privacy https www cis upenn edu aaroth papers privacybook pdf the algorithmic foundations of privacy reading community https github com aceeviliano differential privacy explained differential privacy and application differential privacy and application http dro deakin edu au eserv du 30067556 zhu differential 2014a pdf differential privacy and application reading notes https github com billy1900 differential privacy blob master differential 20privacy 20and 20its 20application pdf the complexity of differential privacy https privacytools seas harvard edu files privacytools files complexityprivacy 1 pdf http www jos org cn jos ch reader create pdf aspx file no 6052 year id 2020 quarter id 7 falg 1 differentially private data publishing and analysis a survey https ieeexplore ieee org document 7911185 repository of paper on differential privacy https github com billy1900 differential privacy blob master collection of papers md paper of differential privacy in ccs s p ndss usenix infocom https github com billy1900 awesome differential privacy blob master paper survey md sok differential privacies https arxiv org pdf 1906 01337 pdf 2 2 course seminar on differential privacy fall 19 20 http www cs tau ac il haimk privacy seminar main page html cse 660 fall 2017 http cs people bu edu gaboardi teaching cse660 fall17 html cs295 data privacy https github com jnear cs295 data privacy privacy study group https uvasrg github io privacy cs 860 algorithms for private data analysis fall 2020 http www gautamkamath com cs860 fa2020 html 2 3 some mechanisms concentrated differential privacy simplifications extensions and lower bounds https arxiv org pdf 1605 02065 pdf 2 4 differential privacy in ccs s p ndss usenix infocom from 2015 2019 some of them are from 2020 survey https github com billy1900 awesome differential privacy blob d05b148f9fa8df8fb38fe77dd3270f9bc29e4147 paper survey md 3 video recent development in differential privacy ii https www youtube com watch v 3epnki2l 20 recent development in differential privacy i https www youtube com watch v pwugfhkfoo0 privacy amplification by sampling and renyi differential privacy https www youtube com watch v 0mavz0yk5e4 differential privacy from theory to practice https www youtube com playlist list pl8vt 7csfnw1li73yxzdtaiaexfkmwwrh 4 code 4 0 dp https zhuanlan zhihu com p 67761743 4 1 k anonymity algorithm https github com billy1900 differential privacy tree master k anonymization algo 4 2 randomized response http ceur ws org vol 1558 paper35 pdf 4 3 laplace and exponential mechanism https github com billy1900 differential privacy tree master laplace 26exponetial 4 4 gaussian mechanism https github com billy1900 differential privacy tree master gaussian 4 5 google differential privacy library https github com google differential privacy 4 6 ibm differential privacy library https github com ibm differential privacy library 4 7 facebook pytorch dp train pytorch models with differential privacy https github com facebookresearch pytorch dp 4 8 differential privacy federated learning https github com gitgik differential privacy federated learning 4 9 pysyft a library for encrypted privacy preserving machine learning https github com openmined pysyft 4 10 pygrid a peer to peer platform for secure privacy preserving decentralized data science https github com openmined pygrid 4 11 pyvacy privacy algorithms for pytorch https github com chriswaites pyvacy 4 12 dp xgboost a dp fork of the famous scalable ml engine https github com sarus tech dp xgboost | differential-privacy differential-privacy-learning privacy-preserving differentially-private dpsgd differential-privacies survey | ai |
web-design-development-fundamentals-tutorial | web design development foundation web technology fundamentals we use the internet for various purposes like banking shopping ticket booking music entertainment and social media but do you know how the web works here i introduce the technology that makes the web run the terminology involved and how it all comes together to power the websites we know and love will cover various terms technologies involved like html css javascript url dns http and explains what separates the front end what we see from the back end what we don t on the web databases servers and the technology that makes the web work topics include 1 course introduction section 1 course introduction 2 introduction to web world section 2 introduction to web world 3 introduction to front end technology section 3 introduction to front end technology 4 introduction to back end technology section 4 introduction to back end technology 5 other supporting technologies section 5 other supporting technologies 6 what s the next step section 6 whats the next step section 1 course introduction 1 1 welcome hi all i m dinanath jayaswal senior ui web developer and adobe certified expert professional i wanna welcome you to web design development foundation web technology fundamentals this content is designed to introduce the fundamental concepts surrounding terminology technology techniques terms that are used to create and run the web for anyone new to the web the vast number of terms abbreviations and processes involved can be a little bit overwhelming disturbing it s my goal to explain in the core basic terms how the web works client servers browsers front end back end and various terminology involved also how the technology all fits together this course contents roadmap to becoming a web designer developer 1 2 who is this for this course is for every web internet user fresher as well as an experienced web designer developer who wants to learn brush up cover up various terms techniques acronyms abbreviations used in web internet design development world section 2 introduction to web world 2 1 how does the web work the internet massive networks of millions of computers all over the world that use to share and transmit information through the following protocols mediums email sms messaging application data world wide web world wide web www runs over the internet transfer data using http hypertext transfer protocol the web communication series of interactions between clients servers client devices that request and render web content some of the popular clients are browsers mobile apps screen reader devices etc server applications that deliver web contents services to clients 2 2 client server interaction model browser the browser is software application that helps to visit view accessing information and make take the best use of websites web pages popular browsers are google chrome mozilla firefox internet explorer safari opera p figure nbsp nbsp nbsp img src examples web design development images browsers all 2 png alt widely used modern browsers image title widely used modern browsers width 400 border 2 figcaption nbsp nbsp nbsp image widely used modern browsers figcaption figure p request response mechanism syntax example 1 user type in url uniform resource locator like http www yahoo com 2 url passed to dns domain name server 3 dns translates url into ip internet protocol address 172 16 0 0 4 browser use dns to locate host server and send request of content in the form of html page images css javascript p figure nbsp nbsp nbsp img src examples web design development images client server request response png title client server request response mechanism alt client server request response mechanism border 2 figcaption nbsp nbsp nbsp image client server request response mechanism figcaption figure p static sites simply shows content which is not change frequently requested by the client without any additional processing static sites pages usually consists of front end technologies like html css javascript dynamic sites first business logic execute by web applications like asp php ruby on rails etc than browser shows content latest updated requested by the client dynamic sites pages usually consists of front end technologies like html css javascript in combination with some back end technology like asp php ruby on rails etc 2 3 dns domain name server every site have unique ip internet protocol address used to identify location http www google com 169 20 127 243 dns domain name server is like internets phone book which stores unique website and its ip internet protocol address for each and every website ip address domain name server 172 16 0 0 domain name http www yahoo com or so 2 4 internet protocol protocols are simple languages standards set of rules through which computers share the information which each other internet wouldn t work at all without protocols different multiple layers of protocols application layer creation sharing of data over the web http hyper text transfer protocol ftp file transfer protocol smtp simple mail transfer protocol dns domain name server rip routing information protocol snmp simple network management protocol transport layer communication tcp transmission control protocol and internet protocol created in 1973 udp user datagram protocol internet layer address and routing structure of data ipv4 internet protocol version 4 ipv6 internet protocol version 6 2 5 http hypertext transfer protocol standard protocol for transferring data resources over the web in the case of http browsers are http clients send http requests and servers are http servers send the http response to transfer data with a standardized format http is a one way stateless protocol it simply means that once the request is send received then that state is forgotten ie discarded by the browser and web server which makes http very much simple and efficient due to its one way stateless nature it s difficult to send and receive or upload and download large files with http some common web protocols ftp file transfer protocol typically used to transfer large files and its ideal for uploading or downloading files to your site smtp simple mail transfer protocol the standard web protocol for sending email across the web most email clients use smtp for sending and receiving emails pop post office protocol pop is typically used to receive emails rtp real time transfer protocol standard protocol for delivering serving audio or video over the web its commonly used in voice over and chat applications rtmp real time messaging protocol developed by macromedia adobe corp for streaming audio video and data over the internet between a flash player and a server https hypertext transfer protocol secure http layered with a security protocol usually used for e commerce or other secure transactions payment gateways 2 6 url uniform resource locator url uniform resource locator or universal resource locator specifies its location on a computer network web address web path web site name anatomy contains of url url http www yahoo com br url https www yahoo com url parts meaning description http http https https protocol www yahoo com 8080 resource name www sub domain most cases sub domain is not required ie you can type http yahoo com or https yahoo com yahoo domain name com top level domain url path port http www yahoo com products product1 html or https www yahoo com products product1 html http www yahoo com 80 80 port number 80 is default port top level domain com net org info 2 7 how browser works the browser uses http to communicate with the web server and request pages content browsers rendering engine translates renders pages and displays the contents as per support why browsers behave differently browsers developed independently of each other multiple rendering engines drive page layout dom document object model support varies browsers use independent javascript engines common rendering engines engine browser trident internet explorer aol america online windows mobile presto opera gecko mozilla ff camino webkit safari google chrome webos blink google chrome opera 14 2 8 w3c and web standards world wide web consortium w3c world wide web consortium is the main international standards organization for the world wide web help in developing protocols and guidelines that ensure long term growth for the web w3c world wide web consortium started in the year 1994 to issue recommendations for web technologies check web standards for consistent design the web standard project https www webstandards org w3c website https www w3 org 2 9 web server websites are hosted uploaded on a web server a web server is nothing more than a normal computer installed with specialized software and components for a specific use web stacks are groups of software that work with each other to build and process websites static dynamic websites 2 10 web stacks web stacks consist of os operating system web server database server programming language common web stacks web stack full form technologies lamp linux apache mysql php linux apache mysql perl python mamp macintosh apache mysql php wamp windows apache mysql php wisa windows iis ms sql net mars mysql apache ruby solaris section 3 introduction to front end technology 3 1 front end design client side front end client side design refers to the visual part user interface layer what we see of websites applications front end design is typically the ui layout typography fonts images and many other visual elements aspects shown on the web page and their styling one can also refer to it as usability and user experience design as well 3 pillars core languages technologies used for frond end web design development 1 html hypertext markup language markup language essential page structure content readable and convey structure to the user text layout model page mark up text tags data details for pages images tables anchor links forms 2 css cascading style sheet style sheet language page design presentation layouts styling formattings look and feel creative part of web pages 3 javascript js scripting language dynamic page behaviour logics conditions and validations events interactivity with user dynamic updates in a web page front end design web design web design is creating and designing web pages designing for all user interfaces experiences like browsers screen readers mobiles printers front end design development includes the visual part of web design planning structuring content data pages user interface designing client side designing interactivity front end designer role includes visual part of web designing creating mockups and developing visual standards ui designing typography page layout structuring semantic content data pages mainly works with html css photoshop front end developer role can act and do the part of front end designer planning for dynamic content logical conditional interactivity fetch data from servers with apis mainly works with javascript and other js frameworks html css web designing workflow determine web site clients audience goals clearly define contest navigation content organization wireframing architecting the site proper navigation content organization generate mockups post it illustrator photoshop fireworks build it html css js flash test it multiple browsers devices consistent output interactive design the creation and integration of feature rich interfaces organize content and flow properly plan navigation use video and blended media client side scripting script executed in the browser environment javascript p figure img src examples web design development images html5 png alt html5 hypertext markup language logo title html5 hypertext markup language border 2 align right figure p 3 2 html hypertext markup language html hypertext markup language the standard foundation gateway language used for creating structuring content on the web we can create a static website by html only no html no web page history of html year history 1990 specification drafted in mid 90s by tim berners lee 1991 tim berners lee published document called html tags 1995 html 2 0 standard html 1997 html 3 2 proprietary browser companies developed many features 1999 html 4 0 w3c web standards support 2000 xhtml 1 0 html move towards xml extensible markup language 2001 2009 xhtml 2 0 2004 whatwg formed web hypertext application technology working group they continued with development of html 5 0 2007 2008 w3c adopts whatwg s html5 web application 1 0 specification and drafts 2009 xhtml 2 0 drops stops 2014 w3c recommendation html5 3 3 html page structuring html syntax is very simple easy to learn html page consists of dtd tags for different visual aspects basic html page structure syntax example html doctype html html lang en head meta charset utf 8 meta name viewport content width device width initial scale 1 0 meta http equiv x ua compatible content ie edge title basic html title head body p this is basic html page p body html dtd document type declaration or document type definition usually the first line of an html page its an instruction to the web browser about what version of html the page is written and rules to parse render html version dtd html5 document type declaration lt doctype html gt xhtml document type definition dtd file lt doc type html public w3c dtd xhtml 1 0 strict en http www w3 org tr xhtml1 dtd xhtml1 strict dtd gt html4 document type definition dtd file lt doctype html public w3c dtd html 4 01 transitional en http www w3 org tr html4 loose dtd gt tags tags are html commands set of instructions to create web page with specific ui element tags are used to identify content like h1 heading 1 p paragraph etc html contains tags which are used to identify elements and structure pages example header table li img head tag consists of non visual elements meta tags links to external css js files and libraries body tag contents all visual elements which appears on web page tools for building web sites web pages writing html text editor html editor like notepad notepad sublimetext atom brackets coda visual studio code dreamweaver etc to view output of html pages we need browsers like google chrome mozilla firefox internet explorer safari etc p figure img src examples web design development images css3 png alt css cascading style sheets logo title css cascading style sheets border 2 align right figure p 3 4 css cascading style sheets css is presentational styling formatting language developed to control look and feel of html files presentational markup tags like font b i u etc discouraged or deprecated as style sheet language like css developed to control the presentation of html documents styles can be written in 3 different ways 1 external style style css separation of concern 2 embedded internal styles style style written inside style tag 3 inline styles p style color red font size 18px this is an inline style p one can write in each markup line with style attribute css selector selector declaration property value syntax example css p color red font size 18px 1 external style style css separation of concern index html syntax example html doctype html html lang en head meta charset utf 8 meta name viewport content width device width initial scale 1 0 meta http equiv x ua compatible content ie edge title external style title link href style css rel stylesheet type text css head body p this is an external style p body html style css syntax example css p color red font size 18px 2 embedded internal styles index html syntax example html doctype html html lang en head meta charset utf 8 meta name viewport content width device width initial scale 1 0 meta http equiv x ua compatible content ie edge title embedded internal styles title style p color red font size 18px style head body p this is an embedded internal styles p body html 3 inline styles index html syntax example html doctype html html lang en head meta charset utf 8 meta name viewport content width device width initial scale 1 0 meta http equiv x ua compatible content ie edge title inline style title head body p style color red font size 18px this is an inline style p body html p figure img src examples web design development images javascript logo 3 png alt javascript logo title javascript border 2 width 200 align right figure p 3 5 javascript created and introduced by brendan eich netscape in 1995 to increase and expand the capabilities of the native browser a client side scripting language used to add interactivity and functionality to websites javascript has nothing to do with java not at all related to java common usage of javascript build interactive elements rollover button clicks menus create dynamic menus logics open new browser windows update data within the page browser client side form validation manipulate page elements basic javascript page structure syntax example html doctype html html lang en head meta charset utf 8 meta name viewport content width device width initial scale 1 0 meta http equiv x ua compatible content ie edge title basic html javascript title script write javascript code here script head body p this is basic html javascript page p body html scripts can be written at different places 1 external script script js separation of concern 2 embedded internal script under head or body tag script script 1 external script script js separation of concern index html syntax example html doctype html html lang en head meta charset utf 8 meta name viewport content width device width initial scale 1 0 meta http equiv x ua compatible content ie edge title basic html javascript title script src script js script head body p this is basic html javascript page p body html script js syntax example javascript write javascript code here alert external javascript code executed 2 embedded internal script under head or body tag index html syntax example html doctype html html lang en head meta charset utf 8 meta name viewport content width device width initial scale 1 0 meta http equiv x ua compatible content ie edge title basic html javascript title script write javascript code here alert embedded internal javascript code executed script head body p this is basic html javascript page p script write javascript code here alert embedded internal javascript code executed script body html javascript frameworks libraries already have the various codes and common utilities ready collection of javascript methods and functions that make it easier to perform targeted tasks popular javascript frameworks libraries jquery angular react vue prototype ember d3 3 6 images there are many formats of photos pictures images web graphics used on the web bmp gif jpeg jpg png svg creating images for the web often requires the designer to find a balance between keeping the file sizes small while maintaining acceptable image quality with resolution jpeg jpg gif and png files type are the standard image formats for web graphics gif graphics interchange format introduced in 1987 by compuserve it is the most popular image format type on the web gif characteristics limited to 256 colors support only typically used for logos or icons gifs support transparency and limited animation jpeg jpg joint photographic experts group image compression standard established in 1992 and widely used for areas such as the web and digital photography often shortened to the file extension jpg jpeg jpg characteristics supports millions of colors used in all types of image high resolutions scenery uses lossy compression to reduce the file size jpgs are compressed each time they saved png portable network graphic created in 1995 to improve over jpeg and replace the gif by open source community over copyright disputes license issues with gif png characteristics like gifs support transparency uses lossless compression like jpeg supports millions of colors older browser support is lacking svg scalable vector graphics svg is a vector graphics format can be scaled independently of the resolution graphics are written in svg markup which makes them editable through the code since they are markup they can be further styled through css svg is now widely supported among the latest devices and browsers tools applications software used for creating graphics images adobe photoshop adobe illustrator adobe fireworks coreldraw gimp 3 7 api application programming interfaces an exposed set of functions that allow other applications to access features and functionality without giving direct access to source code widely used api examples using google maps weather reports youtube videos facebook feeds amazons latest offers latest sports scores latest newsfeeds 3 8 html5 apis html is used to create web pages websites with simple standard elements html5 introduced the following advanced apis to avoid third party plug in support dependency html5 advanced apis examples media api audio video control geo location api access current location of users drag and drop api drag and drop functionality app cache api storing data offline caching for future use canvas api draw directly in the browser 3 9 web fonts earlier we use to use fonts available and installed on user machines like arial verdana times sherif etc web fonts technology allows the downloading and temporary installation of fonts within the browser font face css technique is used to request any engaging and latest fonts typography from the web server new font formats developed eot embedded opentype woff web open font format famous third party font hosting server google fonts adobe typekit section 4 introduction to back end technology 4 1 server side scripting any programming scripting that runs on the web server is referred to as server side scripting it may be processing form data managing users booking a movie tickets travel hotel online purchase server side scripting languages cgi common gateway interface perl asp active server pages java jsp java server pages php hypertext preprocessor personal home page net cold fusion cfml nodejs 4 2 php in the year 1994 rasmus lerdorf has written a set of cgi scripts to track a visitor to his online resume he opens sources the scripts and named it personal home page php in 1997 developers named andi gutmans and zeev suraski rewrote the existing parser and released php 3 0 renaming it hypertext preprocessor as php was free and open source it attracted a wide community of developers php paired with other open source tools like linux apache and mysql php was very easy to learn so it became one of the most important and widely used back end server side scripting languages to build robust dynamic sites php is embedded within html but php asp cfm pages must process render on the server before it displays in browser note php asp cfm or any other server side scripting files will not run locally you just need some software like mamp wamp xampp to set create a server environment on local 4 3 popular server side languages jsp java server pages part of the larger java framework and used by java programmers scripts are combinations of xml and java scriptlets used in enterprise level sites but can be used for any sized sites or applications net vb net is referred to as net but its part of the larger net framework used in enterprise level sites or applications coldfusion adobe coldfusion coldfusion is added to the page through its own markup language called cfml much like html python powerful open source multi purpose development language ruby general purpose development language it is popular due to the ruby on rails web framework 4 4 dealing with data data used in websites applications must store in database for future uses and retrievals like user name password authentication details profile product list pricing and so on at the client side ie in the browser due to security purposes we can store a very less and limited amount of data dbms database management system store data in tables rdbms relational database management system store data in tables that relate to each other allows complex sorting and filtering of data mysql sybase oracle sqlite non relational database no sql database nosql database stores data in object driven datasets faster to index and scale easier than relational databases mongodb cassandra couchdb simpledb hbase 4 5 sql database structured query language the standard query language for managing and retrieving information from databases sql syntax is simple and logical easy to learn select insert update delete such simple keyword based syntax statements used to manipulate data 4 6 cms cms content management system an application to control creation management publishing and archiving sites content a good cms can speed up creation updating of content faster manage user groups controls content as per roles web based cms assist in the creation publishing and archiving of site content also provides advanced control over site functionality such as blogging community boards and e commerce how to choose a cms understand site goals and future needs match site focus to cms focus look for quality plug ins look for robust support compare budget to cms needs make sure site admin is simple does the cms allow custom layouts popular cms wordpress jhoomla drupal expression engine radiant www expressionengine com www madebyfrog com www drupal org www joomla org www movabletype org www radiantcms org www wordpress org 4 7 cdn content delivery network content distribution network a system for delivering content over a distributed network of servers as a developer no need to download all libraries locally and do development use the cdn path to include required libraries it will be cached and downloaded faster cdns are largely used to serve static resources such as javascript css libraries videos or other site dependencies popular cdns google cdn microsoft cdn 4 8 cloud services cloud computing usually refers to distributed processes over the internet dropbox store and share files projects youtube google cloud storage amazon web services microsoft cloud services rackspace heroku 4 9 github github is built around git an open source version control system github is an online distribution service that allows users to store repositories online that can be version controlled and shared with other users allows to manage the project publish files and store revisions sharing and collaborating section 5 other supporting technologies 5 1 javascript libraries collection of pre written methods functions features that make development faster and easier extends the functionality of native javascript maybe focused sets like date js moment js to jquery a broader task oriented library popular javascript frameworks libraries jquery angular react vue prototype ember d3 5 2 frameworks and boilerplates collection of prebuilt html css and javascript files designed to speed up make the development of sites easier html readymade templates semantic structure css ready typography layouts browser resets javascript enhanced features modal windows tooltips menus readymade functions and utilities examples bootstrap angular html shiv shim boilerplate a set of templates build around a specific starting point or goal provide a starting point for building sites or apps latest features need to work support in older browsers framework a collection of assets designed to help build sites or applications faster easier includes css grids javascript libraries or helpful scripts and html templates 5 3 css preprocessors css is a static stylesheet language css preprocessors help to write css more efficiently semantically with programming features like variables functions maths operations conditionals etc the scripting language that extends the functionality of css and must be compiled into native css code before publishing popular css preprocessors css preprocessors source created in file conversion sass written in ruby scss sass css less written in javascript less css stylus written in javascript styl css 5 4 xml extensible markup language a semantic markup language containing rules for defining document structure and data and transfer globally customized tags and widely used to share data between multiple applications syntax example xml xml version 1 0 studentdetails name test name rollnumber 001 rollnumber studentdetails common uses of xml rss feeds online contents ajax applications store or write data flash xml external apis associate technologies with xml xsl xslt xpath 5 5 ajax asynchronous javascript and xml not a new technology not a scripting or programming language it isn t anyone thing at all ajax is a new technique for creating better faster and more interactive interfaces or web applications with the help of xml html css and javascript document object model dom think of ajax as a new specific approach to web development that focuses on building interactive and engaging web experiences 5 6 rss feed rich site summary real simple syndication really simple syndication rss uses a family of standard web feed formats to publish frequently updated information like blog entries news headlines audio video an rss document called feed web feed or channel includes full or summarised text and metadata like publishing date and author s name rss is a standardized xml base format section 6 what s the next step congratulations you have completed the web design development foundation web technology fundamentals lesson thank you for looking into web fundamentals now you have a good idea of how the web works and the technologies behind the web your next step could be mastering html html5 the markup language let s begin your web design development journey with learning html5 essentials https github com dinanathsj29 html5 essentials best of luck happy learning | web-fundamentals web-design web-development html5 css3 javascript internet web-techonology html css html-css webtechnologies www world-wide-web ui-development rss-feed xml json | front_end |
VetTag | vettag introduction this is the official cleaned repo we used to train evaluate and interpret for vettag paper https www nature com articles s41746 019 0113 1 please feel free to contact yuhui zh15 mails tsinghua edu cn if you have any problem using these scripts usage unsupervised learning please create a json file in path to hypes with the following format json psvg json data dir path to data psvg encoder path path to data encoder json prefix psvg oneline label size 0 data dir and prefix save data in path to data psvg psvg oneline train tsv path to data psvg psvg oneline valid tsv and path to data psvg psvg oneline test tsv for training validation and test the file should only contain one line for the whole text encoder path save vocabulary in path to data encoder json it is a json file with format hello 0 world 1 label size for unsupervised learning label size should equal to 0 then use the following command to train and save the model in path to exp psvg python trainer py outputdir path to exp psvg train emb corpus psvg hypes path to hypes psvg json batch size 5 bptt size 600 model type transformer supervised learning please create a json file in path to hypes with the following format json csu json data dir path to data csu encoder path path to data encoder json prefix csu label size 4577 data dir and prefix save data in path to data csu csu train tsv path to data csu csu valid tsv and path to data csu csu test tsv for training validation and test the file contains lines of annotated clinical notes with format text tab label 1 space label 2 space space label k for each line encoder path save vocabulary in path to data encoder json the same file for unsupervised learning it is a json file with format hello 0 world 1 label size for supervised learning we use 4577 finegrained snomed diagnosis codes then use the following command to train and save the model in path to exp csu python trainer py outputdir path to exp csu corpus csu hypes path to hypes csu json batch size 5 model type transformer cut down len 600 train emb hierachical inputdir path to exp psvg pretrained model pickle external evaluation please create a json file in path to hypes with the following format json pp json data dir path to data pp encoder path path to data encoder json prefix pp label size 4577 data dir and prefix save data in path to data csu pp test tsv for test the file contains lines of annotated clinical notes with format text tab label 1 space label 2 space space label k for each line encoder path save vocabulary in path to data encoder json the same file for unsupervised learning it is a json file with format hello 0 world 1 label size for supervised learning we use 4577 finegrained snomed diagnosis codes the same for supervised learning then use the following command to evaluate the model python trainer py outputdir path to exp pp corpus pp hypes path to hypes pp json batch size 5 model type transformer cut down len 600 hierachical inputdir path to exp psvg pretrained model pickle statistics and analysis refer to jupyter snomed stat ipynb jupyter species stat ipynb jupyter length label distribution ipynb and jupyter analysis ipynb hierarchical training two files are required parents json and labels json in data dir labels json the format is snomed id 1 snomed id 2 snomed id 4577 which is all 4577 snomed labels we use parents json the format is snomed id i parent of snomed id i which is all snomed labels and their parents in the shortest path from the root node introduced in the method section interpretation refer to jupyter interpret ipynb and jupyter salient words ipynb | ai |
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IoT | p align center a href https github com ariel mn iot img src docs img dustbiniot png alt dustbin iot logo a p hr span align center document https img shields io badge docs pdf 9cf https montesariel com download en dustbin iot documentation pdf project study https img shields io badge project study informational https montesariel com portfolio project 1 web app heroku https img shields io badge web 20app heroku blueviolet https dustbin iot herokuapp com span br intelligent system for managing the refuse collection of a smart city the administrator can add modify or delete orders employees dustbins or new accesses to the web application each user sees his own orders and the level of filling of the dustbins to collect expired orders will not be displayed automatically technologies and requirements python 3 8 3 https www python org downloads postgresql as db pip install psycopg2 django as framework pip install django read the full list in the requirements txt https github com ariel mn iot blob master server requirements txt file br screenshot docs img screenshot 1 png br screenshot docs img screenshot 2 png br screenshot docs img screenshot 3 png | smartcity webapp django heroku python postgresql javascript | server |
BlockchainWallet-Crypto | https www jitpack io v tianqiujie blockchainwallet crypto svg https www jitpack io tianqiujie blockchainwallet crypto blockchainwallet crypto lssues pr todo list 1 bip38 1 bip44 addressindex m 44 60 0 0 0 | blockchain |
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clarity | note the clarity design system has moved and is now split into two repositories one for clarity angular https github com vmware clarity ng clarity and one for clarity core https github com vmware clarity core this repository is now for archived purposes only clarity logo png clarity design system build https github com vmware clarity workflows build badge svg clarity is an open source design system that brings together ux guidelines design resources and coding implementations with web components this repository includes everything you need to build customize test and deploy clarity for complete documentation visit the clarity website https clarity design if you are looking for clarity angular our previous implementation of clarity built in angular please see the angular branch https github com vmware clarity tree angular for the latest version there getting started clarity is published as five npm packages npm version core https img shields io npm v cds core latest label 40cds 2fcore style flat square https www npmjs com package cds core contains the web components that work in any javascript framework npm version cds angular https img shields io npm v cds angular latest label 40cds 2fangular style flat square https www npmjs com package cds angular contains shims for core usage in angular environment npm version cds react https img shields io npm v cds angular latest label 40cds 2freact style flat square https www npmjs com package cds react contains shims for core usage in react environment npm version clarity city https img shields io npm v cds city latest label 40cds 2fcity style flat square https www npmjs com package cds city our open source sans serif typeface installing clarity visit our documentation at https clarity design get started documentation for documentation on the clarity design system including a list of components and example usage see our website https clarity design contributing the clarity project team welcomes contributions from the community for more detailed information see our contribution guidances docs contributing md licenses the clarity design system is licensed under the mit license license feedback if you find a bug or want to request a new feature please open a github issue https github com vmware clarity issues include a link to the reproduction scenario you created by forking one of the clarity stackblitz templates for the version you are using at clarity stackblitz templates https stackblitz com clr team support for our support policies please visit https clarity design get started support for questions ideas or just reaching out to the team feel free to open a discussion in our github disscussion section https github com vmware clarity discussions | ux ui angular css typescript component-library design-system clarity lit-elements angular-components web web-components web-components-library components accessibility accessible-components accessible-design | os |
Project_CC | cloud computing project repository of the cloud computing course of the master in informatics engineering of the university of granada web site https akourts github io project cc description of the problem knowing at least one foreign language is a must have however many people have problems learning a new language or they are really shy and they are afraid to speak solution a simple and fun application will help people around the world learn the basics of a new language by providing information like simple everyday phrases a dictionary etc architecture the solution will be based in microservices architecture a nosql db will be used for this purpose python will be the main programming language flask is the selected framework a public rest api will be created plan provisioning of vm with ansible https github com akourts project cc tree master provision ansible there are many tools which can help with the provisioning of vms chef ansible puppet salt in this project ansible was selected as a smoother introduction to the world of provisioning as it seemed an easier and simpler choice comparing to the documentation of the rest for instance the only prerequisite is to have installed python which is omnipresent in linux distributions moreover ansible relies on yaml which is more friendly than for example json that chef uses automation of vm creation https github com akourts project cc tree master automation readme md via the new cli of azure a new vm was created automatically this selection was based on learning more things about azure tecnologies and functionalities ubuntu 16 04 was selected as an image for the vm as it is an arguably better choice for beginners to work with despliegue 40 68 116 145 orchestration of vm https github com akourts project cc tree master orquestacion readme md vagrant is the selected software for the orchestration of more than one virtual machines it is used to set up one or more virtual machines by importing pre made images called boxes setting vm specific settings ip address hostnames port forwarding memory etc running provisioning software like puppet or chef one of the positive aspects of vagrant is that provides a single config file to set multiple virtual machines up enabling to launch all of them with one command also there s a large number of boxes available at sites such as http vagrantbox es which is really helpful because anyone can try various oses or distributions applying the same provisioning to set up similar environments despliegue vagrant 13 95 106 124 use of containers https github com akourts project cc tree master contenedores readme md docker will be used as a container because it s easier to test applications against different versions of operating systems databases and scripting languages alpine makes a great docker container because it is so small and optimized to be run in ram it might also might make a good controller for several docker containers with enough ram also alpine image is much lighter than an ubuntu image and can be executed up to 3 times faster than the ubuntu one commands can be run through a configuration file called a dockerfile contenedor https appservicesandra azurewebsites net dockerhub https hub docker com r akourts project cc combination of the virtual infrastucture to deploy a concrete application using the flask framework a service in python was created which enables adding and displaying new words the application is addressed to spanish people allowing to store a spanish word and its translation in english so basically it serves like a personnal dictionary where the user can store the new words that learns every day the combination of all the infrastucture was acomplished with docker compose compose is a tool for defining and running multi container docker applications with compose you use a yaml file to configure your application s services then with a single command you create and start all the services from your configuration here it is used to create two containers one for the database and one for the api hito6 http akourtsdns eastus cloudapp azure com | cloud |
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rtx | rtx deterministic rtos from keil for arm cortex m devices with code under a bsd style license this code is a mirror and line ending cleanup of rtx version 4 from keil it is a deterministic real time operating system which implements the cmsis rtos api the code is under a bsd style license this repository will reflect if and when there are any updates to rtx version 4 as rtx version 5 is not under a bsd style license keil has created rtx5 which implements the cmsis rtos2 api it is under the apache 2 license so it will not be mirrored here rtx5 is available from its own github repository at https github com arm software cmsis 5 | os |
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FreeRtos_Projects_TivaC | freertos projects tivac this repo contains different examples for freertos on tiva c board including 1 task creation 2 access synchronization using mutex 3 event synchronization using event groups 4 event synchronization using semaphores 5 intertask communication using queues 6 task synchronization using event groups | os |
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qml-rg | quantum machine learning reading group icfo fall session 2018 this is the second edition of the qml reading group at icfo the archive of the first edition is here https github com peterwittek qml rg tree master archiv session spring 2017 and here https github com peterwittek qml rg tree master archiv session spring 2018 we will face some restructuring and aim for more self initiative than in the last session we will go back to the roots of this rg and just discuss publications of interest that will be chosen by someone so for the first sessions we have the following structure in mind 1 we will again define papers to read for each session we are happy if people come forward with interesting topics or papers this session will more be about keeping track of latest advances in ml qml and ml assisted physics 2 most people attending will already be quite advanced in ml therefore we will not start with ml basics in this group but we are aware that there are ml beginners who attend the rg and we are also happy to explain basics if needed 3 we are not sure yet if there will be coding exercises in the last session most people were too busy anyway but we are always happy to discuss coding problems or suggestions topics will be announced a week in advance coding will be done collaboratively through this repository the reading group requires commitment apart from the 1 5 2 contact hours a week at least another 2 3 hours must be dedicated to reading and coding you are not expected to know machine learning or programming before joining the group but you are expected to commit the time necessary to catch up and develop the relevant skills the language of choice is python 3 resources the broader qml community is still taking shape we are attempting to organize it through the website quantummachinelearning org http quantummachinelearning org you can also sign up for the mailing list there please also consider contributing to the recently rewritten wikipedia article on qml https en wikipedia org wiki quantum machine learning apart from new content stylistic and grammatical edits figures and translations are all welcome the best way to learn machine learning is by doing it the book python machine learning https www packtpub com big data and business intelligence python machine learning is a good starter along with its github repository https github com rasbt python machine learning book for the deep learning part you can have look at the github of the course https github com patrickhuembeli qml course upc 2018 we gave at the upc where we discuss convolutional neural networks boltzman machines and reinforcement learning kaggle http kaggle com is a welcoming community of data scientists it is not only about competitions several hundred datasets are hosted on kaggle along with notebooks and scripts collectively known as kernels that do interesting stuff with the data these provide perfect stepping stones for beginners find a dataset that is close to your personal interests and dive in for a sufficiently engaging theoretical introduction to machine learning the book the elements of statistical learning data mining inference and prediction https statweb stanford edu tibs elemstatlearn is highly recommended anaconda https www continuum io downloads is the recommended python distribution if you are new to the language it ships with most of the scientific and machine learning ecosystem around python it includes scikit learn http scikit learn org which is excellent for prototyping machine learning models for scalable deep learning keras https keras io is easy to start with bit it uses the more intransparent and complicated tensorflow backend for simple implementations we recommend it but as soon as someone wants to implement more advanced neural networks we recommend changing to pytorch qutip http qutip org is an excellent quantum simulation library and with the latest version 4 1 it is reasonably straightforward http qutip org docs 4 1 installation html platform independent installation to install it in anaconda with conda forge https conda forge github io qutip is somewhat limited in scalability so perhaps it is worth checking out other simulators such as projectq http projectq ch we will follow the good practices of software carpentry http software carpentry org that is elementary it skills that every scientist should have in particular we use git https rogerdudler github io git guide as a version control system and host the repository right here on github when editing text or markdown https guides github com features mastering markdown documents like this one please write every sentence in a new line to ensure that conflicts are efficiently resolved when several people edit the same file meeting 1 10 30 12 00 18 october 2018 aquarium room amr 280 topic we will have a look at this paper https www semanticscholar org paper knowledge graph refinement 3a a survey of approaches paulheim 93b6329091e215b9ef007a85c07635f09e7b8adb knowledge graph refinement a survey of approaches and evaluation methods meeting 2 10 30 12 00 8 november 2018 aquarium room amr 280 topic we will go through the paper bayesian deep learning on a quantum computer https arxiv org abs 1806 11463 this will include a short introduction to supervised learning in feedforward neural networks for the newcomers and notes on bayesian learning meeting 3 10 30 12 00 15 november 2018 aquarium room amr 280 topic our journey through bayesian deep learning on a quantum computer https arxiv org abs 1806 11463 continues we will also review equivalences between gp training and training of deep neural networks and quantum assisted training of gps meeting 4 09 30 11 00 13 december 2018 aquarium room amr 280 topic in this session we will review the recent paper 1 https arxiv org abs 1807 04271 where the author proposes a classical algorithm that mimics the quantum algorithm for recommendation systems 2 http drops dagstuhl de opus volltexte 2017 8154 pdf lipics itcs 2017 49 pdf by using stochastic sampling 3 https www math cmu edu af1p texfiles svd pdf for preparation it is recommended going over 1 and reading the nice introduction of 3 knowing 2 even better meeting 5 10 30 12 00 10 january 2019 aquarium room amr 280 topic to kick start the 2019 reading group i will follow the recent trend of showing the loopholes of by presenting this paper https arxiv org abs 1803 11173 the basic message is to argue that that there are major problems when trying to perform gradient descent on classically parametrised quantum circuits i e quantum neural networks since the gradient will be essentially zero everywhere meeting 6 10 30 12 00 17 january 2019 aquarium room amr 280 in this session we will focus on reinforcement learning we will breifly review the basics of rl and q learning you can take a look at this repo https github com gorkamunoz qml course upc 2018 tree master rl for an introduction to the topic then we will see how this paper https arxiv org pdf 1705 00565 pdf applies such algorithms to do quantum control meeting 7 10 30 12 00 24 january 2019 aquarium room amr 280 topic this week we look at methods of interpretability in classical machine learning for this we start with an overview https www sciencedirect com science article pii s1051200417302385 of activation maximization sensitivity analysis and layer wiserelevancepropagation the 2nd paper learning deep features for discriminative localization http cnnlocalization csail mit edu zhou learning deep features cvpr 2016 paper pdf shows how heatmaps can be implemented in a very simple way and finally this https distill pub 2018 building blocks and this https distill pub 2017 feature visualization distill publications show how activation maximization looks like if used on proper deep learning examples you can find examples how to program cam with gap for keras https towardsdatascience com multi label classification and class activation map on fashion mnist 1454f09f5925 and pytorch https towardsdatascience com training convolutional neural networks to categorize clothing with pytorch 30b6d399f05f meeting 8 10 30 12 00 31 january 2019 seminar room topic we will have a look at this paper https arxiv org pdf 1901 06526 pdf it describes how to formulate division and matrix inversion as a qubo problem that can be solved by a quantum annealer like dwave meeting 9 10 30 12 00 7 february 2019 aquarium room amr 280 topic this week we have a look at qaoa on cv systems https arxiv org pdf 1902 00409 pdf | ai |
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fabric | hyperledger fabric build status https dev azure com hyperledger fabric apis build status merge branchname main https dev azure com hyperledger fabric build latest definitionid 51 branchname main cii best practices https bestpractices coreinfrastructure org projects 955 badge https bestpractices coreinfrastructure org projects 955 go report card https goreportcard com badge github com hyperledger fabric https goreportcard com report github com hyperledger fabric godoc https godoc org github com hyperledger fabric status svg https godoc org github com hyperledger fabric documentation status https readthedocs org projects hyperledger fabric badge version latest http hyperledger fabric readthedocs io en latest this project is a graduated hyperledger project for more information on the history of this project see the fabric wiki page https wiki hyperledger org display fabric information on what graduated entails can be found in the hyperledger project lifecycle document https tsc hyperledger org project lifecycle html hyperledger fabric is a platform for distributed ledger solutions underpinned by a modular architecture delivering high degrees of confidentiality resiliency flexibility and scalability it is designed to support pluggable implementations of different components and accommodate the complexity and intricacies that exist across the economic ecosystem hyperledger fabric delivers a uniquely elastic and extensible architecture distinguishing it from alternative blockchain solutions planning for the future of enterprise blockchain requires building on top of a fully vetted open source architecture hyperledger fabric is your starting point releases fabric provides periodic releases with new features and improvements additionally certain releases are designated as long term support lts releases important fixes will be backported to the most recent lts release and to the prior lts release during periods of lts release overlap for more details see the lts strategy https github com hyperledger fabric rfcs blob main text 0005 lts release strategy md current lts release v2 5 x https hyperledger fabric readthedocs io en release 2 5 whatsnew html prior lts release v2 2 x https hyperledger fabric readthedocs io en release 2 2 whatsnew html maintained through december 2023 historic lts releases v1 4 x https hyperledger fabric readthedocs io en release 1 4 whatsnew html maintenance ended in april 2021 with the delivery of v1 4 12 unless specified otherwise all releases will be upgradable from the prior minor release additionally each lts release is upgradable to the next lts release fabric releases and release notes can be found on the github releases page https github com hyperledger fabric releases please visit the github issues with epic label https github com hyperledger fabric labels epic for our release roadmap documentation getting started and developer guides please visit our online documentation for information on getting started using and developing with the fabric sdk and chaincode v2 5 http hyperledger fabric readthedocs io en release 2 5 v2 4 http hyperledger fabric readthedocs io en release 2 4 v2 3 http hyperledger fabric readthedocs io en release 2 3 v2 2 http hyperledger fabric readthedocs io en release 2 2 v2 1 http hyperledger fabric readthedocs io en release 2 1 v2 0 http hyperledger fabric readthedocs io en release 2 0 v1 4 http hyperledger fabric readthedocs io en release 1 4 v1 3 http hyperledger fabric readthedocs io en release 1 3 v1 2 http hyperledger fabric readthedocs io en release 1 2 v1 1 http hyperledger fabric readthedocs io en release 1 1 main branch development http hyperledger fabric readthedocs io en latest it s recommended for first time users to begin by going through the getting started section of the documentation in order to gain familiarity with the hyperledger fabric components and the basic transaction flow contributing we welcome contributions to the hyperledger fabric project in many forms there s always plenty to do check the documentation on how to contribute to this project http hyperledger fabric readthedocs io en latest contributing html for the full details community hyperledger community https www hyperledger org community hyperledger mailing lists and archives http lists hyperledger org hyperledger discord chat https discord com invite hyperledger hyperledger fabric issue tracking github issues https github com hyperledger fabric issues hyperledger fabric wiki https wiki hyperledger org display fabric hyperledger wiki https wiki hyperledger org hyperledger code of conduct https wiki hyperledger org display hyp hyperledger code of conduct community calendar https wiki hyperledger org display hyp calendar of public meetings license a name license a hyperledger project source code files are made available under the apache license version 2 0 apache 2 0 located in the license license file hyperledger project documentation files are made available under the creative commons attribution 4 0 international license cc by 4 0 available at http creativecommons org licenses by 4 0 | hyperledger fabric blockchain distributed-ledger consensus confidentiality | blockchain |
DIY-oximeter | diy oximeter atmega328p with custom pcb designed to monitor pulse and o2 level created as final project for embeded systems course demo02 https user images githubusercontent com 69490354 129453229 e4fbc0ca f211 488c 8602 d16d48e90563 png schematic projekcik 2021 01 20 10 31 11 https user images githubusercontent com 69490354 129453356 c9db1914 e4f1 439c b738 d672112f69c4 png | os |
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azure-iot-arduino | azureiothub azure iot hub library for arduino stop before you proceed this arduino library is deprecated it is kept here for reference only and should not be used for any new development if you re looking for an arduino library you should use the new version aka ms arduino https aka ms arduino you can find more information about it in this iot techcommunity blog post https techcommunity microsoft com t5 internet of things blog arduino library for azure iot ba p 3034455 license see license license file azure certifiedforiot http azure com certifiedforiot microsoft azure certified badge images microsoft azure certified 150x150 png microsoft azure certified complete information for contributing to the azure iot arduino libraries can be found here https github com azure azure iot pal arduino this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments | server |
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UCInternship | ucinternship project uas mobile app development web development dan database | front_end |
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coursera-mobile-develompent-with-ionic | multiplatform mobile app development with web technologies by the hong kong university of science and technology mobile app development with ionic framework course provided by coursera about confusion is an application which allow you to check information of your favourite restauran application give you possibility to check restaurant s menu contact information and cheefs who are working for us moreover you have posibility to save or delete your favourite dishes in your personal favourite list features simply rest api server with json server https github com typicode json server chosing profile picture during registration using build in camera or android gallery local notification toast notification vibration how to start using npm install all required modules npm install gulp cli g npm install bower g npm install cordova ionic g npm install json server g to start json server go to json server directory and type json server watch db json for ionic server you need to disable non compatible plugins you can do that going to app js and setting constant development to true 5 var development true remember to set this option to false before deploying your application to real device otherwise there will be no access to platform features like camera and galery server address configuration in services js set proper address of your rest api using json server it will be address of your local machine with port 3000 4 constant baseurl http 192 168 0 xxx 3000 run app start ionic server ionic serve lab or deploy application to real device for android ionic platform add android ionic build android ionic run android used tools and technologies html5 css javascript ionic framework angularjs scss gulp json server cordova ngcordova | front_end |
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libbitcoin-blockchain | this branch is not usable in its current state please see version3 https github com libbitcoin libbitcoin blockchain tree version3 for the latest functional branch | blockchain |
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awesome-yoruba-nlp | awesome yoruba nlp awesome https cdn rawgit com sindresorhus awesome d7305f38d29fed78fa85652e3a63e154dd8e8829 media badge svg https github com sindresorhus awesome a curated list of resources dedicated to natural language processing in the yoruba language maintainers olamilekan wahab https github com olamyy please read the contribution guidelines contributing md before contributing please feel free to create pull requests https github com olamyy awesome yoruba nlp pulls contents papers papers slides slides datasets datasets papers natural language processing of english to yoruba https www researchgate net publication 301988345 natural language processing of english language to yoruba language a computerized identification system for verb sorting and arrangement in a natural language case study of the nigerian yoruba language http www eajournals org wp content uploads a computerized identification system for verb sorting and arrangement in a natural language case study of the nigerian yoruba language pdf morphological analysis of standard yor b nouns http www ajer org papers v5 06 b05060812 pdf part of speech tagging of yoruba standard language of niger congo family http www isca in com it sci archive v1 i1 1 isca rjcits 2013 003 pdf rule based parts of speech tagging of yor b simple sentences https www grin com document 347022 yoruba word formation processes ucla linguistics http linguistics ucla edu images stories adewole 1995 pdf part of speech tagging of yoruba standard language of niger congo family https leilbadrahzaki wordpress com development of a syllabicator for yor b language http ifecisrg org sites default files articles syoruba 20syllabicator 4 pdf natural language processing of english to yoruba language https www researchgate net profile abayomi alli adebayo publication 296675810 ait 2015 conference proceedings links 56d7e26508aebe4638af23a4 ait 2015 conference proceedings pdf page 120 applying rough set theory to yor b language translation https www researchgate net profile babatunde obalalu publication 301888987 applying rough set theory to yoruba language translation links 572b1fab08ae2efbfdbdbe89 pdf a computational model of yoruba morphology lexical analyzer http citeseerx ist psu edu viewdoc download doi 10 1 1 740 823 rep rep1 type pdf intelligent system for learning and understanding of yoruba language https pdfs semanticscholar org ac53 a1d3c93518fabd26470181ecbf07462e5e17 pdf a stochastic collocation algorithm method for processing the yoruba language using the data context approach based on text lexicon and grammar http www akamaiuniversity us pjst19 1 175 pdf integration of yoruba language into marytts https link springer com article 10 1007 s10772 016 9334 8 attentive seq2seq learning for diacritic restoration of yor b language text https arxiv org abs 1804 00832 slides development of tech to speech system in yoruba https www slideshare net alexanderdecker development of text to speech system for yoruba language a number to yoruba text transcription system https www slideshare net aflat a number to yorb text transcription system datasets wiki dump https dumps wikimedia org yowiki latest yoruba text https github com niger volta lti yoruba text | nlp nlp-resources nlp-machine-learning yoruba | ai |
CommIT | commit communism with information technology | server |
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Lightwork | lightwork beta lightwork gif https media giphy com media xund9sdwxj85fuaubc giphy gif lightwork simplifies the process of mapping complex arrangements of leds removing the limitations of straight lines and grids from your light based creations features compatible with fadecandy pixelpusher sacn and artnet driven led arrays network discovery for pixelpusher and artnet nodes gui controls for led array configuration and opencv mapping calibration sequential mapping fire and detect leds one at a time sequentially slow for large arrays but more reliable in some cases binary mapping leds flash a unique binary pattern and all leds are mapped simultaneously exponentially faster for large arrays but more suceptible to lighting environmental interference save as csv for use with the included scraper or applications like madmapper touchdesigner lightweight scraper that can be added to your application to drive leds seamless map updates while the scraper is running over osc experimental stereo mode captures 3d coordinates by mapping from 2 camera locations how it works lightwork uses opencv to detect and map your led arrangements set up your leds point a webcam at them run lightwork and you ll have a comprehensive map that enables you to put content on them as if they were a screen here s a video walking through the binary mapping process lightwork tutorial http img youtube com vi 7uj1ocxc8eg 0 jpg http www youtube com watch v 7uj1ocxc8eg requirements libraries pixelpusher opencv controlp5 edmx oscp5 most available from the sketch import library add library dialog in the processing ide artnet p5 included in this repo or clone from https github com sadmb artnetp5 edmx will be added to repo shortly https github com jwarwick processing edmx scraper includes syphon mac and spout win client to receive content from another application you ll need the appropriate library for your platform this is optional comment and remove the pde if you don t need this functionality operating system tested and developed in osx 10 12 and windows 10 may not behave well in other operating systems a webcam we recommend and have built this using the logitech c920 webcam it s cheap is tripod mount compatible and generally provides high quality and high speed capture hardware pixelpusher fadecandy or artnet controller and individually addressable leds are required 1440x900 or higher resolution monitor recommended hardware setup reference this readme assumes you have set up your driver hardware and leds first here are some references to get you started if this is your first time pixelpusher setup reference https sites google com a heroicrobot com pixelpusher home getting started https sites google com a heroicrobot com pixelpusher home getting started fadecandy https github com scanlime fadecandy https github com scanlime fadecandy this guide is very useful for setting up a raspberry pi to drive fadecandy controllers over network https learn adafruit com 1500 neopixel led curtain with raspberry pi fadecandy fadecandy server setup https learn adafruit com 1500 neopixel led curtain with raspberry pi fadecandy fadecandy server setup artnet consult your hardware s manual for setup artnet support is currently incomplete lightwork mapper lightwork mapper screenshot https raw github com pwrflcreative lightwork master doc images lightworkui beta png the application used to map your led array use an attached webcam to capture led states and translate their physical locations into screen locations outputs a normalized csv of led locations for use in the lightwork scraper or any software of your choice quick start guide set up your leds their controller and ensure you can connect to it either locally or over network connect your webcam and point it at the led array we recommend a tripod to allow positioning your cam as needed and keeping it stable any movement will throw off your mapping results be sure to turn off auto features on your webcam like auto focus and auto white balance these will make adjustments between frames while mapping affecting results select your camera from the dropdown and its output should appear if it s not in the list hit refresh select your led controller type from the dropdown and enter the appropriate details make sure to hit enter after changing settings once your settings are correct hit connect on successful connect the button will change to refresh allowing you to reconnect with different settings now that you re connected you can calibrate the opencv view to your leds and lighting conditions default is pattern mode but you can also choose sequential for more challenging setups adjust the contrast and threshold until your lit leds appear well defined white blobs in the right window in general keep the led brightness as low as possible to prevent flaring in the camera once you have the cv settings looking good you re almost ready to map the settings on the right are mapping controls frameskip controls the number of frames between captures or the speed mapping runs at turn this up for more reliable results blob size controls the allowed size of blobs the minimum should be set to detect actual leds but filter our noise max should be set to around the size of leds but small enough to filter out blobs that blend together due to flaring blob distance is used to filter duplicate blobs or blobs that appear very close together often caused by flaring these settings will depend on your setup and environmental settings so adjust until you get the desired result now you can stop calibrating and try mapping binary will capture a sequence of frames then analyze them for patterns before returning the number of matched leds adjust your settings until you get the expected number of matched leds sequential will show an ellipse at the location of detected leds but not the total number once mapping is complete press stop and save layout this will open a save dialog that should default to the lightwork scraper directory the window may appear behind other open windows which is a java issue just al tab to it make sure you add csv to the saved file name and that s it you can now use the generated map in the lightwork scraper or another software of your choice ui control reference camera select a connected usb webcam refresh refreshes the list of connected webcams if a camera only shows black unplug it and then refresh this is to do with os drivers not releasing properly stereo toggle enable multiple captures for depth mapping estimates z depth based on 2 captures once enabled you ll need to use map left and map right before saving the layout fps number in the top right is the current framerate useful for performance testing driver led driver hardware currently fadecandy and pixelpusher are supported artnet is incomplete selecting a driver from the dropdown will reveal the required settings to connect pixelpusher configuration is stored on its usb and will be discovered over network ip the local ip of the driver hardware use 127 0 0 1 for a usb connected fadecandy press enter to set the value strips number of strips connected to the driver hardware press enter to set the value leds per strip number of leds on each attached strip use the longest string if they aren t consistent note that fadecandy supports a max of 64 per channel and pixelpusher supports a max of 480 per channel press enter to set the value connect connect to driver hardware using current settings button turns green on successful connection once connected this button can be used to update the strip settings on the driver hardware contrast adjusts the contrast on the opencv input useful for compensating for ambient lighting threshold opencv is performed on a binary image black and white pixels above the threshold become white pixels below the threshold become black use to isolate your leds in a noisy scene led brightness 0 255 this can help reduce lens flaring or compensate for ambient light in a scene pattern sequence select mapping method sequence fires the leds in order of their address and associates them with a physical location one at a time sequential can be very accurate but is slower on large arrays sequential is also beneficial on arrays where the leds are very close together or when their light blends pattern flashes the full led array in binary patterns capturing a video frame for each bit of the pattern once captured the patterns are associated with their physical location all at once binary mapping is an exponential decrease in mapping time as the number of leds increases but is not suitable for all arrangements calibrate run a test to check physical connections and ensure all leds are working also useful for adjusting settings to compensate for ambient lighting or other environmental factors press calibrate again to stop map run the selected mapping method this will display detected led locations in the output window runs until mapping is complete frameskip amount of frames to wait between animator actions lower numbers speed up mapping but can reduce accuracy min max blob size isolate size of blobs elegible for detection increasing minimum size helps eliminate noise increasing maximum size helps compensate for larger leds min blob distance minimum distance between detected blobs blobs closer than this will be filtered out as erroneous save layout saves a csv of the mapping layout to be used in the lightwork scraper keyboard controls alt shift s save ui properties to file alt shift l load ui properties to file note alt also enables dragging in controlp5 try to release the alt key first after using these commands if it s releases last it can enable dragging shift h disable dragging if activated with alt lightwork scraper lightwork scraper screenshot https raw github com pwrflcreative lightwork master doc images lightworkscraper png use with your own sketch to map your content onto the led array requires interface pde opc pde and scraper pde in your sketch folder as well as a layout csv in the sketch or data folder replace the drawing code in the example to display your own content or use syphon spout to pipe in input from another application example setup java void setup size 1280 960 p3d initialize scraper replace with your filename make sure it s in the sketch or data folder scrape new scraper layout csv initialize connection to led driver replace with adress and led config for your setup device type address not required for pixelpusher number of strips leds per strip network new interface device fadecandy fade2 local 3 50 network connect this update scraper after network connects scrape update example draw loop java void draw your drawing code here scraper functions scrape update update colors to be sent to leds network update scrape getcolors send colors to leds scrape display show locations loaded from csv developed with the participation of creative bc the province of british columbia and the british columbia arts council cbcbcaclogos https raw github com pwrflcreative lightwork master doc images creativebc bc joint rgb png | leds computer-vision pixelpusher fadecandy artnet sacn | ai |
coursera-natural-language-processing-specialization | coursera natural language processing specialization this repository contains material related to coursera natural language processing specialization https www coursera org specializations natural language processing it consists of a bunch of jupyter notebooks for each weekly assignment content natural language processing with classification and vector spaces sentiment analysis with logistic regression https github com naderabdalghani coursera natural language processing specialization tree main 1 20natural 20language 20processing 20with 20classification 20and 20vector 20spaces week1 learn to extract features from text into numerical vectors then build a binary classifier for tweets using logistic regression sentiment analysis with na ve bayes https github com naderabdalghani coursera natural language processing specialization tree main 1 20natural 20language 20processing 20with 20classification 20and 20vector 20spaces week2 learn the theory behind bayes rule for conditional probabilities then apply it toward building a naive bayes tweet classifier vector space models https github com naderabdalghani coursera natural language processing specialization tree main 1 20natural 20language 20processing 20with 20classification 20and 20vector 20spaces week3 vector space models capture semantic meaning and relationships between words learn how to create word vectors that capture dependencies between words then visualize their relationships in two dimensions using pca machine translation and document search https github com naderabdalghani coursera natural language processing specialization tree main 1 20natural 20language 20processing 20with 20classification 20and 20vector 20spaces week4 learn to transform word vectors and assign them to subsets using locality sensitive hashing in order to perform machine translation and document search natural language processing with probabilistic models autocorrect https github com naderabdalghani coursera natural language processing specialization tree main 2 20natural 20language 20processing 20with 20probabilistic 20models week1 learn about autocorrect minimum edit distance and dynamic programming then build a spellchecker to correct misspelled words part of speech tagging and hidden markov models https github com naderabdalghani coursera natural language processing specialization tree main 2 20natural 20language 20processing 20with 20probabilistic 20models week2 learn about markov chains and hidden markov models then use them to create part of speech tags for a wall street journal text corpus autocomplete and language models https github com naderabdalghani coursera natural language processing specialization tree main 2 20natural 20language 20processing 20with 20probabilistic 20models week3 learn about how n gram language models work by calculating sequence probabilities then build an autocomplete language model using a text corpus from twitter word embeddings with neural networks https github com naderabdalghani coursera natural language processing specialization tree main 2 20natural 20language 20processing 20with 20probabilistic 20models week4 learn about how word embeddings carry the semantic meaning of words which makes them much more powerful for nlp tasks then build a continuous bag of words model to create word embeddings from shakespeare text natural language processing with sequence models neural networks for sentiment analysis https github com naderabdalghani coursera natural language processing specialization tree main 3 20natural 20language 20processing 20with 20sequence 20models week1 learn about neural networks for deep learning then build a sophisticated tweet classifier that places tweets into positive or negative sentiment categories using a deep neural network recurrent neural networks for language modeling https github com naderabdalghani coursera natural language processing specialization tree main 3 20natural 20language 20processing 20with 20sequence 20models week2 learn about the limitations of traditional language models and see how rnns and grus use sequential data for text prediction then build a next word generator using a simple rnn on shakespeare text data lstms and named entity recognition https github com naderabdalghani coursera natural language processing specialization tree main 3 20natural 20language 20processing 20with 20sequence 20models week3 learn about how long short term memory units lstms solve the vanishing gradient problem and how named entity recognition systems quickly extract important information from text then build a named entity recognition system using an lstm and data from kaggle siamese networks https github com naderabdalghani coursera natural language processing specialization tree main 3 20natural 20language 20processing 20with 20sequence 20models week4 learn about siamese networks a special type of neural network made of two identical networks that are eventually merged together then build a siamese network that identifies question duplicates in a dataset from quora natural language processing with attention models neural machine translation https github com naderabdalghani coursera natural language processing specialization tree main 4 20natural 20language 20processing 20with 20attention 20models week1 discover some of the shortcomings of a traditional seq2seq model and how to solve for them by adding an attention mechanism then build a neural machine translation model with attention that translates english sentences into german text summarization https github com naderabdalghani coursera natural language processing specialization tree main 4 20natural 20language 20processing 20with 20attention 20models week2 compare rnns and other sequential models to the more modern transformer architecture then create a tool that generates text summaries question answering https github com naderabdalghani coursera natural language processing specialization tree main 4 20natural 20language 20processing 20with 20attention 20models week3 explore transfer learning with state of the art models like t5 and bert then build a model that can answer questions chatbot https github com naderabdalghani coursera natural language processing specialization tree main 4 20natural 20language 20processing 20with 20attention 20models week4 examine some unique challenges transformer models face and their solutions then build a chatbot using a reformer model | coursera natural-language-processing jupyter-notebooks | ai |
leetcode-typescript | awards ranking dev global top 200 certificate https leetcode com sergeyleschev a href https leetcode com sergeyleschev img src https github com sergeyleschev sergeyleschev blob main leetcode ranking png raw true alt drawing width 410 a a href https leetcode com sergeyleschev img src https github com sergeyleschev sergeyleschev blob main leetcode medals png raw true alt drawing width 410 a languages typescript shell database t sql pl sql mysql concurrency python3 algorithms linked lists binary search hash table queue stack dfs bfs sort heap hash two pointers sliding window tree greedy problems etc | tree typescript javascript linked-list stack queue hash sort dfs heap bfs hash-table binary-search two-pointers sliding-window greedy-problems sergeyleschev algorithm algorithms leetcode | server |
Kooboo | kooboo kooboo is a new kind of web development it saves you hours and make your website in a better way it contains many things you need to do web development it contains a builtin webserver an email server template engine dynamic database and javascript executor the binary release and documentation can be found at http www kooboo com some of the features as below 5mb 1 second the binary release of kooboo application is contained in a zip file of less than 5mb you can either use the online version or download unpack and double click kooboo exe to start no installation needed take 1 second to start 1 millisecond response time kooboo can render a dynamic page within 1 millisecond this is a page that contains 10 dynamic content items normally take more than 50 milliseconds for other system to render using instead of learning there are many ways to get started you can migrate a website by a single url upload microsoft office document or standard html zip it will be unpacked converted and make available as pages for editing within kooboo all in javascript you can do any web devlopment by using javascript only you can use javascript to export json api read write database manage session data and process http request the javascript engine in kooboo is called kscript click and edit anything can be inline edited you can click anywhere on the webpage and edit anything directly it can be a text image content item or stylesheet color undo and restore every change you make is logged and can be reverted you can also checkout or restore the website to a certain time in the past relation map every object has a relation map to other objects within the website you know where an image is being used whether it is in the page content or in the stylesheet one click deployment one click to deploy incremental changes to target servers you see exactly which item you edited select and publish changed items to remote site or export the entire website import into another instance websites go live instantly kooboo provide an online version which works exactly the same way as local desktop version no dns a record mx record setup website go live instantly if you register your domains somewhere else you need to change the dns servers to kooboo dns servers that provided to you when you are adding your domain to kooboo all a record cname mx record will be created for you automatically | cms development javascript website-builder kooboo website-development website-scraper netcoreapp netcore21 | front_end |
Flutter-Blog-App | br a href https www buymeacoffee com devstack06 target blank align center p align center img src https cdn buymeacoffee com buttons default orange png alt buy me a coffee height 41 width 174 p a blog app development front end and back end using flutter expressjs nodejs and mongodb playlist for blog app development series playlist name youtube playlist link blog app development main playlist link https youtube com playlist list pltiu0bh0pkkoe2pbvgbhebpap sd670vi blog app development only front end using flutter link https www youtube com watch v 6vclehrnixg list pltiu0bh0pkkpitsp5jzt ydaoxafbkcpb blog app development only back end using node expressjs link https www youtube com watch v t35t8nzyrdi list pltiu0bh0pkkqypuotdhcxz4oatjfji49r br some other playlist playlist name youtube playlist link flutter model class series for rest api connection and json parsing link https www youtube com playlist list pltiu0bh0pkkpxe 1vc7nswoffpby1oyh flutter basic series link https www youtube com playlist list pltiu0bh0pkkrk8c7ktofersvti2clpftg if this tutorial helped you please give a star and also fork the repo thank you happy coding h2 this app have following things h2 h3 align center style color green 1 welcome page h3 p align center img src https github com devstack06 images blob master blog app welcomepage png width 350 img p br h3 align center style color green 2 login screen h3 p align center img src https github com devstack06 images blob master blog app loginpage png width 350 img p br h3 align center 3 wrong username error handling h3 p align center img src https github com devstack06 images blob master blog app wrong username png width 350 img p br h3 align center 4 signup screen h3 p align center img src https github com devstack06 images blob master blog app signuppage png width 350 img p br h3 align center 5 home screen h3 p align center img src https github com devstack06 images blob master blog app homepage png width 350 img p br h3 align center 6 view blog page h3 p align center img src https github com devstack06 images blob master blog app vievblogpage png width 350 img p br h3 align center 7 add profile screen h3 p align center img src https github com devstack06 images blob master blog app addprofile png width 350 img p br h3 align center 8 create profile screen h3 p align center img src https github com devstack06 images blob master blog app createprofile png width 350 img p br h3 align center 9 profile screen h3 p align center img src https github com devstack06 images blob master blog app profilepage png width 350 img p br h3 align center 10 drawer h3 p align center img src https github com devstack06 images blob master blog app drawer png width 350 img p br h3 align center 11 add blog page h3 p align center img src https github com devstack06 images blob master blog app addblog png width 350 img p br h3 align center 12 forgot password h3 p align center img src https github com devstack06 images blob master blog app forgetpassword png width 350 img p br to use this app follow below instructions 1 clone this app using below syntax git clone https github com devstack06 flutter blog app git 2 after cloning install packages using below syntax flutter pub get above command will install all the neccery packges 3 run the app on your mobile using below command flutter run | flutter rest-api nodejs expressjs mongodb mongodb-atlas heroku-deployment youtube appdevelopment appdeveloper youtube-series | server |
Jananga_HelloWorldLabs | jananga helloworldlabs this is the work on the first tutorial of the embedded system design module hellooo programe | os |
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stm32_mw_freertos | middleware freertos mcu component latest tag https img shields io github v tag stmicroelectronics stm32 mw freertos svg color brightgreen stm32cube is an stmicroelectronics original initiative to ease developers life by reducing efforts time and cost stm32cube covers the overall stm32 products portfolio it includes a comprehensive embedded software platform delivered for each stm32 series the cmsis modules core and device corresponding to the arm tm core implemented in this stm32 product the stm32 hal ll drivers an abstraction layer offering a set of apis ensuring maximized portability across the stm32 portfolio the bsp drivers of each evaluation demonstration or nucleo board provided for this stm32 series a consistent set of middleware libraries such as rtos usb fatfs graphics touch sensing library a full set of software projects basic examples applications and demonstrations for each board provided for this stm32 series description this stm32 mw freertos mcu component repository is one element common to all stm32cube mcu embedded software packages providing the freertos middleware part release note details about the content of this release are available in the release note here source history txt details about the updates made by stmicroelectronics are available in the release note here source st readme txt compatibility information please refer to the release note in the repository of the stm32cube firmware you are using to know which version of this middleware library to use with other components versions e g other middleware libraries drivers it is crucial that you use a consistent set of versions troubleshooting please refer to the contributing md contributing md guide | stm32cube-mcu-component middleware middleware-library freertos rtos | os |
deep-learning-drizzle | balloon tada deep learning drizzle confetti ball balloon books read enough so you start developing intuitions and then trust your intuitions and go for it https www deeplearning ai hodl geoffrey hinton books br prof geoffrey hinton university of toronto heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign deep learning deep neural networks arrow heading down https github com kmario23 deep learning drizzle tada deep learning deep neural networks confetti ball balloon probabilistic graphical models arrow heading down https github com kmario23 deep learning drizzle loudspeaker probabilistic graphical models sparkles machine learning fundamentals arrow heading down https github com kmario23 deep learning drizzle cupid machine learning fundamentals cyclone boom natural language processing arrow heading down https github com kmario23 deep learning drizzle hibiscus natural language processing cherry blossom sparkling heart optimization for machine learning arrow heading down https github com kmario23 deep learning drizzle cupid optimization for machine learning cyclone boom automatic speech recognition arrow heading down https github com kmario23 deep learning drizzle speaking head automatic speech recognition speech balloon thought balloon general machine learning arrow heading down https github com kmario23 deep learning drizzle cupid general machine learning cyclone boom modern computer vision arrow heading down https github com kmario23 deep learning drizzle fire modern computer vision camera flash movie camera reinforcement learning arrow heading down https github com kmario23 deep learning drizzle balloon reinforcement learning hotsprings video game boot camps or summer schools arrow heading down https github com kmario23 deep learning drizzle star2 boot camps or summer schools maple leaf bayesian deep learning arrow heading down https github com kmario23 deep learning drizzle game die bayesian deep learning spades gem medical imaging arrow heading down https github com kmario23 deep learning drizzle movie camera medical imaging camera video camera graph neural networks arrow heading down https github com kmario23 deep learning drizzle tada graph neural networks geometric dl confetti ball balloon bird s eye view of artificial intelligence arrow heading down https github com kmario23 deep learning drizzle bird birds eye view of agi eagle heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign tada deep learning deep neural networks confetti ball balloon heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage lecture videos year 1 neural networks for machine learning geoffrey hinton university of toronto lecture slides http www cs toronto edu hinton coursera slides html br csc321 tijmen https www cs toronto edu tijmen csc321 youtube lectures https www youtube com playlist list plorl3ht4jocdu872ghiywf6jwrk snhz9 br uoft mirror https www cs toronto edu hinton coursera lectures html 2012 br 2014 2 neural networks demystified stephen welch welch labs suppl code https github com stephencwelch neural networks demystified youtube lectures https www youtube com playlist list pliahhy2ibx9hdharr6b7xevztgzra1pou 2014 3 deep learning at oxford nando de freitas oxford university oxford ml http www cs ox ac uk teaching courses 2014 2015 ml youtube lectures https www youtube com playlist list ple6wd9fr efw8dtjaupotupcqmov53fu 2015 4 deep learning for perception dhruv batra virginia tech ece 6504 https computing ece vt edu f15ece6504 youtube lectures https www youtube com playlist list pl fzd610i7yasfh2elbirda90kl2ml0f7 2015 5 deep learning ali ghodsi university of waterloo stat 946 https uwaterloo ca data analytics deep learning youtube lectures https www youtube com playlist list plehulrpyt1hyi78uokmpwcgrxgca9nvoe f2015 6 cs231n cnns for visual recognition andrej karpathy stanford university cs231n http cs231n stanford edu 2015 none 2015 7 cs224d deep learning for nlp richard socher stanford university cs224d http cs224d stanford edu youtube lectures https www youtube com playlist list plmimxx8char8dxwb9lrqdpctmewaml96q 2015 8 bay area deep learning many legends stanford none youtube lectures https www youtube com playlist list plraxtmerzgofmuxkacrynd2ftgbzk2thw 2016 9 cs231n cnns for visual recognition andrej karpathy stanford university cs231n http cs231n stanford edu 2016 youtube lectures https www youtube com playlist list plkt2usq6rbvctenovbg1tpcc7oqi31alc br academic torrent https academictorrents com details 46c5af9e2075d9af06f280b55b65cf9b44eb9fe7 2016 10 neural networks hugo larochelle universit de sherbrooke neural networks http info usherbrooke ca hlarochelle neural networks content html youtube lectures https www youtube com playlist list pl6xpj9i5qxyecohn7tqghaj6naprnmubh br academic torrent https academictorrents com details e046bca3bc837053d1609ef33d623ee5c5af7300 2016 11 cs224d deep learning for nlp richard socher stanford university cs224d http cs224d stanford edu youtube lectures https www youtube com playlist list plljy ebtnft4csvwyqschddp58m3zfhig br academic torrent https academictorrents com details dd9b74b50a1292b4b154094b7338ec1d66e8894d 2016 12 cs224n nlp with deep learning richard socher stanford university cs224n http web stanford edu class cs224n youtube lectures https www youtube com playlist list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 2017 13 cs231n cnns for visual recognition justin johnson stanford university cs231n http cs231n stanford edu 2017 youtube lectures https www youtube com playlist list pl3fw7lu3i5jvhm8ljyj zlfqrf3eo8syv br academic torrent https academictorrents com details ed8a16ebb346e14119a03371665306609e485f13 2017 14 topics in deep learning ruslan salakhutdinov cmu 10707 https deeplearning cmu 10707 github io youtube lectures https www youtube com playlist list plpixoj hndsosl buy7 uevqkyfhhapa f2017 15 deep learning crash course leo isikdogan ut austin none youtube lectures https www youtube com playlist list plwkotbjtdolj3rxbl neiprn9v3a9cx07 2017 16 deep learning and its applications fran ois piti trinity college dublin ee4c16 https github com frcs 4c16 2017 youtube lectures https www youtube com playlist list plio1iezl5ib9nkulnr0x5vxn8aaekglwt 2017 17 deep learning andrew ng stanford university cs230 http cs230 stanford edu youtube lectures https www youtube com playlist list ploromvodv4roabxsyghtsbvuz4g yqhob 2018 18 uva deep learning efstratios gavves university of amsterdam uva dlc https uvadlc github io lecture videos https uvadlc github io lectures sep2018 html 2018 19 advanced deep learning and reinforcement learning many legends deepmind none youtube lectures https www youtube com playlist list plqymg7htrazdnjre23vqcgivpfz k2rzs 2018 20 machine learning peter bloem vrije universiteit amsterdam mlvu https mlvu github io youtube lectures https www youtube com playlist list plcof9eqayqgsoro3pfzeyzfz6cszyo0vj 2018 21 deep learning francois fleuret epfl ee 59 https fleuret org ee559 2018 dlc video lectures https fleuret org ee559 2018 dlc materials 2018 22 introduction to deep learning alexander amini harini suresh and others mit 6 s191 http introtodeeplearning com youtube lectures https www youtube com playlist list pltbw6njqru rwp5 7c0oivt26zgjg9ni br 2017 version https www youtube com playlist list plkkunyzb8lmxfutyupa7b4oimn6cjd6rs 2017 2021 23 deep learning for self driving cars lex fridman mit 6 s094 https selfdrivingcars mit edu youtube lectures https www youtube com playlist list plraxtmerzgoeikm4sgnokngvnjby9efdf 2017 2018 24 introduction to deep learning bhiksha raj and many others cmu 11 485 785 http deeplearning cs cmu edu youtube lectures https www youtube com playlist list plp 0k3kfddpwjbj4q8we 0ynqeg0fzrsa s2018 25 introduction to deep learning bhiksha raj and many others cmu 11 485 785 http deeplearning cs cmu edu youtube lectures https www youtube com playlist list plp 0k3kfddpyh44fp0dl0cbyprvtcfgoi recitation inclusive https www youtube com playlist list pllr0 zolbfd6kdbq93g8 guhi j1icefm f2018 26 deep learning specialization andrew ng stanford dl ai https www deeplearning ai deep learning specialization youtube lectures https www youtube com channel uccixc5mjshvytzr1mal5l9w playlists 2017 2018 27 deep learning ali ghodsi university of waterloo stat 946 https uwaterloo ca data analytics teaching deep learning 2017 youtube lectures https www youtube com playlist list plehulrpyt1hxtolyuweyyioxdabdmaosb f2017 28 deep learning mitesh khapra iit madras cs7015 https www cse iitm ac in miteshk cs7015 html youtube lectures https www youtube com playlist list plyqspqzte6m9gcgajvqbc68hk jkgbayt 2018 29 deep learning for ai upc barcelona dlai 2017 https telecombcn dl github io 2017 dlai br dlai 2018 https telecombcn dl github io 2018 dlai youtube lectures https www youtube com playlist list pl 5emc3hqtbagiujkefjctbnxc0wxc vd 2017 2018 30 deep learning alex bronstein and avi mendelson technion cs236605 https vistalab technion github io cs236605 info youtube lectures https www youtube com playlist list plm0a6z788yazuqg2ip dplzed33lzvp2 2018 31 mit deep learning many researchers lex fridman mit 6 s094 6 s091 6 s093 https deeplearning mit edu youtube lectures https www youtube com playlist list plraxtmerzgoeikm4sgnokngvnjby9efdf 2019 32 deep learning book companion videos ian goodfellow and others dl book slides https www deeplearningbook org lecture slides html youtube lectures https www youtube com playlist list plsxu9mhqgs8df5a4pzqgw kfviylc r9b 2017 33 theories of deep learning many legends stanford stats 385 https stats385 github io youtube lectures https www youtube com playlist list plwuqqmt5en7fflwsda9v3jikdam wwgqy br first 10 lectures f2017 34 neural networks grant sanderson none youtube lectures https www youtube com playlist list plzhqobowtqdnu6r1 67000dx zcjb 3pi 2017 2018 35 cs230 deep learning andrew ng kian katanforoosh stanford cs230 http cs230 stanford edu youtube lectures https www youtube com playlist list ploromvodv4roabxsyghtsbvuz4g yqhob a2018 36 theory of deep learning lots of legends canary islands dali 18 http dalimeeting org dali2018 workshoptheorydl html youtube lectures https www youtube com playlist list plecnfjwzkqxtwbnv8gefgqmmpgz9yf4lr 2018 37 introduction to deep learning alex smola uc berkeley stat 157 http courses d2l ai berkeley stat 157 index html youtube lectures https www youtube com playlist list plzso 6 bsqhqhbcogaobuljoxayyqhpfw s2019 38 deep unsupervised learning pieter abbeel uc berkeley cs294 158 https sites google com view berkeley cs294 158 sp19 home youtube lectures https www youtube com channel ucf4sx8kazm ogczjmresu9w videos s2019 39 machine learning peter bloem vrije universiteit amsterdam mlvu https mlvu github io youtube lectures https www youtube com playlist list plcof9eqayqgupldntvqny bthtcme5r93 2019 40 deep learning on computational accelerators alex bronstein and avi mendelson technion cs236605 https vistalab technion github io cs236605 lectures youtube lectures https www youtube com playlist list plm0a6z788yaa wcy v q9nrgm5qqegzr5 s2019 41 introduction to deep learning bhiksha raj and many others cmu 11 785 http www cs cmu edu bhiksha courses deeplearning spring 2019 www youtube lectures https www youtube com playlist list plp 0k3kfddpzndzpx4p0lvi6acdxbofuf s2019 42 introduction to deep learning bhiksha raj and many others cmu 11 785 https www cs cmu edu bhiksha courses deeplearning fall 2019 www youtube lectures https www youtube com playlist list plp 0k3kfddpwz13vqv1pamxf6v6dydesj br recitations https www youtube com playlist list plp 0k3kfddpxf4t59jeqkv5uanlpvsxzz f2019 43 uva deep learning efstratios gavves university of amsterdam uva dlc https uvadlc github io lecture videos https uvadlc github io lectures apr2019 html s2019 44 deep learning prabir kumar biswas iit kgp none youtube lectures https www youtube com playlist list plbrmhdvumngc7nm gdwcbziyznfsk2n1a 2019 45 deep learning and its applications aditya nigam iit mandi cs 671 http faculty iitmandi ac in aditya cs671 index html youtube lectures https www youtube com playlist list plkvx2d3iuq586ic9gihzj6ubpwv ojfl4 2019 46 neural networks neil rhodes harvey mudd college cs 152 https www cs hmc edu rhodes cs152 schedule html youtube lectures https www youtube com playlist list plgeuvsrbai9uiqshgy4l01laa 12yoqej f2019 47 deep learning thomas hofmann eth z rich dal dl http www da inf ethz ch teaching 2019 deeplearning lecture videos https video ethz ch lectures d infk 2019 autumn 263 3210 00l html f2019 48 deep learning milan straka charles university npfl114 https ufal mff cuni cz courses npfl114 lecture videos https ufal mff cuni cz courses npfl114 1718 summer s2019 49 uva deep learning efstratios gavves university of amsterdam uva dlc 19 https uvadlc github io lectures lecture videos https uvadlc github io lectures f2019 50 artificial intelligence principles and techniques percy liang and dorsa sadigh stanford university cs221 https stanford cs221 github io autumn2019 youtube lectures https www youtube com playlist list ploromvodv4ro1nb9td4iuz3qghgegtqnx f2019 51 analyses of deep learning lots of legends stanford university stats 385 https stats385 github io youtube lectures https stats385 github io lecture videos 2017 2019 52 deep learning foundations and applications debdoot sheet and sudeshna sarkar iit kgp ai61002 http www facweb iitkgp ac in debdoot courses ai61002 spr2020 youtube lectures https www youtube com playlist list pl addfjimo6pzfwjz0rjlke mismrw7mh s2020 53 designing visualizing and understanding deep neural networks john canny uc berkeley cs 182 282a https bcourses berkeley edu courses 1487769 pages cs l w 182 slash 282a designing visualizing and understanding deep neural networks spring 2020 youtube lectures https www youtube com playlist list plkfd6 40kjiwao6eca8kzsefbob0nfvwm s2020 54 deep learning yann lecun and alfredo canziani nyu ds ga 1008 https atcold github io pytorch deep learning youtube lectures https www youtube com playlist 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lectures https www youtube com playlist list pltkmizhvd 2jkr6qtqenml7swcnfbtq4p s2020 60 deep learning andreas maier fau erlangen n rnberg dl 2020 https www video uni erlangen de course id 925 youtube lectures https www youtube com playlist list plpogqvpcdqzvgpd3s0vty7bje2pf yjfj br lecture videos https www video uni erlangen de course id 925 ss2020 61 introduction to deep learning laura leal taix and matthias niessner tu m nchen i2dl in2346 https dvl in tum de teaching i2dl ss20 youtube lectures https www youtube com playlist list plq8y4kiibzy oaxv86lfbqwphsomk2o2e ss2020 62 deep learning sargur srihari suny buffalo cse676 https cedar buffalo edu srihari cse676 youtube lectures p1 https www youtube com playlist list plmx4utxjuqd70k nzeisixf30m54t e1h br youtube lectures p2 https www youtube com channel ucum7yumvjyabyh 0ppj4h g videos 2020 63 deep learning lecture series lots of legends deepmind x ucl london dlls 20 https deepmind com learning resources deep learning lecture series 2020 youtube lectures https www youtube com playlist list plqymg7htrazcdxz44o4p3n5anz3llrvzf 2020 64 multimodal machine learning louis philippe morency others carnegie mellon university 11 777 mmml 20 https cmu multicomp lab github io mmml course fall2020 youtube lectures https www youtube com channel ucqlhijtgyhiwqpnupu5e2gg videos f2020 65 reliable and interpretable artificial intelligence martin vechev eth z rich riai 20 https www sri inf ethz ch teaching riai2020 youtube lectures https www youtube com playlist list plwjm4hhpang6c w7jjnydec kjk9osp0y f2020 66 fundamentals of deep learning david mcallester toyota technological institute chicago ttic 31230 https mcallester github io ttic 31230 fall2020 youtube lectures https www youtube com channel uccivrtrrr3bqdagbti9 hvq videos f2020 67 foundations of deep learning soheil feize university of maryland college park cmsc 828w http www cs umd edu class fall2020 cmsc828w youtube lectures https www youtube com playlist list plhgjs9ncvhi80ucslsvqe tk uoydv jf f2020 68 deep learning andreas geiger universit t t bingen dl ut https uni tuebingen de fakultaeten mathematisch naturwissenschaftliche fakultaet fachbereiche informatik lehrstuehle autonomous vision teaching lecture deep learning youtube lectures https www youtube com playlist list pl05ump7r6ij3ntwidtmbfvx7z 4wexrqd w20 21 69 deep learning andreas maier fau erlangen n rnberg dl fau https www fau tv course id 1599 youtube lectures https www youtube com playlist list plpogqvpcdqzvjepfuq3mjz72gj95jyzth w20 21 70 fundamentals of deep learning terence parr and yannet interian university of san francisco dl fundamentals https github com parrt fundamentals of deep learning youtube lectures https www youtube com playlist list plfcc fc116ikeol9czcwwkqmrjljxhe4n s2021 71 full stack deep learning pieter abbeel sergey karayev uc berkeley fs dl https fullstackdeeplearning com spring2021 youtube lectures https www youtube com playlist list pl1t8fo7arwlcwg04ognijy91pywmkt2lv s2021 72 deep learning designing visualizing and understanding dnns sergey levine uc berkeley cs 182 https cs182sp21 github io youtube lectures https www youtube com playlist list pl iwqose6tfvmkkqhucjpaortijyt8a5a s2021 73 deep learning in the life sciences manolis kellis mit 6 874 https mit6874 github io youtube lectures https www youtube com playlist list plypixjdtica5sxv7ae3 ps9fyx3vudiox s2021 74 introduction to deep learning and generative models sebastian raschka university of wisconsin madison stat 453 http pages stat wisc edu sraschka teaching stat453 ss2021 youtube lectures https www youtube com playlist list pltkmizhvd 2kjtixow0zfhffbajjilh51 s2021 75 deep learning alfredo canziani and yann lecun nyu nyu dlsp21 https atcold github io nyu dlsp21 youtube lectures https www youtube com playlist list pllhtzkzzvu9e6xufg10tktwapkszczubi s2021 76 applied deep learning alexander pacha tu wien none youtube lectures https www youtube com playlist 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pl8fnqmh2k7jzprxqdyufoiyvhim8pyzwd 2022 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign cupid machine learning fundamentals cyclone boom heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage video lectures year 1 linear algebra gilbert strang mit 18 06 sc http ocw mit edu 18 06scf11 youtube lectures https www youtube com playlist list pl221e2bbf13becf6c 2011 2 probability primer jeffrey miller brown university mathematical monk youtube lectures https www youtube com playlist list pl17567a1a3f5db5e4 2011 3 information theory pattern recognition and neural networks david mackay university of cambridge itprnn http www inference org uk mackay itprnn youtube lectures https www youtube com playlist list plrubu5bi5n4afpg32imbdworvaa vcso6 2012 4 linear algebra review zico kolter cmu linalg http www cs cmu edu zkolter course linalg index html youtube lectures https www youtube com playlist list plm4pv4kyyzgzl5ay6dmpyzrnbzq 8v t 2013 5 probability and statistics michel van biezen none youtube lectures https www youtube com playlist list plx2gx ftpvxuwwtzakohbdhplvz0fbyqv 2015 6 linear algebra an in depth introduction pavel grinfeld none part 1 https www youtube com playlist list pllxfthzgmrukxd88idzs14f4nxazudsmv br part 2 https www youtube com playlist list pllxfthzgmrulwjythculb2qweizokwtsu br part 3 https www youtube com playlist list pllxfthzgmruiqyrutsfxcomiqkugoggj5 br part 4 https www youtube com playlist list pllxfthzgmrulzfrncrrj7xdctjgr633mm 2015 2017 7 multivariable calculus grant sanderson khan academy none youtube lectures https www youtube com playlist list plsql0a2vh4hc5feha6rc5c0wbrtx56nf7 2016 8 essence of linear algebra grant sanderson none youtube lectures https www youtube com playlist list plzhqobowtqdpd3mizzm2xvfitgf8he ab 2016 9 essence of calculus grant sanderson none youtube lectures https www youtube com playlist list plzhqobowtqdmsr9k rj53dwvrmyo3t5yr 2017 2018 10 math background for machine learning geoff gordon cmu 10 606 https canvas cmu edu courses 603 assignments syllabus 10 607 https piazza com cmu fall2017 1060610607 home youtube lectures https www youtube com playlist list pl7y 1rk2ccsaqrtwoz95z gmcecvg5mza f2017 11 mathematics for machine learning linear algebra calculus david dye samuel cooper and freddie page ic london mml https www coursera org learn linear algebra machine learning youtube lectures https www youtube com playlist list plmauaus7wsop itndivr0ankutuhezme4 2018 12 multivariable calculus s k gupta and sanjeev kumar iit roorkee mvc https nptel ac in syllabus 111107108 youtube lectures https www youtube com playlist list plq gm0yrywtiqtk374nzhfocqkwmj71vx 2018 13 engineering probability rich radke rensselaer polytechnic institute none youtube lectures https www youtube com playlist list pluh62q4sv7bu1dn2g6ncyimbml7oxh jx 2018 14 matrix methods in data analysis signal processing and machine learning gilbert strang mit 18 065 https ocw mit edu courses mathematics 18 065 matrix methods in data analysis signal processing and machine learning spring 2018 youtube lectures https www youtube com playlist list plul4u3cngp63omnuhxqiucrks2pivhn3k s2018 15 information theory himanshu tyagi iisc bengaluru e2 201 https ece iisc ac in htyagi course e2201 2020 html youtube lectures https www youtube com playlist list plgmdnelgj1cys 8dlmgpiaowvfeda4nuj 2018 20 16 math camp mark walker university of arizona uamathcamp econ 519 http www u 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lectures https www youtube com playlist list pl05ump7r6ij1a6kdey8pve9zocv6slhrs w2020 21 introduction to causal inference brady neal mila montr al causalinf https www bradyneal com causal inference course youtube lectures https www youtube com playlist list ploazktcs0rzb6bb9l508cyj1z u9iwka0 f2020 22 applied linear algebra andrew thangaraj iit madras ee5120 http www ee iitm ac in andrew ee5120 youtube lectures https www youtube com playlist list plyqspqzte6m chzu5rgfamcxonufytopm 2021 23 mathematical tools for data science carlos fernandez granda new york university ds ga 1013 math ga 2824 https cds nyu edu math tools youtube lectures https www youtube com playlist list plbef5mjte6ktu6ylxfzd6lyychhw5pilc 2021 24 mathematics for numerical computing and machine learning ryan adams princeton university cos 302 sml 305 https www cs princeton edu courses archive spring21 cos302 youtube lectures https www youtube com playlist list plco4cuablhfeho42hvivwasovbaih30uc 2021 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign cupid optimization for machine learning cyclone boom heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage video lectures year 1 convex optimization stephen boyd stanford university ee364a http web stanford edu class ee364a lectures html youtube lectures https www youtube com playlist list pl3940dd956cdf0622 2008 2 introduction to optimization michael zibulevsky technion cs 236330 https sites google com site michaelzibulevsky optimization course youtube lectures https www youtube com playlist list pldfb2eef4ddafe30b 2009 3 optimization for machine learning s v n vishwanathan purdue university none youtube lectures https www youtube com playlist list pl09b0e8afc69be108 2011 4 optimization geoff gordon ryan tibshirani cmu 10 725 https www cs cmu edu ggordon 10725 f12 youtube lectures https www youtube com playlist list pl7y 1rk2ccsdov91mclonv4kexfftb7du 2012 5 convex optimization joydeep dutta iit kanpur cvx nptel https nptel ac in courses 111 104 111104068 youtube lectures https www youtube com playlist list plbmvogvj5njqhfqfisdgalccwvdcm1w4l 2013 6 foundations of optimization joydeep dutta iit kanpur fop nptel https nptel ac in courses 111 104 111104071 youtube lectures https www youtube com playlist list plbmvogvj5njrrbofh3qm3p6 nvyevdgd 2014 7 algorithmic aspects of machine learning ankur moitra mit 18 409 aaml http people csail mit edu moitra 409 html youtube lectures 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https transp or epfl ch books optimization html about book html youtube lectures https www youtube com playlist list plm4pv4kyyzgzopwwsav6ggllt6njsi1g 2019 18 optimization and simulation michel bierlaire cole polytechnique f d rale de lausanne epfl opt sim https transp or epfl ch courses optsim2019 slides php youtube lectures https www youtube com playlist list pl10nonsbp5q5nlj y6eiup6rtsfkuj1tr s2019 19 brazilian workshop on continuous optimization lots of legends instituto nacional de matem tica pura e aplicada rio de janeiro cont opt https impa br eventos do impa eventos 2019 xiii brazilian workshop on continuous optimization youtube lectures https www youtube com playlist list plo4jxe lddtqvzhnlpq2w31vj1fq1vsp6 2019 20 one world optimization seminar lots of legends universit t wien 1w opt https owos univie ac at youtube lectures https www youtube com playlist list plbqo yzomzlwecaptztyonwxo9hhxraa2 2020 21 convex optimization ii constantine caramanis ut austin cvx optim ii http users ece utexas edu cmcaram constantine caramanis announcements html youtube lectures https www youtube com playlist list plxsmhndvpjorzpelsds0lsdrfjcqyllzc s2020 22 combinatorial optimization constantine caramanis ut austin comb op https caramanis github io teaching youtube lectures https www youtube com playlist list plxsmhndvpjorctrfmvf3augyylhsxfhnl f2020 23 optimization methods for machine learning and engineering julius pfrommer j rgen beyerer karlsruher institut f r technologie kit optim mle https ies anthropomatik kit edu lehre 1487 php slides https drive google com drive folders 1wwvwv4vdbiokjzc6ufy3nfxvpaouhcfb youtube lectures https www youtube com playlist list pldktdauaunqpzuoczyuuzc0lxf4 pxnr5 w2020 21 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus 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data mining nando de freitas university of british columbia cpsc 340 https www cs ubc ca nando 340 2012 index php youtube lectures https www youtube com playlist list ple6wd9fr ecf 5ncbnsqmhqorpichfjf 2012 5 learning from data yaser abu mostafa caltech cs156 http work caltech edu telecourse html youtube lectures https www youtube com playlist list pld63a284b7615313a 2012 6 machine learning rudolph triebel technische universit t m nchen machine learning https vision in tum de teaching ws2013 ml ws13 youtube lectures https www youtube com playlist list pltbdjv 4f eiiongkls9okrbep8qr47wl 2013 7 introduction to machine learning alex smola cmu 10 701 http alex smola org teaching cmu2013 10 701 youtube lectures https www youtube com playlist list plzso 6 bsqhqmmkwwvvywkregu4b4kmu9 2013 8 introduction to machine learning alex smola and geoffrey gordon cmu 10 701x http alex smola org teaching cmu2013 10 701x youtube lectures https www youtube com playlist list plzso 6 bsqhr7npk4k0zqdm2dpdraqz b 2013 9 pattern recognition sukhendu das iit m and c a murthy isi calcutta pr nptel https nptel ac in syllabus 106106046 youtube lectures https www youtube com playlist list plbmvogvj5njqjmlb2cyw9rry0d5s0tqrp 2014 10 an introduction to statistical learning with applications in r trevor hastie and robert tibshirani stanford stat learn https lagunita stanford edu courses humanitiesandscience statlearning winter2015 about br r bloggers https www r bloggers com in depth introduction to machine learning in 15 hours of expert videos youtube lectures https www youtube com playlist list plog0nghtcqbptlzzrha2ocqzqb1d qz5v 2014 11 introduction to machine learning katie malone sebastian thrun udacity ml udacity https www udacity com course ud120 youtube lectures https www youtube com playlist list plawxtw4syapkqxg8tkvdivyv4hflg7sih 2015 12 introduction to machine learning dhruv batra virginia tech ece 5984 https filebox ece vt edu s15ece5984 youtube lectures https www youtube com playlist list pl fzd610i7yduintfy teoxktwg4mhzhu 2015 13 statistical learning classification ali ghodsi university of waterloo stat 441 https uwaterloo ca data analytics statistical learning classification youtube lectures https www youtube com playlist list plehulrpyt1hy 4obwbk4ab0xk97s6imfc 2015 14 machine learning theory shai ben david university of waterloo none youtube lectures https www youtube com playlist list plpw2kenyw usgvmr7ftq3zrjfls5jt4bo 2015 15 introduction to machine learning alex smola cmu 10 701 http alex smola org teaching 10 701 15 youtube lectures https www youtube com playlist list plzso 6 bsqhttv7w9u7grtxbhmh mw3qn s2015 16 statistical machine learning larry wasserman cmu none youtube lectures https www youtube com playlist list pljbui5mgii6bweuzf7he6nowwvgne y8r s2015 17 ml supervised learning michael littman charles isbell pushkar kolhe gatech ml udacity https eu udacity com course machine learning ud262 youtube lectures https www youtube com playlist list plawxtw4syapl0n6 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large datasets 10 605 in fall 2016 youtube lectures https www youtube com playlist list plnfbqxrw5mrhptfkadfwq0vcusi2iwecw f2016 23 math background for machine learning geoffrey gordon cmu 10 600 youtube lectures https www youtube com playlist list pl7y 1rk2ccsa339crwxmwuabrulbvpbcg f2016 24 statistical learning classification ali ghodsi university of waterloo none youtube lectures https www youtube com playlist list plehulrpyt1hzxdemu7k4etcf0ld b5adg 2017 25 machine learning andrew ng stanford university coursera ml https www coursera org learn machine learning youtube lectures https www youtube com playlist list pllsst5z dsk h9vyzkqkynwcitqhlrjln 2017 26 machine learning roni rosenfield cmu 10 601 http www cs cmu edu roni 10601 f17 youtube lectures https www youtube com playlist list pl7k0r4t5c10 g7cwcnhfzoaxlaininchk 2017 27 statistical machine learning ryan tibshirani larry wasserman cmu 10 702 http www stat cmu edu ryantibs statml youtube lectures https www youtube com playlist list pljbui5mgii6b7a0nm74zhtovqttc9dacv s2017 28 machine learning for computer vision fred hamprecht heidelberg university none youtube lectures https www youtube com playlist list plurasnb3n4ksqfyt8vbldsq9po9xtu8ry f2017 29 math background for machine learning geoffrey gordon cmu 10 606 10 607 https canvas cmu edu courses 603 assignments syllabus youtube lectures https www youtube com playlist list pl7y 1rk2ccsaqrtwoz95z gmcecvg5mza f2017 30 data visualization ali ghodsi university of waterloo none youtube lectures https www youtube com playlist list plehulrpyt1hzqoxehtnuytmd0anqvtyak 2017 31 machine learning for physicists florian marquardt uni erlangen n rnberg ml4phy 17 http www thp2 nat uni erlangen de index php 2017 machine learning for physicists by florian marquardt lecture videos https www video uni erlangen de course id 574 2017 32 machine learning for intelligent systems kilian weinberger cornell university cs4780 http www cs cornell edu courses cs4780 2018fa youtube lectures https www youtube com playlist list pll8olhzgyoq7bkvburthesalr7bonzbxs f2018 33 statistical learning theory and applications tomaso poggio lorenzo rosasco sasha rakhlin 9 520 6 860 https cbmm mit edu lh 9 520 youtube lectures https www youtube com playlist list plygkbdfnk iatlo6olw4swmiqgz4f2opy f2018 34 machine learning and data mining mike gelbart university of british columbia cpsc 340 https ubc cs github io cpsc340 youtube lectures https www youtube com playlist list plwmxhcz 53q02zleaxigki1jzffco6m b 2018 35 foundations of machine learning david rosenberg bloomberg foml https bloomberg github io foml home youtube lectures https www youtube com playlist list plnzuxoufsxnvftwtb1hl6mel1v32w0thi 2018 36 introduction to machine learning andreas krause eth z rich introml https las inf ethz ch teaching introml s18 youtube lectures https www youtube com playlist list plzn6ln6whln273tsqyfdrbusa o5nuesv 2018 37 machine learning fundamentals sanjoy dasgupta uc san diego mlf slides https drive google com drive folders 1l1rwv jmihlzipw0ztggn9 snwosa3m9 youtube lectures https www youtube com playlist list pl onphfckvqhuzctvgqic8w2shzkwlm0s 2018 38 machine learning jordan boyd graber university of maryland cmsc 726 http users umiacs umd edu jbg teaching cmsc 726 youtube lectures https www youtube com playlist list plegwunz91wfselyrcz7d1gwavifdazmgo 2015 2018 39 machine learning andrew ng stanford university cs229 http cs229 stanford edu syllabus autumn2018 html youtube lectures https www youtube com playlist list ploromvodv4rmigqp3wxshtmggzqpfvfbu 2018 40 machine intelligence h r tizhoosh uwaterloo syde 522 https kimialab uwaterloo ca kimia index php teaching syde 522 machine intelligence 2 youtube lectures https www youtube com playlist list pl4upcu5bnihwcx93gv6aqnkmvmwx4azot 2019 41 introduction to machine learning pascal poupart university of waterloo cs480 680 https cs uwaterloo ca ppoupart teaching cs480 spring19 youtube lectures https www youtube com playlist list pldaol1zkcqtw uzosvbneeckhsnug m0k s2019 42 advanced machine learning thorsten joachims cornell university cs 6780 https www cs cornell edu courses cs6780 2019sp lecture videos https cornell mediasite com mediasite catalog full f5d1cd3323f746cca80b2468bf97efd421 s2019 43 machine learning for structured data matt gormley carnegie mellon university 10 418 10 618 http www cs cmu edu mgormley courses 10418 schedule html youtube lectures https www youtube com playlist list pl4cxkujbvnvihrkp4bxufvrliwzes iep f2019 44 advanced machine learning joachim buhmann eth z rich ml2 aml https ml2 inf ethz ch courses aml lecture videos https video ethz ch lectures d infk 2019 autumn 252 0535 00l html f2019 45 machine learning for signal processing vipul arora iit kanpur mlsp http home iitk ac in vipular stuff 2019 mlsp html lecture videos https iitk my sharepoint com f g personal vipular iitk ac in enf97nzfsovbiyclc6yhfe4bluv6ca4u8lpqq4vtsdo xg f2019 46 foundations of machine learning animashree anandkumar caltech cms 165 http tensorlab cms caltech edu users anima cms165 2019 html youtube lectures https www youtube com playlist list plvnifwxslhca5guh0o92nemiwiqigvfqp 2019 47 machine learning for physicists florian marquardt uni erlangen n rnberg none lecture videos https www video uni erlangen de course id 778 2019 48 applied machine learning andreas m ller columbia university coms w4995 https www cs columbia edu amueller comsw4995s19 youtube lectures https www youtube com playlist list pl pvmaaanxiqgzqs2oi3owept dpmwtfa 2019 49 fundamentals of machine learning over networks hossein shokri ghadikolaei kth sweden mlons https sites google com view mlons course materials youtube lectures https www youtube com playlist list plwoztd81wfcebfrxdfnurdnt3abdlfg80 2019 50 foundations of machine learning and statistical inference animashree anandkumar caltech cms 165 http tensorlab cms caltech edu users anima cms165 2020 html youtube lectures https www youtube com playlist list plvnifwxslhcdlbyitallyboaepbmf1ahg 2020 51 machine learning rebecca willett and yuxin chen university of chicago stat 37710 cmsc 35400 https voices uchicago edu willett teaching stats37710 cmsc35400 s20 lecture videos https voices uchicago edu willett teaching stats37710 cmsc35400 s20 s2020 52 introduction to machine learning sanjay lall and stephen boyd stanford university ee104 cme107 http ee104 stanford edu youtube lectures https www youtube com playlist list ploromvodv4rn uy7 wms051 q1d6akxmk s2020 53 applied machine learning andreas m ller columbia university coms w4995 https www cs columbia edu amueller comsw4995s20 schedule youtube lectures https www youtube com playlist list pl pvmaaanxirnsw6wicpsvshfycrezmlm s2020 54 statistical machine learning ulrike von luxburg eberhard karls universit t t bingen stat ml https www tml cs uni tuebingen de teaching 2020 statistical learning index php youtube lectures https www youtube com playlist list pl05ump7r6ij2xcvrrzlokx6eohwaga2cc ss2020 55 probabilistic machine learning philipp hennig eberhard karls universit t t bingen prob ml https uni tuebingen de en 180804 youtube lectures https www youtube com playlist list pl05ump7r6ij1thaofy96m5ux3j21a6ynd ss2020 56 machine learning sarath chandar polymtl udem mila inf8953ce http sarathchandar in teaching ml fall2020 youtube lectures https www youtube com playlist list plimtcgowf et0mi ammqq0sijupwyaior f2020 57 machine learning erik bekkers universiteit van amsterdam uva ml https uvaml1 github io youtube lectures https www youtube com playlist list pl8fnqmh2k7jzhtvybkmvrmyxdymmgjj n f2020 58 neural networks for signal processing shayan srinivasa garani indian institute of science nn4sp https labs dese iisc ac in pnsil neural networks and learning systems i fall 2020 youtube lectures https www youtube com playlist list plgmdnelgj1czn1399dv7 u4vbnjflrsua f2020 59 introduction to machine learning dmitry kobak universit t klinikum t bingen none youtube lectures https www youtube com playlist list pl05ump7r6ij35shkldqccjsdntugy4fqt 2020 60 machine learning prml erik j bekkers universiteit van amsterdam uvaml 1 https uvaml1 github io youtube lectures https www youtube com playlist list pl8fnqmh2k7jzhtvybkmvrmyxdymmgjj n 2020 61 machine learning with kernel methods julien mairal and jean philippe vert inria ens paris saclay google ml kernels http members cbio mines paristech fr jvert svn kernelcourse course 2021mva index html youtube lectures https www youtube com playlist list pld93kgj6 edrknj27azmecbrlq1smkp o s2021 62 continual learning vincenzo lomonaco universit di pisa contlearn 21 https course continualai org background details youtube lectures https www youtube com playlist list plm6qxeab xkbfm5rgqp6wcr7jegdg51px 2021 63 causality christina heinze deml eth zurich causal 21 https stat ethz ch lectures ss21 causality php course materials youtube lectures https stat ethz ch lectures ss21 causality php course materials 2021 go to contents arrow 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materials html youtube lectures https www youtube com playlist list pliclbsfqnfaxomveqphi5er1lgf2 l9i4 2014 3 introduction to reinforcement learning david silver deepmind ucl rl http www0 cs ucl ac uk staff d silver web teaching html youtube lectures https www youtube com playlist list plqymg7htrazdm oyhwgpebj2mfcfzfobq 2015 4 reinforcement learning charles isbell chris pryby gatech michael littman brown rl udacity https eu udacity com course reinforcement learning ud600 youtube lectures https www youtube com playlist list plawxtw4syapniddwo9e2c7ixisu pdsnp 2015 5 reinforcement learning balaraman ravindran iit madras rl iitm https www cse iitm ac in ravi courses reinforcement 20learning html youtube lectures https www youtube com playlist list plndwvhi37uggqivcazcmtggeqhy9w7d9d 2016 6 deep reinforcement learning sergey levine uc berkeley cs 294 http rail eecs berkeley edu deeprlcoursesp17 youtube lectures https www youtube com playlist list plkfd6 40kjiwtmsbcv9ovjb3yao4sfwkx s2017 7 deep reinforcement learning sergey levine uc berkeley cs 294 http rail eecs berkeley edu deeprlcourse fa17 youtube lectures https www youtube com playlist list plkfd6 40kjiznc9cdbvtjaf2oyt8 vae3 f2017 8 deep rl bootcamp many legends uc berkeley deep rl https sites google com view deep rl bootcamp lectures youtube lectures https www youtube com channel uctgm vlxkuylprz ygajhow videos 2017 9 data efficient reinforcement learning lots of legends canary islands derl 17 http dalimeeting org dali2017 data efficient reinforcement learning html youtube lectures https www youtube com playlist list pl twvtpyd1vavdpxukup6w suzqq7e8k8 2017 10 deep reinforcement learning sergey levine uc berkeley cs 294 112 http rail eecs berkeley edu deeprlcourse fa18 youtube lectures https www youtube com playlist list plkfd6 40kjixjmr j5a1mkxk26gh qg37 2018 11 reinforcement learning pascal poupart university of waterloo cs 885 https cs uwaterloo ca ppoupart teaching cs885 spring18 youtube lectures https www youtube com playlist list pldaol1zkcqtxfjnio3tqqn6xmbbl07edc 2018 12 deep reinforcement learning and control katerina fragkiadaki and tom mitchell cmu 10 703 http www andrew cmu edu course 10 703 youtube lectures https www youtube com playlist list plpixoj hndsnfvowrklsuobmnf2j1l5ov 2018 13 reinforcement learning and optimal control dimitri bertsekas arizona state university rloc http web mit edu dimitrib www rlbook html lecture videos http web mit edu dimitrib www rlbook html 2019 14 reinforcement learning emma brunskill stanford university cs 234 http web stanford edu class cs234 index html youtube lectures https www youtube com playlist list ploromvodv4rosopzutgyctapigly2nd8u 2019 15 reinforcement learning day lots of legends microsoft research new york rld 19 https www microsoft com en us research event reinforcement learning day 2019 agenda youtube lectures https www youtube com playlist list pld7hfcn7lxre9nwex3up ricdi6 0mqvc 2019 16 new directions in reinforcement learning and control lots of legends ias princeton university ndrlc 19 https www math ias edu ndrlc youtube lectures https www youtube com playlist list plddzb3twjpz61sgqd6cbwcmtc275nrku3 2019 17 deep reinforcement learning sergey levine uc berkeley cs 285 http rail eecs berkeley edu deeprlcourse fa19 youtube lectures https www youtube com playlist list plkfd6 40kjiwhwjpgazj9vsj9cfmkb79a f2019 18 deep multi task and meta learning chelsea finn stanford university cs 330 https cs330 stanford edu youtube lectures https www youtube com playlist list ploromvodv4rmc6zfymnd7ug3lvvwaity5 f2019 19 rl theory seminars lots of legends earth rl theory sem https sites google com view rltheoryseminars past seminars youtube lectures https www youtube com channel ucfbfutc9rbkk6p b4r9eba videos 2020 20 deep reinforcement learning sergey levine uc berkeley cs 285 http rail eecs berkeley edu deeprlcourse fa20 youtube lectures https www youtube com playlist list pl iwqose6tfuriihcrlt wj9byivpbfgc f2020 21 introduction to reinforcement learning amir massoud farahmand vector institute university of toronto rl intro https amfarahmand github io introrl youtube lectures https www youtube com playlist list plcveixxl2xnbidq51a8ijwprq2ao0ykrq s2021 22 reinforcement learning antonio celani and emanuele panizon international centre for theoretical physics none youtube lectures https www youtube com playlist list plp0hsy2ubep8q2g3mfhgvgvqfemx0qrwm 2021 23 computational sensorimotor learning pulkit agrawal mit csail 6 884 csl https pulkitag github io 6 884 lectures youtube lectures https www youtube com playlist list plwnwxag kbxpmtis2fkwssf7hql2tcc78 s2021 24 reinforcement learning dimitri p bertsekas asu mit rl 21 http web mit edu dimitrib www rlbook html youtube lectures https www youtube com playlist list plmh30bg15sip79jrj mvf12uvb1qptpzn s2021 25 reinforcement learning sarath chandar cole polytechnique de montr al inf8953de https chandar lab github io inf8953de youtube lectures https www youtube com 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lecture html youtube lectures https www youtube com playlist list pli3niod p5aoxroztd1p6cclavu9rntc 2014 4 learning with structured data an introduction to probabilistic graphical models christoph lampert ist austria none youtube lectures https www youtube com playlist list pleqohzpnmtfa0wc1jxjovvorjlx8w0rgf 2016 5 probabilistic graphical models nicholas zabaras university of notre dame pgm https www zabaras com probabilistic graphical models youtube lectures https www youtube com playlist list pld pudzw85acv4bgdu7whpl37hm60w4rm 2018 6 probabilistic graphical models eric xing cmu 10 708 https sailinglab github io pgm spring 2019 lecture videos https sailinglab github io pgm spring 2019 lectures br youtube lectures https www youtube com playlist list plozgvqqhoumty2caqhl45tqp6kmdndcqn s2019 7 probabilistic graphical models eric xing cmu 10 708 https www cs cmu edu epxing class 10708 20 index html youtube lectures https www youtube com playlist list plozgvqqhoumtqxihcdcpoajooimrrcgzn s2020 8 uncertainty modeling in ai gim hee lee national university of singapura nus cs 5340 ch https www coursehero com sitemap schools 2652 national university of singapore courses 7821096 cs5340 cs 5340 nb https github com clear nus cs5340 notebooks youtube lectures https www youtube com playlist list plxg0cgqviygob9eyc8ixm27doxjp2sk0h 2020 21 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign 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minus sign s no course name university instructor s course webpage lecture videos year 1 bayesian neural networks variational inference lots of legends none youtube lectures https www youtube com playlist list plm4pv4kyyzgwub4bfy183hwghpl9ytva1 2014 now 2 variational inference chieh wu northeastern university none youtube lectures https www youtube com playlist list pldk2fd27cqzsd1sq3kbyl4vtv6gjxvpse 2015 3 deep learning and bayesian methods lots of legends hse moscow dlbm ss http deepbayes ru 2018 youtube lectures https www youtube com playlist list ple5rnuydzv9q01vwcp9bv7nhjg3j7mz62 2018 4 deep learning and bayesian methods lots of legends hse moscow dlbm ss http deepbayes ru youtube lectures https www youtube com playlist list ple5rnuydzv9qhe8vdstpu0o8yp63oecdw 2019 5 nordic probabilistic ai lots of legends ntnu trondheim probai https github com probabilisticai probai 2019 youtube lectures https www youtube com playlist list plry vw 9hv8s jkhxzvnd26kgjrp2ik 2019 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign movie camera medical imaging camera video camera heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign 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lectures https www youtube com playlist list plgshh6boff5ujaut4zriazc ofxolkagk 2015 3 medical imaging summer school lots of legends sicily miss 16 http iplab dmi unict it miss16 programme html youtube lectures https www youtube com playlist list pltrcr47ytx5ixiysnex3lkf16upaw59wa 2016 4 optical and ultrasound imaging opus lots of legends universit de lyon france opus 16 https opus2016lyon sciencesconf org resource page id 2 youtube lectures https www youtube com playlist list pl95ayovlx8gdukbxu r9wqrwwzdwckjti 2016 5 medical imaging summer school lots of legends sicily miss 18 http iplab dmi unict it miss programme htm youtube lectures https www youtube com playlist list pl veuglulxqux1dv4ia3xumx6auejmgga 2018 6 seminar on ai in healthcare lots of legends stanford cs 522 http cs522 stanford edu 2018 index html youtube lectures https www youtube com playlist list plyn zmpr1dtnqj ot l2v2egueh6oh 7w 2018 7 machine learning for healthcare david sontag peter szolovits csail mit mlhc 19 https mlhc19mit github io br mit 6 s897 https ocw mit edu courses electrical engineering and computer science 6 s897 machine learning for healthcare spring 2019 lecture notes youtube lectures https www youtube com playlist list plul4u3cngp60b0pqxvqygndcyctdu1q5j s2019 8 deep learning and medical applications lots of legends ipam ucla dlm 20 https www ipam ucla edu programs workshops deep learning and medical applications tab schedule lecture videos https www ipam ucla edu programs workshops deep learning and medical applications tab schedule 2020 9 stanford symposium on artificial intelligence in medicine and imaging lots of legends stanford aimi aimi 20 https aimi stanford edu news events aimi symposium agenda youtube lectures https www youtube com watch v tr2obil4il8 list ple6zdime5b7ir0odoobxbdblyy1eqlypx 2020 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign tada graph neural networks geometric dl confetti ball balloon heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage lecture videos year 1 deep learning on graphs and manifolds michael bronstein technion none youtube lectures https www youtube com playlist list plh39km3nuavcvouiibrabnhjv cwed1uv 2017 2 geometric deep learning on graphs and manifolds michael bronstein technische universit t m nchen none lec part1 https streams tum de mediasite play 1f3b894e78f6400daa7885c886b936fb1d br lec part2 https streams tum de mediasite play 6039c846b2f84e7a806024c06e3f5c5c1d 2017 3 eurographics symposium on geometry processing graduate school lots of legends siggraph london sgp 2017 http geometry cs ucl ac uk sgp2017 p gradschool youtube lectures https www youtube com playlist list plop ngxvomharqntglvnzujndznx3rdjz 2017 4 eurographics symposium on geometry processing graduate school lots of legends siggraph paris sgp 2018 https sgp2018 sciencesconf org resource page id 7 youtube lectures https www youtube com playlist list plvcorb dvamgpp8lyw7duvlxh 1vrrm v 2018 5 analysis of networks mining and learning with graphs jure leskovec stanford university cs224w http snap stanford edu class cs224w 2018 lecture videos http snap stanford edu class cs224w 2018 2018 6 machine learning with graphs jure leskovec stanford university cs224w http snap stanford edu class cs224w 2019 youtube lectures https www youtube com playlist list pl y8zk4dwcrqyasidb2mjj itw2 yyx6 2019 7 geometry and learning from data in 3d and beyond geometry and learning from data tutorials lots of legends ipam ucla gldt http www ipam ucla edu programs workshops geometry and learning from data tutorials lecture videos http www ipam ucla edu programs workshops geometry and learning from data tutorials tab schedule 2019 8 geometry and learning from data in 3d and beyond geometric processing lots of legends ipam ucla geopro http www ipam ucla edu programs workshops workshop i geometric processing lecture videos http www ipam ucla edu programs workshops workshop i geometric processing tab schedule 2019 9 geometry and learning from data in 3d and beyond shape analysis lots of legends ipam ucla shape analysis http www ipam ucla edu programs workshops workshop ii shape analysis lecture videos http www ipam ucla edu programs workshops workshop ii shape analysis tab schedule 2019 10 geometry and learning from data in 3d and beyond geometry of big data lots of legends ipam ucla geo bdata http www ipam ucla edu programs workshops workshop iii geometry of big data lecture videos http www ipam ucla edu programs workshops workshop iii geometry of big data tab schedule 2019 11 geometry and learning from data in 3d and beyond deep geometric learning of big data and applications lots of legends ipam ucla dgl bdata http www ipam ucla edu programs workshops workshop iv deep geometric learning of big data and applications lecture videos http www ipam ucla edu programs workshops workshop iv deep geometric learning of big data and applications tab schedule 2019 12 israeli geometric deep learning lots of legends israel igdl 20 https gdl israel github io schedule html lecture videos https www youtube com watch v c8 32ivn sg 2020 13 machine learning for graphs and sequential data stephan g nnemann technische universit t m nchen tum mlgs 20 https www in tum de en daml teaching summer term 2020 machine learning for graphs and sequential data lecture videos https www in tum de daml teaching mlgs s2020 14 machine learning with graphs jure leskovec stanford cs224w http web stanford edu class cs224w youtube lectures https www youtube com playlist list ploromvodv4rplkxipqhjhpgdqy7imnkdn w2021 15 geometric deep learning ammi lots of legends virtual gdl ammi https geometricdeeplearning com lectures youtube lectures https www youtube com playlist list pln2 demqetfq8yvuhbovahulnipyxkeu3 2021 16 summer school on geometric deep learning lots of legends dtu diku aau gdl dtu diku aau https geometric deep learning compute dtu dk lecture videos https geometric deep learning compute dtu dk talks and materials 2021 17 graph neural networks alejandro ribeiro university of pennsylvania ese 514 https gnn seas upenn edu youtube lectures https www youtube com channel uc yprqpieqkegog1tct0giq playlists f2021 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign hibiscus natural language processing cherry blossom sparkling heart heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage lecture videos year 1 computational linguistics i jordan boyd graber university of maryland cms 723 http users umiacs umd edu jbg teaching cmsc 723 youtube lectures https www youtube com playlist list plegwunz91wfupebli97 wueap90jo 15i 2013 2018 2 deep learning for natural language processing nils reimers tu darmstadt dl4nlp https github com ukplab deeplearning4nlp tutorial youtube lectures https www youtube com channel uc1zcutrfpjt6sv2kjk jkva videos 2015 2017 3 deep learning for natural language processing many legends deepmind oxford dl nlp http www cs ox ac uk teaching courses 2016 2017 dl youtube lectures https www youtube com playlist list pl613dyigmxozbtzhbyibqb0qtgk6ojbpm 2017 4 deep learning for speech language upc barcelona dl sl https telecombcn dl github io 2017 dlsl lecture videos https telecombcn dl github io 2017 dlsl 2017 5 neural networks for natural language processing graham neubig cmu nn4nlp http www phontron com class nn4nlp2017 code https github com neubig nn4nlp code youtube lectures https www youtube com playlist list pl8pytp1v4i8abxzdqtopb eqblvaz xpt 2017 6 neural networks for natural language processing graham neubig cmu nn4 nlp http www phontron com class nn4nlp2018 youtube lectures https www youtube com playlist list pl8pytp1v4i8ba7 ry4fob4 jfuj7vdkee 2018 7 deep learning for nlp min yen kan nus cs 6101 https www comp nus edu sg kanmy courses 6101 1810 youtube lectures https www youtube com playlist list plllwxvcs7ca5ed44ktcit7rmu hfaafxb 2018 8 neural networks for natural language processing graham neubig cmu nn4nlp http www phontron com class nn4nlp2019 youtube lectures https www youtube com playlist list pl8pytp1v4i8ajj7sy6sdtmjgkt7eo2vms 2019 9 natural language processing with deep learning abigail see chris manning richard socher stanford university cs224n http web stanford edu class cs224n youtube lectures https www youtube com playlist list ploromvodv4rohcuxmzknm7j3fvwbby42z 2019 10 natural language understanding bill maccartney and christopher potts cs224u https web stanford edu class cs224u youtube lectures https www youtube com playlist list ploromvodv4robpmcir6rnnulfan56js20 s2019 11 neural networks for natural language processing graham neubig carnegie mellon university cs 11 747 http www phontron com class nn4nlp2020 schedule html youtube lectures https www youtube com playlist list pl8pytp1v4i8cj7nmxmc8axv8wqkywj aj s2020 12 advanced natural language processing mohit iyyer umass amherst cs 685 https people cs umass edu miyyer cs685 youtube lectures https www youtube com playlist list plwnsvgp6czadmqx6qevbar3 vdbiowhjl f2020 13 machine translation philipp koehn johns hopkins university en 601 468 668 http mt class org jhu syllabus html youtube lectures https www youtube com playlist list plqrciudqdlg0lqx54o9jb4phj sli6zbq f2020 14 neural networks for nlp graham neubig carnegie mellon university cs 11 747 http www phontron com class nn4nlp2021 youtube lectures https www youtube com playlist list pl8pytp1v4i8akahej7loorlex pcxs xv 2021 15 deep learning for natural language processing kyunghyun cho new york university ds ga 1011 https drive google com drive folders 1ykxbtophay 65vhk 8ydzzqjwfjdd5ve youtube lectures https www youtube com playlist list pldh9u0f1xkw s c8ecgjpn hjz5jj1irf f2021 16 natural language processing with deep learning chris manning stanford university cs224n https web stanford edu class archive cs cs224n cs224n 1214 youtube lectures https www youtube com playlist list ploromvodv4rosh4v6133s9lfprhjembmj 2021 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign speaking head automatic speech recognition speech balloon thought balloon heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage lecture videos year 1 deep learning for speech language upc barcelona dl sl https telecombcn dl github io 2017 dlsl lecture videos https telecombcn dl github io 2017 dlsl br youtube videos https www youtube com playlist list pl 5dczhuhzkwef9ljijoc x5ghrlntiku 2017 2 speech and audio in the northeast many legends google nyc sane 15 http www saneworkshop org sane2015 youtube videos https www youtube com playlist list plbjwrpcgwk7szob4utvilwwnrg84l9o5i 2015 3 automatic speech recognition samudra vijaya k tifr none youtube videos https www youtube com channel uchk6uq1cr9j3k5knmisyunw videos 2016 4 speech and audio in the northeast many legends google nyc sane 17 http www saneworkshop org sane2017 youtube videos https www youtube com playlist list plbjwrpcgwk7tnlabvu s90zqsblo3bwjg 2017 5 speech and audio in the northeast many legends google cambridge sane 18 http www saneworkshop org sane2018 youtube videos https www youtube com playlist list plbjwrpcgwk7sjmann8jqosyhime6djhmn 2018 1 deep learning for speech recognition many legends aoe none youtube videos https www youtube com playlist list plm4pv4kyyzgyfycxv6ypwakvor2gmhnqd 2015 2018 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign fire modern computer vision camera flash movie camera heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign s no course name university instructor s course webpage lecture videos year 1 microsoft computer vision summer school classical lots of legends lomonosov moscow state university none youtube videos https www youtube com playlist list plbwkcm5vdisyu54xfug1zoxqtulqvicju br russian mirror https www youtube com playlist list pl cknuvayaup0ecl7ko8qy4ety3tidfh 2011 2 computer vision classical mubarak shah ucf cap 5415 http crcv ucf edu courses cap5415 fall2012 index php youtube lectures https www youtube com playlist list pld3hlsjsx imk bpmb h3aqjfkzs9xgzm 2012 3 image and multidimensional signal processing classical william hoff colorado school of mines csci 510 eeng 510 http inside mines edu whoff courses eeng510 youtube lectures https www youtube com playlist list plyed3w677alnv8htn0f9xh ahe1azpftv 2012 4 computer vision classical william hoff colorado school of mines csci 512 eeng 512 http inside mines edu whoff courses eeng512 index htm youtube lectures https www youtube com playlist list pl4b3f8d4a5cad8da3 2012 5 image and video processing from mars to hollywood with a stop at the hospital guillermo sapiro duke university none youtube videos https www youtube com playlist list plz9qnfmhz a79y1stvuuqgyl o0fzh2rs 2013 6 multiple view geometry classical daniel cremers technische universit t m nchen mvg https vision in tum de teaching ss2014 mvg2014 youtube lectures https www youtube com playlist list pltbdjv 4f ejn6udz34tht9eviw7lbeo4 2013 7 mathematical methods for robotics vision and graphics justin solomon stanford university cs 205a http graphics stanford edu courses cs205a youtube lectures https www youtube com playlist list plq3uicqqtfnvq vzflhykhaqzitxokswi 2013 8 computer vision classical mubarak shah ucf cap 5415 http crcv ucf edu courses cap5415 fall2014 index php youtube lectures https www youtube com playlist list pld3hlsjsx imkp68wfkzjviptd8ie5u 9 2014 9 computer vision for visual effects classical rich radke rensselaer polytechnic institute ecse 6969 https www ecse rpi edu rjradke cvfxcourse html youtube lectures https www youtube com playlist list pluh62q4sv7bujlklt84hfqswfw36mdd5a s2014 10 autonomous navigation for flying robots juergen sturm technische universit t m nchen autonavx https jsturm de wp teaching autonavx slides youtube lectures https www youtube com playlist list pltbdjv 4f ekbcus1hmmtsnxv4juofrzg 2014 11 slam mobile robotics cyrill stachniss universitaet freiburg robotmapping http ais informatik uni freiburg de teaching ws13 mapping youtube lectures https www youtube com playlist list plgnqpqtftogqrz4o5qzbihgl3b1jhimn 2014 12 computational photography irfan essa david joyner arpan chakraborty cp udacity https eu udacity com course computational photography ud955 youtube lectures https www youtube com playlist list plawxtw4syapn unawtrmley4pese4oziy 2015 13 introduction to digital image processing rich radke rensselaer polytechnic institute ecse 4540 https www ecse rpi edu rjradke improccourse html youtube lectures https www youtube com playlist list pluh62q4sv7buf60vkjepfcoqc8shxmndx s2015 14 lectures on digital photography marc levoy stanford google research lodp https sites google com site marclevoylectures youtube lectures https www youtube com playlist list pl7ddpxyvfxspun0n gobf1gxoca da 7i 2016 15 introduction to computer vision foundation aaron bobick irfan essa arpan chakraborty cv udacity https eu udacity com course introduction to computer vision ud810 youtube lectures https www youtube com playlist list plawxtw4syapnbdacyrk kb rukuxqblcm 2016 16 computer vision syed afaq ali shah university of western australia none youtube lectures https www youtube com playlist list plvqb6 mdbcdlnt84lk nvboqcxllotr8j 2016 17 photogrammetry i ii cyrill stachniss university of bonn pg i ii https www ipb uni bonn de photogrammetry i ii youtube lectures https www youtube com playlist list plgnqpqtftogrsi5vzy9piqpnwhjq bkn1 2016 18 deep learning for computer vision upc barcelona dlcv 16 http imatge upc github io telecombcn 2016 dlcv br dlcv 17 https telecombcn dl github io 2017 dlcv br dlcv 18 https telecombcn dl github io 2018 dlcv youtube lectures https www youtube com playlist list pl 5emc3hqtbbuatfp4wsfd2y2vqefqcap 2016 2018 19 convolutional neural networks andrew ng stanford university deeplearning ai https www deeplearning ai deep learning specialization youtube lectures https www youtube com playlist list plkdae6sczn6gl29aoe31iwdvwsg kndzf 2017 20 variational methods for computer vision daniel cremers technische universit t m nchen vmcv https vision in tum de teaching ws2016 vmcv2016 youtube lectures https www youtube com playlist list pltbdjv 4f ej7a2iih5l5ztqqrwyjp2ri 2017 21 winter school on computer vision lots of legends israel institute for advanced studies ws cv http www as huji ac il cse youtube lectures https www youtube com playlist list pltn74qx5mpssnia5tt6w o0ogyeekscug 2017 22 deep learning for visual computing debdoot sheet iit kgp nptel https onlinecourses nptel ac in noc18 ee08 preview notebooks https github com iitkliv dlvcnptel youtube lectures https www youtube com playlist list pluv3gm6 gse1biyakccxb3fan4wblyfwf 2018 23 the ancient secrets of computer vision joseph redmon ali farhadi tascv https pjreddie com courses computer vision tascv uw https courses cs washington edu courses cse455 18sp youtube lectures https www youtube com playlist list pljmxczuzeychvw5yysu92wry8iwhtuq7p 2018 24 modern robotics kevin lynch northwestern robotics modern robot http hades mech northwestern edu index php modern robotics youtube lectures https www youtube com playlist list plgglp4f rq02vx0oqq5vrcxbjrzamydfx 2018 25 digial image processing alex bronstein technion cs236860 https vistalab technion github io cs236860 info youtube lectures https www youtube com playlist list plm0a6z788yazoxuywda9y3n i2upij1ep 2018 26 mathematics of imaging variational methods and optimization in imaging lots of legends institut henri poincar workshop 1 http www ihp fr sites default files conf1 04 au 08 fevr imaging2019 pdf youtube lectures https www youtube com playlist list pl9kd4mpdvwcazd5aq p1trlliyckrloxw 2019 27 deep learning for video xavier gir upc barcelona deepvideo https mcv m6 video github io deepvideo 2019 youtube lectures https www youtube com playlist list pl 5emc3hqtbbpy 627gornj09pzrnqgpd 2019 28 statistical modeling for shapes and imaging lots of legends institut henri poincar paris workshop 2 https imaging in paris github io semester2019 workshop2prog youtube lectures https www youtube com playlist list pl9kd4mpdvwcazd5aq p1trlliyckrloxw 2019 29 imaging and machine learning lots of legends institut henri poincar paris workshop 3 https imaging in paris github io semester2019 workshop3prog youtube lectures https www youtube com playlist list pl9kd4mpdvwcazd5aq p1trlliyckrloxw 2019 30 computer vision jayanta mukhopadhyay iit kgp cv nptel https nptel ac in courses 106 105 106105216 youtube lectures https nptel ac in courses 106 105 106105216 2019 31 deep learning for computer vision justin johnson umichigan eecs 498 007 https web eecs umich edu justincj teaching eecs498 lecture videos http leccap engin umich edu leccap site jhygcph151x25gjj1f0 br youtube lectures https www youtube com playlist list pl5 tkqafazfbzxjbhtzdvcwe0zbhomg7r 2019 32 sensors and state estimation 2 cyrill stachniss university of bonn none youtube lectures https www youtube com playlist list plgnqpqtftogqh j16imwdlji18swq2pz6 s2020 33 computer vision iii detection segmentation and tracking laura leal taix tu m nchen cv3dst https dvl in tum de teaching cv3dst ss20 youtube lectures https www youtube com playlist list plog3nopcjkbnegyffektlxxmfv1otkmcs s2020 34 advanced deep learning for computer vision laura leal taix and matthias nie ner tu m nchen adl4cv https dvl in tum de teaching adl4cv ss20 youtube lectures https www youtube com playlist list plog3nopcjkbnjhuhmixu4ise4z4f2jm39 s2020 35 computer vision foundations fred hamprecht universit t heidelberg cvf https hci iwr uni heidelberg de ial cvf youtube lectures https www youtube com playlist list plurasnb3n4krabnmiygd77hyogzo9wpde ss2020 36 mit vision seminar lots of legends mit mit vision https sites google com view visionseminar past talks youtube lectures https www youtube com channel uclmifkfyfcnnzs6iwylpi9g videos 2015 now 37 tum ai guest lectures lots of legends technische universit t m nchen tum ai https niessner github io tum ai lecture series youtube lectures https www youtube com playlist list plq8y4kiibzy8kmlz7crqz bjbdywsflxt 2020 now 38 seminar on 3d geometry vision lots of legends virtual 3dgv seminar https 3dgv github io youtube lectures https www youtube com playlist list plzk0jtn0g8e xvtfsiv67q8iz1czo fpv 2020 now 39 event based robot vision guillermo gallego technische universit t berlin evis ss20 https sites google com view guillermogallego teaching event based robot vision youtube lectures https www youtube com playlist list pl03gm3nzjvgufyuh3v5x8jvonjrgfcal8 2020 now 40 deep learning for computer vision vineeth balasubramanian iit hyderabad dl cv 20 https onlinecourses nptel ac in noc20 cs88 preview youtube lectures https www youtube com playlist list plyqspqzte6m pi riz4o1jegffhju9ggg 2020 41 deep learning for visual computing peter wonka kaust sa none youtube lectures https www youtube com playlist list plmpqleui13s2dhbw6kttxwqma8rehlfze 2020 42 computer vision yogesh rawat university of central florida cap5415 cv https www crcv ucf edu courses cap5415 fall 2020 schedule youtube lectures https www youtube com playlist list pld3hlsjsx ikm5il1hgmdb z62beoikfx f2020 43 multimedia signal processing mark hasegawa johnson uiuc ece 417 msp https courses engr illinois edu ece417 fa2020 lecture videos https mediaspace illinois edu channel ece 20417 26816181 f2020 44 computer vision andreas geiger universit t t bingen comp vis https uni tuebingen de fakultaeten mathematisch naturwissenschaftliche fakultaet fachbereiche informatik lehrstuehle autonomous vision lectures computer vision youtube lectures https www youtube com playlist list pl05ump7r6ij35l2mhgzis8aehz7mg381 s2021 45 3d computer vision lee gim hee national univeristy of singapura none youtube lectures https www youtube com playlist list plxg0cgqviygp47ervqhw v7fvnuovjeaz 2021 46 deep learning for computer vision fundamentals and applications t dekel et al weizmann institute of science dl4cv https dl4cv github io schedule html youtube lectures https www youtube com playlist list pl z2 u9mijdngfm7 f2fz9zxjvrp jhjv s2021 47 current topics in ml methods in 3d and geometric deep learning animesh garg others university of toronto csc 2547 http www pair toronto edu csc2547 w21 youtube lectures https www youtube com channel ucrsmaxnwu6sgccwevw12dfg videos 2021 48 first principles of computer vision shree k nayar columbia university fpcv https fpcv cs columbia edu youtube lectures https www youtube com channel ucf0wb91t8ky6auycqv0cclw videos 2021 49 self driving cars andreas geiger universit t t bingen sdc 21 https uni tuebingen de de 123611 youtube lectures https www youtube com playlist list pl05ump7r6ij321zzkxk6xcqxaaayjqbzr w2021 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus 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lkre13rmuc2aybrvvaxo5demibh 2013 3 machine learning summer school lots of legends mpi is t bingen mlss 13 http mlss tuebingen mpg de 2013 2013 index html youtube lectures https www youtube com playlist list plqjm7rc5 exfv6rxapzzzlzo93hl0v91e 2013 4 graduate summer school computer vision lots of legends ipam ucla gss cv http www ipam ucla edu programs summer schools graduate summer school computer vision video lectures http www ipam ucla edu programs summer schools graduate summer school computer vision tab schedule 2013 5 machine learning summer school lots of legends reykjavik university mlss 14 http mlss2014 hiit fi youtube lectures https www youtube com playlist list plqdbxunkqow2nkn7vxyqirkwcqrkqyosf 2014 6 machine learning summer school lots of legends pittsburgh mlss 14 http www mlss2014 com youtube lectures https www youtube com playlist list plzso 6 bsqhqciyxe3ycglxhmjk3xv7iz 2014 7 deep learning summer school lots of legends universit de montr al dlss 15 https sites google com site deeplearningsummerschool home youtube lectures http videolectures net deeplearning2015 montreal 2015 8 biomedical image analysis summer school lots of legends centralesupelec paris none youtube lectures https www youtube com playlist list plgshh6boff5ujaut4zriazc ofxolkagk 2015 9 mathematics of signal processing lots of legends hausdorff institute for mathematics sigproc http www him uni bonn de signal processing 2016 youtube lectures https www youtube com playlist list plul8lct3ajqsqo3lr5rbwxj92rsgrudtx 2016 10 microsoft research machine learning course s v n vishwanathan and prateek jain ms research none youtube lectures https www youtube com playlist list pl34iye0uxtxo7vpxgfkmm6kbgzqwjf9kf 2016 11 deep learning summer school lots of legends universit de montr al dl ss 16 https sites google com site deeplearningsummerschool2016 home youtube lectures https www youtube com playlist list pl5bqic6xopcbb fvnhmd1nevlqkwgzqyr 2016 12 lisbon machine learning school lots of legends instituto superior t cnico portugal lxmls 16 http lxmls it pt 2016 youtube lectures https www youtube com playlist list pltolj8m4ao fymxxbiou6sf1ngflb5eix 2016 13 machine learning advances and applications seminar lots of legends fields institute university of toronto mlaas 16 http www fields utoronto ca activities 16 17 machine learning youtube lectures https www youtube com playlist list plfsvaysmwskuqcrkudapp40lx i08d0qk br video lectures http www fields utoronto ca video archive event 2267 2016 2017 14 machine learning advances and applications seminar lots of legends fields institute university of toronto mlaas 17 http www fields utoronto ca activities 17 18 machine learning video lectures http www fields utoronto ca video archive event 2487 2017 2018 15 machine learning summer school lots of legends mpi is t bingen mlss 17 http mlss tuebingen mpg de 2017 index html youtube lectures https www youtube com playlist list plqjm7rc5 exfuovoycdkikfck8yeucnl9 2017 16 representation learning lots of legends simons institute replearn https simons berkeley edu workshops abstracts 3750 youtube lectures https www youtube com playlist list plgkuh lkre13unv4ztswuxciuz7x zdhz 2017 17 foundations of machine learning lots of legends simons institute ml bootcamp https simons berkeley edu workshops abstracts 3748 youtube lectures https www youtube com playlist list plgkuh lkre11gbzwneln vzdlhyejo7yd 2017 18 optimization statistics and uncertainty lots of legends simons institute optim stats https simons berkeley edu workshops abstracts 4795 youtube lectures https www youtube com playlist list plgkuh lkre13acd44z2fh ivp1e8ip5jo 2017 19 deep learning theory algorithms and applications lots of legends tu berlin dl taa http doc ml tu berlin de dlworkshop2017 youtube lectures https www youtube com playlist list pljozdkh8t5kqcnv v1w2tapvtjdzyiohw 2017 20 deep learning and reinforcement learning summer school lots of legends universit de montr al dlrl 2017 https mila quebec en cours deep learning summer school 2017 lecture videos http videolectures net deeplearning2017 montreal 2017 21 statistical physics methods in machine learning lots of legends international centre for theoretical sciences tifr spmml https www icts res in discussion meeting spmml2017 youtube lectures https www youtube com playlist list pl04qvxpjcnjhtl3iivyfrmogdhwtpn7yj 2017 22 lisbon machine learning school lots of legends instituto superior t cnico portugal lxmls 17 http lxmls it pt 2017 youtube lectures https www youtube com playlist list pltolj8m4ao furfnzejccnvuw2 fej73n 2017 23 interactive learning lots of legends simons institute berkeley il 2017 https simons berkeley edu workshops schedule 3749 youtube lectures https www youtube com playlist list plgkuh lkre10t2pof wzxh0ckdpyvanux 2017 24 computational challenges in machine learning lots of legends simons institute berkeley ccml 17 https simons berkeley edu workshops schedule 3751 youtube lectures https www youtube com playlist list plgkuh lkre12exz4dnvc8oervo2 af4iu 2017 25 foundations of data science lots of legends simons institute ds bootcamp https simons berkeley edu workshops abstracts 6680 youtube lectures https www youtube com playlist list plgkuh lkre13r1qrnrejj3f498 nursf3 2018 26 deep learning and bayesian methods lots of legends hse moscow dlbm ss http deepbayes ru 2018 youtube lectures https www youtube com playlist list ple5rnuydzv9q01vwcp9bv7nhjg3j7mz62 2018 27 new deep learning techniques lots of legends ipam ucla ipam workshop https www ipam ucla edu programs workshops new deep learning techniques tab schedule youtube lectures https www youtube com playlist list plhyi3fbmv0sdm0zxj31hwjg9t9q0v2xyn 2018 28 deep learning and reinforcement learning summer school lots of legends university of toronto dlrl 2018 https dlrlsummerschool ca 2018 event lecture videos http videolectures net dlrlsummerschool2018 toronto 2018 29 machine learning summer school lots of legends universidad aut noma de madrid spain mlss 18 http mlss ii uam es mlss2018 index html youtube lectures https www youtube com channel ucbpjhr eior 7jfh3hmvhq videos br course videos http mlss ii uam es mlss2018 speakers html 2018 30 theoretical basis of machine learning lots of legends international centre for theoretical sciences tifr tbml 18 https www icts res in discussion meeting tbml2018 lecture videos https www icts res in discussion meeting tbml2018 talks br youtube videos https www youtube com playlist list pl04qvxpjcnjj1dgnxxfbo2fksju4r ggr 2018 31 polish view on machine learning lots of legends warsaw plinml 18 https plinml mimuw edu pl youtube videos https www youtube com playlist list ploawrlj9tdhpca6n9dzq6gpxboyuumdrp 2018 32 big data analysis in astronomy lots of legends tenerife bdaa 18 http research iac es winterschool 2018 pages book ws2018 php youtube lectures https www youtube com playlist list plm4pv4kyyzgx42w5psp3itetp0u penti 2018 33 machine learning advances and applications seminar lots of 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ai institute 2019 youtube lectures https www youtube com playlist list pld7hfcn7lxre30qq36it2xcljxc340o d 2019 43 deep learning for science school many folks lbnl berkeley dlfss https dl4sci school lbl gov agenda youtube lectures https www youtube com playlist list pl20s5eeaposvfveyhcpouzu7zkbcr5 el 2019 44 emerging challenges in deep learning lots of legends simons institute berkeley ecdl https simons berkeley edu workshops schedule 10629 youtube lectures https www youtube com playlist list plgkuh lkre10bpafdrv0fg2vnuwewxwvd 2019 45 full stack deep learning pieter abbeel and many others uc berkeley fsdl m19 https fullstackdeeplearning com march2019 youtube lectures day 1 https www youtube com playlist list pl1t8fo7arwlcf3hc4vmevblh8hzm nbeb br day 2 https www youtube com playlist list pl1t8fo7arwlf6twwdstb pcwlubnlrkrm 2019 46 algorithmic and theoretical aspects of machine learning lots of legends iiit bengaluru acm ml https india acm org education machine learning br nptel https nptel ac in courses 128 106 128106011 youtube lectures https nptel ac in courses 128 106 128106011 2019 47 deep learning and reinforcement learning summer school lots of legends amii edmonton canada dlrl 2019 https dlrlsummerschool ca past years youtube lectures https www youtube com playlist list plklhhkvvu8 axmpqznyg e 2ntd0tje8v 2019 48 mathematics of machine learning summer graduate school lots of legends university of washington moml sgs http www msri org summer schools 866 schedule moml ss http mathofml cs washington edu youtube lectures https www youtube com playlist list pltpqex 31jxhgucush5j7ogneorofocw9 2019 49 workshop on theory of deep learning where next lots of legends ias princeton university wtdl https www math ias edu wtdl youtube lectures https www youtube com playlist list plddzb3twjpz5dqqg s rgjqsfeh4dqqfq 2019 50 computational vision summer school lots of legends black forest germany cvss 2019 http orga cvss cc program cvss 2019 youtube lectures https www youtube com playlist list plecnfjwzkqxsvidolvltwq9s7sisx1qtc 2019 51 learning under complex structure lots of legends mit lucs https mifods mit edu complex php youtube lectures https www youtube com playlist list plm4pv4kyyzgwhihcay6zyr7m9hhfo4vud 2020 52 machine learning summer school lots of legends mpi is t bingen virtual mlss http mlss tuebingen mpg de 2020 schedule html youtube lectures https www youtube com channel ucbogpkdhquyevvjuzs5wtxw videos ss2020 53 eastern european machine learning summer school lots of legends krak w poland virtual eeml https www eeml eu program youtube lectures https www youtube com playlist list plaky4p4v3ge1j01foy2feglv4jrntqj84 s2020 54 lisbon machine learning summer school lots of legends lisbon portugal virtual lxmls http lxmls it pt 2020 page id 19 youtube lectures https www youtube com channel uckvfzwgt1jr75uvslgp9 mw s2020 55 workshop on new directions in optimization statistics and machine learning lots of legends institute of advanced study princeton ml opt new dir https www ias edu video workshop 2020 0415 16 youtube lectures https www youtube com playlist list plddzb3twjpz4ri6i0midesiepyk4lx17q 2020 56 mediterranean machine learning school lots of legends italy virtual m2l school https www m2lschool org talks youtube lectures https www youtube com playlist list plf wkqrv4u1yrbfnwn8cxxyrmxld sked 2021 57 mathematics of machine learning one world seminar lots of legends virtual 1w ml https sites google com view oneworldml past events youtube lectures https www youtube com channel ucz7wlgxs20czugkfxhfcnfg videos 2020 now 58 deep learning theory summer school lots of legends princeton university virtual dlt 21 https deep learning summer school princeton edu youtube lectures https www youtube com playlist list pl2mb9ggluejj fnjj8rwgz4nc hcsxfmu 2021 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus 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artificial youtube lectures https www youtube com playlist list plhwo5ntex8iy9xhpswwas451ngvuqbe7u 2017 now 4 deep learning alchemy or science lots of legends institute for advanced study princeton dlas https video ias edu deeplearning 2019 0222 br agenda https www math ias edu tml dlasagenda youtube lectures https www youtube com playlist list plddzb3twjpz7aaxhihalboh8l6 uxmmnp 2019 go to contents arrow heading up https github com kmario23 deep learning drizzle contents heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus 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sign heavy minus sign white large square optimization courses which form the foundation for ml dl rl white large square computer vision courses which are dl ml heavy white large square speech recognition courses which are dl heavy white large square structured courses on geometric graph neural networks white large square section on autonomous vehicles white large square section on computer graphics with ml dl focus heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign 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minus sign heavy minus sign heavy minus sign around the web earth asia montreal ai http www montreal ai ai4all pdf upc dlai 2018 https telecombcn dl github io 2018 dlai upc dlai 2019 https telecombcn dl github io dlai 2019 www hashtagtechgeek com https www hashtagtechgeek com 2019 10 250 machine learning deep learning videos courseware html upc barcelona idl 2020 https telecombcn dl github io idl 2020 upc dlai 2020 https telecombcn dl github io dlai 2020 heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign contributions pray if you find a course that fits in any of the above categories i e dl ml rl cv nlp and the course has lecture videos with slides being optional then please raise an issue or send a pr by updating the course according to the above format heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign support moneybag optional if you re a kind samaritan and want to support me please do so if possible for which i would eternally be thankful and most importantly your contribution imbues me with greater motivation to work particularly in hard times pray https www paypalobjects com en us i btn btn donatecc lg gif https www paypal com cgi bin webscr cmd s xclick hosted button id nt3eats5n35wu vielen lieben dank blue heart heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign heavy minus sign 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explainable-ai probability | ai |
Quaver.Website | quaver website discord https discordapp com api guilds 354206121386573824 widget png style shield https discord gg nja8vfr the front end website for quaver setting up install the dependencies using npm install compile the project using tsc create a config folder in the dist directory and copy the config example json file renamed to config json fill in the appropriate configuration details run using node index js license this project is licensed under the agpl 3 0 https github com swan quaver website blob master license license | quaver game rhythm-game website typescript nodejs | front_end |
sf-chain-guides | build a blockchain and cryptocurrency from scratch the official code for the build a blockchain and cryptocurrency from scratch course on udemy by david katz check out the course https www udemy com build blockchain https www udemy com build blockchain the blockchain is a revolutionary technology that allows for the secure distributed decentralized storage of information over the past few years the blockchain has taken the engineering landscape by storm many people in the industry predict that the blockchain will disrupt the ways we interact with technology on the same way the internet did in the early 2000s some of the main course highlights build a blockchain in the object oriented programming style generate hashes for blocks in the chain unit test components of the blockchain create an api around the blockchain create a real time connected peer to peer server implement a proof of work algorithm sign transactions with cryptography and digital signature create a transaction pool for a real time list of incoming data include transactions in core blocks of the chain ultimately knowledge of the blockchain will set you up for success in the future as an engineer in a blockchain dominated world | blockchain |
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Dot-Matrix-Game | dot matrix game refer to report for materials info atmega1284 layout in jpg file | os |
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ain | lint https github com defich ain actions workflows lint yaml badge svg https github com defich ain actions workflows lint yaml tests https github com defich ain actions workflows tests e2e yaml badge svg https github com defich ain actions workflows tests e2e yaml build dev https github com defich ain actions workflows build dev yaml badge svg https github com defich ain actions workflows build dev yaml defichain logo doc img defichain logo svg what is defichain https defichain com defichain s primary vision is to enable decentralized finance with bitcoin grade security strength and immutability it s a blockchain dedicated to fast intelligent and transparent financial services accessible by everyone for more information visit the defichain website https defichain com read our white paper https defichain com white paper downloadable binaries are available from the github releases https github com defich ain releases page note master branch is a development branch and is not suitable for production please download tagged releases https github com defich ain releases or compile from the release tags bitcoin core defichain is a fork on bitcoin core https github com bitcoin bitcoin from commit 7d6f63c https github com bitcoin bitcoin commit 7d6f63cc2c2b9c4f07a43619eef0b7314474fffd which is slightly after v0 18 1 of bitcoin core defichain has done significant modifications from bitcoin core for instance moving from proof of work to proof of stake masternode model community fund support bitcoin blockchain block anchoring increased decentralized financial transaction and opcode support etc configuration defaults mainnet ports 8555 4 testnet ports 18555 4 changi ports 20554 4 devnet ports 21554 4 regnet ports 19555 4 etc merges from upstream bitcoin core will be done selectively if it applies to the improved functionality and security of defichain quick start running a node doc setup nodes md running a node with docker doc setup nodes docker md running a masternode doc setup masternodes md building from scratch doc build quick md license defichain is released under the terms of the mit license see copying copying for more information or see https opensource org licenses mit development process the master branch is regularly built and tested but is not guaranteed to be completely stable tags https github com defich ain tags are created regularly to indicate new official stable release versions of defichain the contribution workflow is described in contributing md contributing md questions pull requests are warmly welcomed reach us at engineering defichain com mailto engineering defichain com for any questions or collaborations | bitcoin defi decentralized-finance decentralization finance dfi defi-blockchain defichain blockchain cryptocurrency | blockchain |
beginner_nlp | a curated list of beginner resources in natural language processing maintainer dibya chakravorty https github com gutfeeling contributions feel free to send pull requests https github com gutfeeling beginner nlp pulls or email me dibyachakravorty gmail com how this list got started on november 10 2016 a hacker news hn user aarohmankad asked the hn community for suggestions on beginner nlp resources this ask hn thread https news ycombinator com item id 12916498 became popular and stayed in the front page for some time in this time it gathered plenty of community generated suggestions about beginner nlp resources this list is an attempt to summarize this discussion into a coherent list of resources i also wrote a blog post https medium com ask hn summaries how to get into natural language processing 2011bf9f4cfe szm4ljj93 on this table of contents books books moocs moocs youtube videos youtube videos online university courses online university courses packages to play with packages to play with academic papers academic papers learning by doing learning by doing open source projects open source projects fun ideas fun ideas apis apis user groups user groups other guides other guides books speech and language processing http amzn to 2jr4nsq classic and standard textbook in nlp pre publication draft of 3rd edition available here https web stanford edu jurafsky slp3 natural language processing with python http amzn to 2icxf6g application oriented book examples are in python nltk free online version here http nltk org book taming text http amzn to 2jr2shd application oriented book examples are in java foundations of statistical natural language processing http amzn to 2jqssug classic text on statistical nlp goes deep into the implementation of parsers taggers etc handbook of natural language processing http amzn to 2j1woda a complete treatment of nlp that starts from the historical roots and ends with the modern methods of nlp statistical machine translation http amzn to 2jzflvh learn how to make a service like google translate introduction to information retrieval http amzn to 2i9mclo learn the nuts and bolts of services like google search and google news search text classification clustering etc prolog and natural language analysis http amzn to 2j4rvdh implement nlp algortihms in prolog moocs coursera course offered by university of michigan https www coursera org learn natural language processing introductory course that covers all prerequisite materials favored programming language is python dicontinued coursera course offered by comlumbia university available on academic torrents http academictorrents com details f99e7184fca947ee8f77901679e171fcadbf82e7 theory and concept oriented course only the course materials are available at this point youtube videos video series by jurafsky and martin https www youtube com watch v nfoudtpbv68 list pl6397e4b26d00a269 jurafsky and martin are both professors at stanford and they have written multiple classic textbooks on nlp stanford cs224d deep learning in nlp https www youtube com playlist list pliivrb6g w0i uoos6cdh 5nkuyxy hxe applicatin of deep learning in nlp nlp with python and nltk https www youtube com playlist list plqvvvaa0qudf2jswnfigklibinznic4hl application oriented video series using python and nltk online university courses machine translation course at the university of pennsylvania http mt class org penn packages to play with nltk http www nltk org most popular nlp library in python excellent documentation in the form of a book http amzn to 2fdayie free online version http nltk org book powerful and extensible stanford corenlp http stanfordnlp github io corenlp fast and feature rich nlp library written in java an online demo is available here http corenlp run spacy https spacy io another emerging nlp library in python fast and state of the art tries to maintain an uniform api while implementing state of the art algorithms they have a blog http explosion ai and an online demo https demos explosion ai displacy apache tika https tika apache org offers an unified interface for extracting text data and meta data from many different file formats ppt pdf etc and analysis academic papers deep learning in nlp https github com andrewt3000 dl4nlp a github repo that collects papers on deep learning in nlp learning by doing often the best way to learn is to contribute to an existing open source nlp project or implementing a fun idea open source projects betty https github com pickhardt betty betty is a open source project with both real life use and practical nlp considerations and is looking for new maintainers fun ideas interactive fiction parser based fiction http iftechfoundation org frequently asked questions a video game where the player s interactions primarily involve text listen to this illuminating floss podcast https www youtube com watch v tsr4okyxwjk on the topic apis ibm watson cloud https www ibm com watson developercloud from the makers of ibm watson https en wikipedia org wiki watson computer it lets you integrate nlp functionality in your app via an api there s a free tier free trial user groups acm special interest group in ai https sigai acm org index html if you are craving for some face to face human contact other guides quora question on how to get into nlp https www quora com how do i learn natural language processing awesome nlp on github https github com keonkim awesome nlp a github repo containing a curated list of nlp resources | nlp nlp-resources natural-language-processing | ai |
frontend-test | frontend developer test please fork this repository to begin your front end developer test then download your repository and follow the installation instructions installation installation requires node js on your computer npm install node server js instructions there should now be a web server running at http localhost 8888 follow the instructions displayed on that webpage the node server provides everything you need for the back end of the test you are welcome to look at the server code but you should not need to modify anything to make your front end work all of your code should be placed in the public directory in this project the node server will act as the web server for your front end application please insure that your code runs properly with the node server test all the features and make sure that no javascript errors are generated thank you | front_end |
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typescene | p align center a href https typescene dev img width 160 src https typescene dev assets logo detail png a p p align center a href https npmcharts com compare typescene minimal true img src https img shields io npm dm typescene svg a a href https www npmjs com package typescene img src https img shields io npm v typescene svg a a href https www npmjs com package typescene img src https img shields io npm l typescene svg a p h1 align center typescene h1 typescene is a robust front end library written in typescript strongly typed no dependencies no nonsense documentation all documentation for typescene can be found on the website https typescene dev docs https typescene dev docs the online docs include an overview https typescene dev docs introduction as well as other guides and reference documentation https typescene dev docs getting started if you re new to typescene make sure to check out these resources read up on the architecture overview https typescene dev docs introduction follow along with the installation guide https typescene dev docs installation use create typescene webapp https github com typescene create typescene webapp for a complete example check out typescene realworld example app https github com typescene typescene realworld example app todo a few very important tasks are currently still pending tests lots of em the core package now comes with a test harness but only very few tests these need to be added in some kind of structural way perhaps this can be organized using a github project opinions welcome on twitter please more tests for the typescene webapp package these probably need an automated browser based test framework opinions welcome on twitter please more examples for the reference documentation these need to be added to the typescene github io repo on github more sample projects along with articles on how they work before publishing these we re happy to review your own samples please dm on twitter more prebuilt components calendars combo buttons etc see typescene treegrid on github for a full featured example questions and issues have a question about typescene get in touch with us on twitter https twitter com typescene or post a question on stack overflow https stackoverflow com if you think you ve found a bug or other issue please make sure you can reproduce the issue with a minimal example then post your issue here on github https github com typescene typescene issues changes the main typescene repository uses tagged releases https github com typescene typescene releases to communicate changes between versions make sure you stay updated by following typescene https twitter com typescene on twitter faq q why another javascript framework a typescene is different in a number of ways read this article https typescene dev docs introduction goals for a detailed overview of typescene s design goals q should i use typescene for my next project a sure typescene works best for desktop style and or mobile web apps as always read the docs https typescene dev docs first if you need something for your blog or other online content then other frameworks might suit your needs better q how do i get started with typescene a typescene is distributed through npm read through the guides provided on the typescene docs website https typescene dev docs and follow along to create your first project reach out find typescene on twitter https twitter com typescene for the latest news and please consider giving the repository a star on github https github com typescene typescene support your support is greatly appreciated at this stage we mostly need to get the word out if you like typescene tell all your friends and colleagues for contributions in the form of bug fixes and changes feel free to use pull requests https github com typescene typescene pulls or send a dm on twitter to discuss how best to approach your issue license the typescene source code is licensed under the mit license | typescript web webapp ui gui toolkit framework | front_end |
lane_tracker | lane tracker example output images example03 png 1 introduction this repository contains a lane detection and tracking program that uses a traditional i e non machine learning computer vision approach to detect lane lines under the following circumstances 1 can detect curved lane lines with shapes that can be described by a second degree polynomial 2 detects exactly two lane lines which are the left and right lane lines delimiting the lane that a vehicle is currently driving in the lane lines of adjacent lanes cannot be detected detection will not work if the vehicle is not currently inside a lane and oriented along the direction of the lane 3 if one of the two lane lines is successfully detected but the other one is not then the detection will be discarded as invalid the reason for this is that the algorithm can only evaluate the validity of a detection if two lines were detected 4 the purpose of this implementation is to demonstrate the capabilities of this approach not to provide a real time detector which would obviously need to be implemented using parallel processing and in a compiled language demo videos 1 demo 1 highway with dark pavement https www youtube com watch v fjnjztttd9y 2 demo 2 highway with uneven pavement https www youtube com watch v dvioi8wrpp8 3 demo 3 curvy road with varying slope and lighting https www youtube com watch v gdvio6m4xye 2 basic use instructions the main purpose of this repository is to demonstrate the approach explained further below the program uses camera calibration and perspective transform matrices that depend on the resolution of the video image the camera used to record it and its mount position i e the camera s exact perspective on the road it is therefore not straight forward to use on your own videos or images here are some basic use instructions nonetheless clone or fork this repository in order to process a video 1 open the file process video py with an editor 2 set the file path of the video to be processed and the name of the desired output video 3 set the file paths to load camera calibration and warp matrices the camera calibration and warp matrices in this repository are for an input resolution of 1280x720 and a dashboard camera mounted at the top center of the windshield of a sedan these matrices will only serve as very rough approximations for your own camera and setup in order to generate a camera calibration matrix for your camera please consult this opencv tutorial http docs opencv org 2 4 doc tutorials calib3d camera calibration camera calibration html in order to generate warp matrices and meters per pixel conversion parameters please consult this jupyter notebook perspective transformation ipynb 4 set all the options in the lanetracker constructor particularly the input image size and the size of the warped images that will result from the perspective transformation 5 once all these parameters are loaded set execute the file from the console python process video py note the processing of the video frames is performed by the process method of the lanetracker object since this method is passed to the fl image of videofileclip as an argument you need to set all of the desired options for process as default argument values in order to do so open lane tracker py and set the argument values accordingly in order to process a single image 1 instantiate a lanetracker object as shown in process video py 2 call the object s process method 3 dependencies 1 python 3 x 2 numpy 3 opencv 4 moviepy to process video files 5 pickle to load the camera calibration and warp matrices in this repository 4 description of the algorithm all code references in this section refer to the module lane tracker py the process method of the lanetracker class aggregates the individual steps outline below the rough steps of the algorithm are 1 distortion is removed from the input image using a camera matrix and distortion coefficients 2 the input image is warped into the bird s eye view bv perspective 3 various adaptive filtering color thresholding and morphological operations are performed by filter lane points to yield a binary version of the bv image in which filtered pixels are white and all other pixels are black 4 one of two search methods sliding window search or band search is applied to the binary image to separate lane pixels from noise the goal of the search methods is to identify exactly two lane lines 5 second degree polynomials are fit to the detected lane pixels for the left and right lane line fit poly 6 a validity check is performed to evaluate whether the detected lane lines are plausible check validity 4 1 perspective transformation the program transforms input images into the bird s eye view perspective before processing them further there are upsides and downsides to doing this among the downsides is that this transformation is an approximation to the true perpendicular view onto the road from above in that it assumes that 1 the road is a plane and 2 the vehicle s velocity vector is parallel to that plane at all times both of these assumptions are false almost all of the time there are bumps in the road and the slope of the road is almost never constant which leads to the vehicle s velocity vector not being parallel to the surface of the road lying ahead nonetheless the approximation is sufficiently accurate in many situations among the upsides of the bv transformation is that it makes the search methods sliding window search and band search more reliable because valid lane lines will be nearly parallel in the bv perspective information which the search methods can use to search for lane line pixels in a more targeted way it also facilitates the evaluation of whether a pair of detected lane lines is valid you can find details about how the perspective transform is calibrated in this jupyter notebook perspective transformation ipynb 4 2 filtering after the perspective transform a number of adaptive filtering color thresholding and morphological operations are performed by filter lane points to yield a binary version of the bv image in which filtered pixels are white and all other pixels are black let s take a step back though the goal is to identify lane lines so it is crucial to have a clear understanding of some optical properties of lane lines on us roads 1 lane lines have one of two colors white or yellow 2 typically the surface on both sides of a lane line has lower brightness and or saturation and a different hue than the line itself 3 lane lines are not necessarily contiguous so the algorithm needs to be able to identify individual line segments as belonging to the same lane line the former two properties define the filtering process the first step for the design of this process was to analyze individual color channels of various color spaces and combinations of them in the search for those channels that are able to best separate lane line pixels from all other pixels a comparison of all channels of the rgb hls hsv lab and yuv color spaces on a wide variety of test images yielded the following results out of all color channels tested 1 the b channel of the lab color space is the strictly superior representation to identify yellow lane lines it is most resistant against changes in lighting and has the highest signal to noise ratio 2 the r channel of the rgb color space is the overall superior representation to identify white lane lines some other color channels show comparable results on some or all test images but no other color channel or combination works better except on rare exceptions these two color channels are used to filter yellow and white lane lines separately here is an excerpt of the color space comparison colors channels output images color channels10 png next the tophat morphology cv2 morphologyex is applied to both isolated color channels to filter shapes that are brighter than their surroundings in addition to keeping only shapes that are brighter than their surroundings the tophat morphology also keeps only shapes that fit inside the kernel although this is a bit of an imprecise explanation so large bright objects are also filtered out this is good because lane lines are narrow objects next a pixel intensity threshold is applied to both isolated color channels individually since hue saturation and brightness of the same object change under different lighting conditions a global fixed pixel intensity threshold would be useless the same white lane line could have a r channel intensity value of 120 when it s cloudy or of 230 when the sun is shining hence a local and adaptive threshold is needed filter lane points can use one of two local adaptive threshold functions opencv s cv2 adaptivethreshold or my own bilateral adaptive threshold for the lack of a better name which is defined at the top of lane tracker py the program makes use of both thresholding functions see further below why but in the vast majority of all cases it uses the more important bilateral adaptive threshold i wrote this function because a simple adaptive thresholding function like cv2 adaptivethreshold does not fit the needs for this problem with a simple adaptive thresholding function like cv2 adaptivethreshold a given pixel passes the threshold if it has a higher intensity value than the average or some weighted average of its surrounding pixels possibly plus some additive constant while this is useful to filter bright lane lines unfortunately it also keeps the contours of any large bright areas in the image that are surrounded by darker areas and so a lot of noise will pass the threshold this is because it doesn t matter to the threshold whether a pixel is brighter than the surrounding pixels in both half planes around it or whether it is only brighter than the surrounding pixels in one half plane but not in the other remember lane lines have the property that both sides of the line are darker than the line itself so we want to make use of this property in contrast the bilateral adaptive threshold function uses a cross shaped kernel of 1 pixel thickness that for a given pixel computes the average intensities of the left right upper and lower parts of the cross separately in order to pass the threshold a given pixel is required to be brighter than either both the left and right averages or both the top and bottom averages here is an illustration of the implications this has here is an example image and its warped version original image warped image image output images test4 jpg image output images test4 warped png here are two thresholded versions of the warped image above one using the bilateral adaptive threshold function and the other one using opencv s adaptivethreshold both applied to the raw warped color channels with no prior tophat morphology more about that below cv2 adaptivethreshold bilateral adaptive threshold image output images test4 thresh cv2adapt png image output images test4 thresh bilat png the lane lines are clearly visible in both binary images but there is more noise in the left image you can see a strong line left of the left lane line in the left image why is it there in the left image but not in the right one the sharp shadow cast by the guardrail causes the pixels immediately next to it to be brighter than their overall average neighborhood and so they pass the threshold in cv2 adaptivethreshold in bilateral adaptive threshold they don t pass the threshold because these pixels are only brighter than what s on the left of them but not what s on the right of them for the same reason you can see white pixels in the left image at the points where the pavement suddenly turns from dark to light gray and at the points where the black car is surrounded by light pavement most of this noise isn t there in the right image that s not the whole story though if you apply a tophat morphology before the threshold it makes the results with cv2 adaptivethreshold a lot better than without because the tophat morphology already eliminates many bright shapes that are too large or shapes that aren t brighter than two sides of their neighborhood hence it is possible to get similar results with a combination of cv2 adaptivethreshold tophat as with bilateral adaptive threshold but when i compared the results i found that the bilateral threshold still works a little better filter lane points still uses cv2 adaptivethreshold without a prior tophat morphology in some cases though why is that consider a case where the lane lines aren t visible they might be covered by foliage as in the third demo video with the curvy road they might simply not be there or whatever else could happen if however there still happens to be a clear cut intensity contrast between the lane pavement and the side of the road then cv2 adaptivethreshold has a much higher chance of finding such a line than bilateral adaptive threshold because this time we re not looking for a lane line where both sides of the line are darker than the line itself this time it s good enough if one side of the line is brighter than the other e g the foliage is brighter than the road pavement the algorithm therefore can try this as a second attempt a fallback whenever it wasn t able to find lane lines with bilateral adaptive threshold at the first attempt filter lane points also has a mask noise feature this feature uses the lab b channel in an additional stage to filter out background noise in the image it is mostly targeted at greenery this masking stage is activated by default but it is deactivated when cv2 adaptivethreshold is used so that said foliage from the example above doesn t get eliminated from the image in case it is needed the last step of the filtering process is to apply an open morphology to the binary image the open morphology first erodes the white pixel shapes by the kernel size and then dilates what remains left resulting in the elimination of smaller noise parts in the image this improves the robustness of the thresholding quite a bit 4 3 search modes the result of the filtering stage is a binary image in which filtered pixels are white and all other pixels are black next one of two possible search algorithms sliding window search and band search tries to identify lane pixels among the white pixels the combination of these two search methods becomes relevant when multiple consecutive frames are processed sliding window search is the search method used whenever no valid lane line detections are available from recent previous frames it scans the image by iterating two separate small rectangular search windows one for each lane line from the bottom to the top of the image in each successive iteration the two search windows can slide to the left and right within a certain range around the horizontal center of their respective preceding search windows the width and height of the search windows and the search range how far to the left and right of the previous window center to search largely determine the performance of this search method both search windows generally move in the direction where they find the most white pixels if one search window does not find any white pixels in a given iteration which often happens because the white lane lines are dashed it moves in the same direction as the other search window this is one reason why the bv perspective is useful since the lane lines are nearly parallel in this perspective one search window can determine the correct moving direction for both of them band search is the search method used whenever valid lane line detections are available from recent previous frames it searches for lane line pixels in a narrow band of specified width around the previous polynomial graph lane lines move approximately continuously across the frames of a video stream i e if there is a lane line at a certain position in a frame then that lane line must be within a certain narrow neighborhood of this position in the subsequent frame this is why searching for lane line pixels within a narrow band around previous lane line detections works this search method is a lot more robust than the sliding window search here is an example from the third demo video to visualize these two search processes the left image is the first frame of the sequence in this example and so the algorithm must use the sliding window search the right image is the second frame and so it can use the two lane lines found in the first frame to perform a band search around them to find the next lane lines the search area is highlighted in green the detected lane pixels are highlighted in red and blue and graphs of the resulting fitted polynomials are shown in yellow first frame result second frame result image output images search lane result01 png image output images search lane result02 png first frame sliding window search second frame band search image output images search lane vis01 png image output images search lane vis02 png 4 4 curve radius and eccentricity get curve radius computes the curve radius and get eccentricity computes the car s distance from the center of the lane the computation of the eccentricity is straight forward and uses conversion factors from pixels to meters that were calculated based on the warped images the computation of the curve radius happens according to the formula that can be found here http www intmath com applications differentiation 8 radius curvature php once again using the metric conversion factors the y value at which the curve radius is computed is the bottom of the image 5 brief discussion one conclusion from my experiments was that for this particular problem color thresholding worked better than gradient thresholding and the program is more robust without applying any gradient thresholding at all the objects we re trying to detect here lane line segments have a relatively simple structure compared to detecting cars for example and are clearly defined by just color and size we re looking only for shades of white and yellow of a certain minimum thickness that start close to the bottom of the image and by the fact that they are brighter than their surrounding pixels these properties of the objects of interest allow us to get good results using just color thresholding and some morphologies but no gradients in my experiments color thresholding accounted for the vast majority of the exposed lane line pixels while gradient thresholding added little value on top of that but at the same time contributed the majority of the noise in the filtered image using a bilateral adaptive color threshold has a similar effect as using a gradient threshold only better one big problem that becomes apparent in the third demo video is the constantly changing slope of the road as explained above the warp matrix used for the perspective transform is static and assumes that the road ahead is flat from the vehicle s perspective if that assumption does not hold lines that should be parallel in the warped image will no longer be parallel this makes it a lot harder to assess the validity of detected lane lines because even if the lane lines in the warped image are not nearly parallel they might still be valid lane lines one bad way to compensate for this is to relax the criteria that evaluate lane line validity in check validity otherwise the program would reject too many valid lane lines this relaxation of the validity criteria also leads to some detections passing for valid lane lines even though they should have been rejected so this solution is not ideal 6 todo turn all absolute pixel values into relative values in all function parameters | lane-lines lane-tracker lane-lines-detection lane-detection computer-vision | ai |
DatabaseEasyJet | databaseeasyjet reverse engineering easyjets database from the website project report with sql included | server |
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marigold | p align center img width 494 align center alt type logo src https raw githubusercontent com marigold ui marigold 45a5ac5c3ef740a5698958f2a3244743ef9b3338 marigold logo svg p br br ci ci badge ci coverage coverage badge coverage mit license license badge license version version badge package open in visual studio code https shields io badge open 20in 20visual 20studio 20code blue logo visualstudiocode style for the badge https open vscode dev marigold ui marigold marigold react implementation of the marigold design system based on react aria https react spectrum adobe com react aria and tailwind css https tailwindcss com installation sh with pnpm pnpm add marigold components with npm npm install marigold components save with yarn yarn add marigold components for development git clone https github com marigold ui marigold git cd marigold yarn install if you want to use a marigold theme you have to install them seperatly like sh with pnpm pnpm add marigold theme b2b with npm npm install marigold theme b2b save with yarn yarn add marigold theme b2b usage use the styles for your component from a global theme object to provide the theme in context wrap your component into the marigoldprovider basic usage import react from react import marigoldprovider text from marigold components import b2btheme from marigold theme b2b marigoldprovider theme b2btheme text lorem ipsum text marigoldprovider for developers setup usage open a terminal and navigate to a folder of your choice clone the project git clone https github com marigold ui marigold git and navigate to the new folder cd marigold install the packages using pnpm install command in the root and use pnpm dev to start storybook open localhost 1337 http localhost 1337 to see the components in storybook navigate to the documentation cd docs and start the development server pnpm start which opens the documentation site on localhost 3000 http localhost 3000 when working on the components use the following commands 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toshi br sub b toshi b sub a br a href https github com marigold ui marigold commits author toshibot title code a a href https github com marigold ui marigold commits author toshibot title documentation a a href design toshibot title design a td td align center valign top width 20 a href https github com julianthiel img src https avatars1 githubusercontent com u 59880423 v 4 s 120 width 120px alt julian thiel br sub b julian thiel b sub a br a href https github com marigold ui marigold commits author julianthiel title documentation a td td align center valign top width 20 a href https github com johannaracky img src https avatars githubusercontent com u 86712740 v 4 s 120 width 120px alt johannaracky br sub b johannaracky b sub a br a href https github com marigold ui marigold commits author johannaracky title documentation a td td align center valign top width 20 a href https bandism net img src https avatars githubusercontent com u 22633385 v 4 s 120 width 120px alt ikko ashimine br 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lauranawrotzki img src https avatars githubusercontent com u 104084537 v 4 s 120 width 120px alt lauranawrotzki br sub b lauranawrotzki b sub a br a href ideas lauranawrotzki title ideas planning feedback a td td align center valign top width 20 a href https osama abdellatef vercel app img src https avatars githubusercontent com u 62595605 v 4 s 120 width 120px alt osama abdul latif br sub b osama abdul latif b sub a br a href https github com marigold ui marigold commits author osamaabdellateef title code a a href design osamaabdellateef title design a a href maintenance osamaabdellateef title maintenance a a href https github com marigold ui marigold commits author osamaabdellateef title tests a a href https github com marigold ui marigold commits author osamaabdellateef title documentation a td td align center valign top width 20 a href https github com aromko img src https avatars githubusercontent com u 77496890 v 4 s 120 width 120px alt marcel k hler br sub b marcel k hler b sub a br a href https github com marigold ui marigold commits author aromko title documentation a a href https github com marigold ui marigold commits author aromko title code a a href design aromko title design a a href example aromko title examples a a href https github com marigold ui marigold commits author aromko title tests a td tr tbody table markdownlint restore prettier ignore end all contributors list end this project follows the all contributors https github com all contributors all contributors specification contributions of any kind welcome links badges ci https github com marigold ui marigold actions workflows build yml ci badge https github com marigold ui marigold actions workflows build yml badge svg license https github com marigold ui marigold blob master license license badge https img shields io github license marigold ui marigold style flat square package https www npmjs com package marigold components version badge https img shields io npm v marigold components style flat square coverage https codecov io github marigold ui marigold branch main coverage badge https codecov io github marigold ui marigold branch main graph badge svg token yiickx7tzu | design-system component-library ui theme style react aria react-aria a11y design-tokens tailwindcss tailwind | os |
pos-blockchain | proof of stake blockchain this project is a proof of concept of proof of stake algorithm used in ethereum 2 0 accompaining article and tutorial https medium com coinmonks implementing proof of stake e26fa5fb8716 api reference nodes post transact send transactions body json json to address amount number type transaction stake validator fee get transactions returns the content of the transaction pool get public key returns the public key of the node get balance returns the balance of the node ico add prefix ico before nodes apis for ico s apis start system 1 run a few nodes with different http and socket ports 1st node http port 3001 p2p port 5001 npm run dev 2nd node add the 1st node as peer http port 3002 p2p port 5002 peers wc localhost 5001 npm run dev 3rd node add the 1st and 2nd node as peer http port 3003 p2p port 5003 peers wc localhost 5001 wc localhost 5002 npm run dev 2 initial they all have zero balance run an ico and send then some coins you may or may not add all of the nodes but atleast add one peers wc localhost 5001 wc localhost 5002 wc localhost 5003 npm run ico 3 open postman and call localhost 3000 ico transact with the following in the body note intial balance of ico is 1000 you can change that in config js get the address of the all the nodes by calling json to e6ff899aabe0242e0c70441a8b28662d82284b8538d3f9fb6b11b3e9b1cad849 amount 100 type transaction 4 do this 5 times since 5 is set as the threshold for the transaction pool and can be changed in config js it is only when this threshold is hit a block is generated 5 once the block is generated check the balance of those nodes that you have sent coins too they would get some less amount because the transaction fee is set to 1 coin in config js 6 in the next round send the validator fee and then send another trasanction to stake some coins if you want any node to be leader change the transaction type to validator fee to send transaction for validator fee and stake to send trasnaction to stake coins json to 0 amount 30 type validator fee json to 0 amount 30 type stake 7 default validator fee is set to 25 coins sending extra would lose your coins wont be refunded 8 it is important to first send validator fee transaction first and then send stake transaction since stake transaction will only increase your stake amount but to be considered for leader you need to send validator fee sending stake coins before wont lose your coins but won t make you leader 9 by default ico has 0 stake so that the smallest stake can change leadership 10 now send normal transactions to other nodes json to your nodes address amount 10 type transaction 11 send stake transactions again to change the leader 12 stop the ico app when you re done | blockchain |
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venture-stack | venturestack venturestack is an aws cloud development template designed to let teams focus their time on product engineering by taking the hassle and complexity out of aws deployments venturestack is opinionated it prescribes the languages frameworks and development techniques you use its overarching objective is to minimize the breadth of frameworks your team must grasp to successfully deliver a product guides want to get started check out our guides development guide docs develop md scheme migration docs schema md authentication docs auth md logging and instrumentation docs sentry md cron and asynchronous docs cron md components udoit a react demo application with apis cron jobs queues aurora postgres rds schema migration secrets auth0 and sentry io next js a framework for building full stack web applications supports csr ssr apis and static content kysely a type safe sql query builder for typescript no orm magic just sql sst a framework that makes it easy to deploy full stack applications on aws udoit udoit is a task manager starter application the starter application demonstrates client side rendering rest apis and rds aurora postgres data storage kysely schema migrations type generation and queries secrets management with aws parameter store auth0 client authentication sentry io browser and server side instrumentation a cron triggered batch job with sqs event handler synchronises auth0 user metadata to rds udoit screenshot docs udoit screenshot png next js full stack web application framework using react next js supports client side rendering server side rendering static site generation incremental site generation image optimization middleware next apps are deployed on aws as lambda functions and s3 with cloudfront includes an optional warmer function to avoid cold starts open next overview docs open next overview png kysely kysely pronounce key seh lee is a type safe and autocompletion friendly typescript sql query builder key concepts type safe queries kysely lets you write type safe sql queries this eliminates entire classes of errors and lets you sleep peacefully at night no magic just sql kysely is a light abstraction layer over sql this makes it easy to reason about performance and reduces the number of concepts you need to learn to be proficient with the library kysely overview docs kysely overview png sst is an open source framework for deploying full stack web applications to aws key concepts infrastructure made easy with typescript simple constructs and sensible defaults development and testing on the cloud no local environment setup schema migration supports evolving the structure of a database schema over time deploy environments with one command from the cli lambda function debugging with vs code dev console for database and log access expand and add any aws service using cdk sst venture stack docs sst venture stack png | cloud |
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k8s-tutorial-python | although most of the content in this repository is still valid it is now superseded by the bigbitbus kubernetes automation toolkit https github com bigbitbusinc kubernetes automation toolkit we encourage you to visit that github project for the latest code changes and documentation kubernetes 101 for python programmers welcome to the kubernetes 101 for python programmers https 2018 pycon ca talks talk pc 51523 tutorial this repository contains a sample django application django poll project that can be deployed into a kubernetes abbreviated k8s cluster using the included k8s spec files kubecode the application can be launched on your local laptop in development mode using a minikube https kubernetes io docs setup minikube k8s cluster we have also included terraform https www terraform io scripts here aws k8s pgdb with terraform aws kubernetes that can help you spin up a k8s cluster in amazon cloud eks https aws amazon com eks to highlight a few integration points between your k8s resident application and the underlying cloud provider you can download the slides tutorial bigbitbus kubernetes 101 for python programmers feb 2019 pdf accompanying the code your kubernetes learning plan 1 browse through the slides tutorial bigbitbus kubernetes 101 for python programmers feb 2019 pdf in this tutorial to get a condensed start to the concepts around docker and k8s and understanding how a python django application can live inside k8s 2 if you want to try out k8s without installing anything or creating a cloud provider account spend time on the official kubernetes tutorials https kubernetes io docs tutorials these include interactive tools to let you actually play with a k8s cluster without spinning one up on your own machine cloud account create a k8s cluster https kubernetes io docs tutorials kubernetes basics create cluster cluster interactive deploy a nodejs app https kubernetes io docs tutorials kubernetes basics deploy app deploy interactive other basic k8s operations such as scaling rolling versions etc https kubernetes io docs tutorials kubernetes basics 3 if you are ready to take the next step and commit to installing a k8s all in one vm on your pc head over to the minikube installation page https kubernetes io docs tasks tools install minikube to install the single node k8s instance on your pc this is useful for development work you can then try to deploy the django application included django poll project in this tutorial on your local pc k8s cluster 4 the next step is moving toward kubernetes for production we strongly recommend creating a cloud provider managed k8s cluster unless you have a strict requirement for an in house production grade do it yourself k8s cluster https kubernetes io docs setup scratch 5 we have included terraform scripts aws k8s pgdb with terraform aws kubernetes to create a amazon cloud eks https aws amazon com eks cluster you will need aws credentials and a credit card warning running a k8s cluster is not cheap see our slides tutorial bigbitbus kubernetes 101 for python programmers feb 2019 pdf for an example what is where here are links to key resources in this repository slides tutorial kubernetes 101 for python programmers feb 2019 pdf for the tutorial demo video https youtu be lrucfet42pi jenkins kubernetes demo to help you visualize a devops infrastructure as code approach to python development and operations as code using kubernetes minikube installation instructions https kubernetes io docs tasks tools install minikube docker setup and commands django poll project poll app readme md and the dockerfile django poll project dockerfile to create the django application container image from a standard base python image amazon aws eks k8s setup aws k8s pgdb with terraform aws kubernetes aws k8s readme md you will need an aws account credit card required using the k8s cluster kubecode kubectl code readme md this is the meat of the tutorial things to do with the k8s cluster amazon aws rds setup aws k8s pgdb with terraform aws kubernetes aws k8s readme md to create a simple postgres database instance you will need an aws account credit card required we separated the terraform scripts for eks and rds setup because you probably don t want to create and tear down your production database and eks cluster at the same time sample django poll application django poll project the settings py django poll project kube101 kube101 settings py may be of particular interest the readme django poll project poll app readme md describes the docker commands to package the application into a container the jenkins pipeline files jenkins contain shell scripts executed in stages by jenkins this gives you ideas about how to use a ci cd tool with kuberetes workflows see the demo video https youtu be lrucfet42pi to see these jenkins pipelines in action faq 1 why did you choose aws eks and not google or azure as the managed k8s cluster provider and why not flask instead of django k8s evolved from an internal project in google and it is widely believed that the google cloud managed k8s product is the most polished and easy to use as of late 2018 google has also done a phenomenal job of creating documentation for python developers using their k8s product see a python flask app on google cloud k8s https cloud google com python tutorials bookshelf on kubernetes engine we decided to go with aws eks for this tutorial to complement google s k8s flask example https cloud google com python tutorials bookshelf on kubernetes engine in addition there is a large installed base of aws users who may find this tutorial helpful 2 what are the costs associated with running the aws eks cluster you pay for the k8s management plane 0 20 per hour the number of worker nodes ec2 pricing and the load balancer cost if you use other services like rds or s3 those are extra contributions we welcome contributions through pull requests to keep this tutorial accurate and up to date license this repository is licensed under the apache license see the license md license md file for details copyright 2018 bigbitbus inc http bigbitbus com 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 | front_end |
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How-to-learn-Deep-Learning | approach a practical top down approach starting with high level frameworks with a focus on deep learning updated version check out my 60 page guide no ml degree https www emilwallner com p no ml degree on how to land a machine learning job without a degree getting started 2 months there are three main goals to get up to speed with deep learning 1 get familiar to the tools you will be working with e g python the command line and jupyter notebooks 2 get used to the workflow everything from finding the data to deploying a trained model 3 building a deep learning mindset an intuition for how deep learning models behave and how to improve them spend a week on codecademy com and learn the python syntax command line and git if you don t have any previous programming experience it s good to spend a few months learning how to program otherwise it s easy to become overwhelmed spend one to two weeks using pandas https www youtube com watch v yzimircgu5i list pl5 da3qgb5iccsgw1mxlz0hq8ll5u3u9y and scikit learn http scikit learn org stable on kaggle problems https www kaggle com competitions sortby grouped group general page 1 pagesize 20 category gettingstarted using jupyter notebook on colab https colab research google com notebooks welcome ipynb e g titanic https www kaggle com c titanic house prices https www kaggle com c house prices advanced regression techniques and iris https www kaggle com uciml iris this gives you an overview of the machine learning mindset and workflow spend one month implementing models on cloud gpus start with fastai and pytorch http course fast ai the fastai community is the go to place for people wanting to apply deep learning and share the state of the art techniques once you have done this you will know how to add value with ml portfolio 3 12 months think of your portfolio as evidence to a potential employer that you can provide value for them when you are looking for your first job there are four main roles you can apply for 1 machine learning engineering 1 applied machine learning researcher residencies 1 machine learning research scientist and 1 software engineering a lot of the work related to machine learning is pure software engineering roles category 4 e g scaling infrastructure but that s out of scope for this article it s easiest to get a foot in the door if you aim for machine learning engineering roles there are a magnitude more ml engineering roles compared to category 2 3 roles they require little to no theory and they are less competitive most employers prefer scaling and leveraging stable implementations often 1 year old instead of allocating scarce resources to implement sota papers which are often time consuming and seldom work well in practice once you can cover your bills and have a few years of experience you are in a better position to learn theory and advance to category 2 3 roles this is especially true if you are self taught you often have an edge against an average university graduate in general graduates have weak practical skills and strong theory skills context you ll have a mix of 3 10 technical and non technical people looking at your portfolio regardless of their background you want to spark the following reactions the applicant has experience tackling our type of problems the applicant s work is easy to understand and well organized and the work was without a doubt 100 made by the applicant most ml learners end up with the same portfolio as everyone else portfolio items include things as mooc participation dog cat classifiers and implementations on toy datasets such as the titanic and iris datasets they often indicate that you actively avoid real world problem solving and prefer being in your comfort zone by copy pasting from tutorials these portfolio items often signal negative value instead of signaling that you are a high quality candidate a unique portfolio item implies that you have tackled a unique problem without a solution and thus have to engage in the type of problem solving an employee does daily a good starting point is to look for portfolio ideas on active kaggle competitions https www kaggle com competitions and machine learning consulting projects https www upwork com freelance jobs machine learning and demo versions of common production pipelines https github com chiphuyen machine learning systems design blob master build build1 consolidated pdf here s a twitter thread on how to come up with portfolio ideas https twitter com emilwallner status 1184723538810413056 here are rough guidelines to self assess the strength of your portfolio machine learning engineering even though ml engineering roles are the most strategic entry point they are still highly competitive in general there are 50 software engineering roles for every ml role from the self learners i know 2 3 fail to get a foot in the door and end up taking software engineering roles instead you are ready to look for a job when you have two high quality projects that are well documented have unique datasets and are relevant to a specific industry https towardsdatascience com the cold start problem how to build your machine learning portfolio 6718b4ae83e9 say banking or insurance project type base score common project 1 p unique project 10 p multiplier type factor strong documentation 5x 5000 word article 5x kaggle medal 10x employer relevancy 20x hireable 5 250 p competative 15 000 p applied research research assistant residencies for most companies the risk of pursuing cutting edge research is often too high thus only the biggest companies tend to need this skillset there are smaller research organizations that hire for these positions but these positions tend to be poorly advertised and have a bias for people in their existing community many of these roles don t require a ph d which makes them available to most people with a bachelor s or master s degrees or self learners with one year of focussed study given the status scarcity and requirements for these positions they are the most competitive ml positions positions at well known companies tend to get more than a thousand applicants per position daily these roles require that you understand and can implement sota papers thus that s what they will be looking for in your portfolio projects type base score common project 10 p unique project 1 p sota paper implementation 20 p multiplier type factor strong documentation 5x 5000 word article 5x sota performance 5x employer relevancy 20x hireable 52 500 p competitive 150 000 p research scientist research scientist roles require a ph d or equivalent experience while the former category requires the ability to implement sota papers this category requires you to come up with research ideas the mainstream research community measure the quality of research ideas by their impact here is a list of the venues and their impact https scholar google es citations view op top venues hl en vq eng artificialintelligence to have a competitive portfolio you need two published papers in the top venues in an area that s relevant to your potential employer project type base score common project 100 p an unpublished paper 5 p icml iclr neurips publication 500p all other publications 50 p multiplier type factor first author paper 10x employer relevancy 20x hireable 20 000 p competitive roles and elite phd positions 200 000 p examples my first portfolio item after 2 months of learning code https github com emilwallner coloring greyscale images write up https blog floydhub com colorizing b w photos with neural networks my second portfolio item after 4 months of learning code https github com emilwallner screenshot to code write up https blog floydhub com turning design mockups into code with deep learning dylan djian s https github com dylandjian first portfolio item code https github com dylandjian retro contest sonic write up https dylandjian github io world models dylan djian s https github com dylandjian second portfolio item code https github com dylandjian supergo write up https dylandjian github io alphago zero reiichiro nakano s https github com reiinakano first portfolio item code https github com reiinakano arbitrary image stylization tfjs write up https magenta tensorflow org blog 2018 12 20 style transfer js reiichiro nakano s https github com reiinakano second portfolio item write up https reiinakano com 2019 01 27 world painters html most recruiters will spend 10 20 seconds on each of your portfolio items unless they can understand the value in that time frame the value of the project is close to zero thus writing and documentation are key here s another thread on how to write about portfolio items https twitter com emilwallner status 1162289417140264960 the last key point is relevancy it s more fun to make a wide range of projects but if you want to optimize for breaking into the industry you want to do all projects in one niche thus making your skillset super relevant for a specific pool of employers further inspiration fastai student projects https forums fast ai t share you work here highlights 57140 stanford nlp student projects https nlp stanford edu courses cs224n stanford cnn student projects http cs231n stanford edu index html theory 101 4 months learning how to read papers is critical if you want to get into research and a brilliant asset as an ml engineer there are three key areas to feel comfortable reading papers 1 understanding the details of the most frequent algorithms gradient descent linear regression and mlps etc 2 learning how to translate the most frequent math notations into code 3 learn the basics of algebra calculus statistics and machine learning for the first week spend it on 3blue1brown s essence of linear algebra https www youtube com watch v fnk zzamoss list plzhqobowtqdpd3mizzm2xvfitgf8he ab the essence of calculus https www youtube com watch v wuvtyaankzm list plzhqobowtqdmsr9k rj53dwvrmyo3t5yr and statquests the basics of statistics https www youtube com watch v qbigtkblu6g list plblh5jkooluk0fluzwntyyi10uqfuhsy9 and machine learning https www youtube com watch v gv9 4ymhfhi list plblh5jkooluictaglrohqduf 7q2gfujf use a spaced repetition app like anki and memorize all the key concepts use images as much as possible they are easier to memorize spend one month recoding the core concepts https github com eriklindernoren ml from scratch in python numpy including least squares gradient descent linear regression and a vanilla neural network this will help you reduce a lot of cognitive load down the line learning that notations are compact logic and how to translate it into code will make you feel less anxious about the theory i believe the best deep learning theory curriculum is the deep learning book http www deeplearningbook org by ian goodfellow and yoshua bengio and aaron courville i use it as a curriculum and the use online courses and internet resources to learn the details about each concept spend three months on part 1 of the deep learning book use lectures https www youtube com watch v vi7lackouao list plsxu9mhqgs8df5a4pzqgw kfviylc r9b ab channel alenakruchkova and videos to understand the concepts khan academy type exercises to master each concept and anki flashcards to remember them long term key books deep learning book http www deeplearningbook org by ian goodfellow and yoshua bengio and aaron courville deep learning for coders with fastai and pytorch ai applications without a phd https www amazon com gp product 1492045527 ref ppx od dt b asin title s00 ie utf8 psc 1 by jeremy howard and sylvain gugger deep learning with python https www manning com books deep learning with python by fran ois chollet neural networks and deep learning http neuralnetworksanddeeplearning com by michael nielsen grokking deep learning https www manning com books grokking deep learning by andrew w trask forums fastai http forums fast ai keras slack https keras slack autojoin herokuapp com distill slack https join slack com t distillpub shared invite enqtmzg1nzu3mzezmtg3lwjknmq4y2jlnjjkndlhytu2zmqxmgfkm2nimti2ngvjnzjkotdjntfiogzmndbjntezzguwm2u0mzg4ndayn2e pytorch https discuss pytorch org twitter other good learning strategies emil wallner https blog floydhub com emils story as a self taught ai researcher s zayd enam http ai stanford edu zayd why is machine learning hard html catherine olsson https 80000hours org articles ml engineering career transition guide greg brockman v2 https blog gregbrockman com how i became a machine learning practitioner greg brockman v1 https www quora com what are the best ways to pick up deep learning skills as an engineer andrew ng https www youtube com watch v f1ka6a13s9i amid fish http amid fish reproducing deep rl spinning up by openai https spinningup openai com en latest spinningup spinningup html confession as an ai researcher https www reddit com r machinelearning comments 73n9pm d confession as an ai researcher seeking advice yc threads one https news ycombinator com item id 20765553 and two https news ycombinator com item id 18421422 if you have suggestions questions create an issue or ping me on twitter https twitter com emilwallner updated version check out my 60 page guide no ml degree https twitter com emilwallner status 1528961488206979072 on how to land a machine learning job without a degree language versions korean https github com emilwallner how to learn deep learning blob master readme kr md english https github com emilwallner how to learn deep learning blob master readme md | deep-learning machine-learning tflearn cnn rnn | ai |
IoT-Pentesting-Methodology | iot pentesting methodology resources to help get started with iot pentesting mindmap covers step by step methodology to perform iot pentesting 1st draft contributors aditya gupta adi1391 https twitter com adi1391 eben berry of mitre feel free to contribute by opening an issue here or by reaching out through the contact page https www attify store com pages contact us | server |
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Become-A-Full-Stack-Web-Developer | 100 free resources for learning full stack web development hello i created this list and am constantly updating it with new resources information and news if you want to stay updated on the newest trends tutorials and articles in the web development world please subscribe to the once weekly newsletter subscribe to the weekly newsletter here https docs google com forms d e 1faipqlseqyymbcbfjf9mxfmrj7hnwyxvmwycthc5wxvdh5cq vt6fg viewform never more than one email per week easy to unsubscribe your information will never be shared table of contents 1 start here start here 2 how to learn how to learn 3 what is the most useful cs bookmark you have what is the single most useful cs bookmark you have 4 programs classes programs and classes 5 learn html learn html 6 learn css learn css 7 learn javascript learn javascript 8 learn react js learn react js 9 full stack tutorials full stack tutorials 10 learn node js learn node js 11 learn python learn python 12 learn apis learn apis 13 learn databases learn databases 14 learn authentication learn authentication 15 learn git learn git 16 games challenge websites games and challenge websites 17 free programming books free programming books 18 open source contribution opportunities open source contribution opportunities 19 am i ready to be a developer am i ready to be a developer 20 software developer success stories software developer success stories 21 resume s portfolio s linkedin interview prep salary information get the job start here the list below isn t meant to be exclusive it s more so a collection of links that have helped me out along the way and can hopefully help you as you ll see i ve focused on javascript react and node js there is also a wealth of information on interview prep and applying to jobs more free resources can be found on codeburst io https codeburst io take a look at the big picture 2018 web developer roadmap https codeburst io the 2018 web developer roadmap 826b1b806e8d youtube video outlining what to learn similar to above but in video format watch this if you want to become a web developer https www youtube com watch v sbzrwzy7g k learn about the common tools associated with full stack web development what is the a z of web development https www quora com what is the a z of web development and web design my journey to becoming a web developer from scratch without a cs degree and what i learned from it https medium freecodecamp com my journey to becoming a web developer from scratch without a cs degree 2 years later and what i 4a7fd2ff5503 vk5vkb18q medium what happens when you type google into your address bar reddit link https www reddit com r cscareerquestions comments 55ydbs common interview question what happens when you tuts plus the http protocol every web developer must know https code tutsplus com tutorials http the protocol every web developer must know part 1 net 31177 find a local web development related meetup https www meetup com don t worry it s not as scary as you might think and is one of the best ways to start learning how to learn how to learn coursera course not cs specific learning how to learn https www coursera org learn learning how to learn repetition repetition repetition reddit discussion on study techniques https www reddit com r learnprogramming comments 5pyx5t a beginners trick i learned way too late in the what technologies to learn and strategies to learn them how to learn web development https codetheweb blog 2017 10 04 how to learn web development what is the single most useful cs bookmark you have what is the single most useful cs bookmark you have reddit link to discussion https www reddit com r cscareerquestions comments 5bsg82 whats the single most useful csrelated link you learn x in y minutes https learnxinyminutes com what cs majors should know http matt might net articles what cs majors should know google s technical development guide https www google com about careers students guide to technical development html css tricks complete flexbox guide https css tricks com snippets css a guide to flexbox regex cheat sheet http regexr com devdocs http devdocs io awesome list of everything programming https github com sindresorhus awesome how to break into the tech industry a guide to job hunting and tech interviews http haseebq com how to break into tech job hunting and interviews programs and classes bootcamps the web developer bootcamp by colt steele http bit ly 2yeysob i include this paid course because it is worth its weight in gold inexpensive less than 20 and one of the best resources out there if you re going to buy one course to learn webdev buy this one also see the advanced web dev bootcamp http bit ly 2z3tngr thoughts on coding boot camps https www reddit com r learnprogramming comments 5ew1gs thoughts on coding boot camps the complete guide to bootcamps https www reddit com r cscareerquestions comments 506myw the complete guide to bootcamps self 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VCRTSGroup4 | vcrtsgroup4 the project aims to develop a java based software application that can collect user input store it in a database using client server architecture the application s class design and relationships were developed according to functional and non functional requirements using the agile development methodology features user input collection database management client server architecture class design agile development methodology technologies used java mysql client server architecture | agile-development class-design client-server database java | server |
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week3 lab build a radio group component lab writing more tests c6 advanced react week3 lab writing more tests module quiz jsx and testing c6 advanced react week3 module quiz jsx and testing br week 4 c6 advanced react week4 final graded quiz c6 advanced react week4 final graded quiz portfolio project c6 advanced react week4 portfolio certificate of completion https coursera org share 8548f0013bf6b6d3aab1adef5d541e77 br rest courses coming soon | coursera coursera-assignment coursera-specialization css front-end-development javascript meta react reactjs meta-professional-certificate bootstrap git github coding-interviews html5 ui-ux-design version-control | front_end |
Platooning-F1Tenth | f1tenth platooning computer vision reinforcement learning path planning table of contents 1 introduction introduction 2 installation installation 3 runtime verification runtimeverification 4 zero shot policy transfer zeroshot 5 platooning platooning 6 computer vision computervision 7 reinforcement learning reinforcementlearning 8 system identification https github com pmusau17 platooning f1tenth tree master src race sys id 9 docker docker 10 developers developers introduction a name introduction a welcome this repository contains a host of ros http wiki ros org ros introduction packages for the f1tenth autonomous racing competition http f1tenth org in this repository you will find ros packages designed for both simulation and the f1tenth hardware platform the simulation is based on a gazebo based virtual racing environment our goal is to develop the f1tenth platform for ai research to experiment with deep learning computer vision reinforcement learning and path planning for autonomous racing if you have any questions or run into any problems feel free to send me an email mailto patrick musau vanderbilt edu or to post an issue and i ll do my best to get back to you promptly if you think something needs more documentation or explaining please send me an email too i mean it three car sim images three car platoon gif three car simulation installation a name installation a install necessary environments hr installation has been tested on 20 04 lts we highly recommend using the dockerized version of the simulator the instructions can be found at the botton of this file for a native installation see below the computer vision packages contained in this repository assume your system is gpu enabled if your system is gpu enabled you will need to install cuda and cudnn https developer nvidia com cudnn download survey new versions of cuda are released periodically each year thus to keep this repo up to date we refer you to nvidia s installation guide here https developer nvidia com cuda 10 1 download archive update2 target os linux target arch x86 64 target distro ubuntu target version 1604 cudnn requires membership in the nvidia developer program and you can register for this program here https developer nvidia com cudnn download survey for ease of python library installation we highly recommend using anaconda installation of anaconda can be found here https docs anaconda com anaconda install linux ros still uses python 2 https ubuntu com blog psa for ros users some things to know as python 2 approaches eol and if you want to use python 3 we leave that adventure to you once anaconda installed run the following conda create name ros27 python 2 7 conda activate ros27 note if your system is not gpu enabled change the requirements in setup sh setup sh to requirements cpu txt installing pytorch hr to install pytorch kindly visit the following link https pytorch org get started locally installing reinforcement learning libraries hr our reinforcement learning approaches leverage a couple of custom libraries to install them run the following pip install gdown gdown https drive google com uc id 152kl7jzdreydg6qubznl9wtc0i5ivnx6 unzip the rl library zip file cd rl library pip install e install the repo hr if you already have these environments set up then go with this choice we assume that you have ros kinetic and gazebo installed if not please installed these packages using the following link install ros kinetic http wiki ros org kinetic installation ubuntu once you have ros kinetic installed run the following commands bash source opt ros kinetic setup bash git clone https github com pmusau17 platooning f1tenth cd platooning f1tenth after everything has been cloned in to platooning f1tenth run bash chmod u x setup bash setup sh catkin make source devel setup bash troubleshooting hr if you get the error importerror no module named catkin pkg packages and you are using anaconda run the following command conda install c auto catkin pkg runtime verification a name runtimeverification a recent advances in artificial intelligence research have led to the emergence of machine learning models deployed within safety critical systems where their tremendous ability to deal with complex data makes them particularly useful for sensing actuation and control despite the prolific advances enabled by machine learning methods such systems are notoriously difficult to assure the challenge here is that some models such as neural networks are black box in nature making verification and validation difficult and sometimes infeasible in this repo we provide the code used for our evaluation of the use of a real time reachability algorithm in order to reason about the safety of a 1 10 scale open source autonomous vehicle platform known as f1 10 our regime allows us to a provide provable guarantees of safety and b detect potentially unsafe scenarios in the context of autonomous racing there are two options for interacting with our code a native installation or running the code through docker our methods were run on a dell optiplex 7050 running ubuntu 16 04 lts ros kinetic and equipped with a geforce gtx 1080 gpu the real time reachability code is avaliable here https github com pmusau17 rtreach f1tenth and is required in order to run experiments native install hr to use runtime verification code you must first step clone and build this repo as a rospackage the instructions for doing so are provided above once this is done please add the path to the devel setup bash to your bashrc file as shown below change this path to the one on your system source path to platooning f1tenth devel setup bash one this is done either close and reopen your terminal or run the following source bashrc next clone the rtreach f1tenth https github com pmusau17 rtreach f1tenth repository outside of this one and run the following cd rtreach ft1enth build rtreach sh cd rtreach ros source devel setup bash with that you are now ready to run the experiments docker docker installation instructions can be found here dockerinstall running the experiments native installation hr we provide launch files for running the relevant experiments sim for rtreach multi agent launch src race launch sim for rtreach multi agent launch this file launches a simulation with two vehicles the vehicle for which we are reasoning about safety is racecar2 as a demonstration we have implemented a naive simplex architechture simple switch based on safety signal the launch file allows for various parameters to be set such as the number of cars 2 or 3 the velocity used a setpoint for racecar2 the reach time utilized for verification the walltime alloted for reachset computation and the number reachset boxes to displayed in rviz sim for rtreach multi agent batch launch src race launch sim for rtreach multi agent batch launch this file is the launch file used for the empirical evaluation of our regime it is used by the following bash script src race batch scripts run model safety comparison sh the evaluation was done with respect to varying speeds and different types of controllers the options can be found in the aformentioned bash script the results of these experiments were stored in the logs src race logs folder and summarized in the following jupyter notebook src race logs iros experiments ipynb sim for rtreach multi agent multiple controller launch src race launch sim for rtreach multi agent multiple controller launch one of the motivations in utilizing the runtime verification approach is that it abstracts away the need to analyze the underlying controller and instead focuses on the effects of control decisions on the system s future states utilizing our regime one could evaluate the safety of a set of controllers at runtime and select the one with the highest performance that has been determined safe this is what this launch file provides example launch roslaunch race sim for rtreach multi agent launch number of cars 3 zero shot policy transfer a name zeroshot a there are few technologies that hold as much promise in achieving safe accessible and convenient transportation as autonomous vehicles however as recent years have demonstrated safety and reliability remain the most critical challenges especially in complex domains autonomous racing has demonstrated unique benefits in that researchers can conduct research in controlled environments allowing for experimentation with approaches that are too risky to evaluate on public roads in this repo you will find scripts for evaluating two leading methods for training neural network controllers reinforcement learning and imitation learning for the autonomous racing task we compare their viability by analyzing their performance and safety when deployed in novel scenarios outside their training via zero shot policy transfer the controllers were evaluated through a variety of scenarios that test their ability to still perform optimally in scenarios outside of their training these scenarios include changing the constant speed value adding obstacles to the track evaluating on a different track and a real world evaluation on our hardware platform https github com pmusau17 f1tenthhardware we tracked the number of collisions as well as the number of collisions that occured during the experiments docker hr build the docker containers as described here docker dockerinstall run the experiments as follows make sure you ve run xhost docker docker run docker sh or docker run docker cpu sh this should open a terminal within the docker container run the following in the terminal source devel setup bash roslaunch race model comparison launch world number 0 algorithm 0 velocity 1 0 to change the algorithm velocity and track edit the parameters in the above command the mapping is as follows world number 0 track porto world number 1 racecar walker world number 2 track barca world number 3 racear tunnel world number 4 track levine algorithm 0 end to end controller algorithm 1 soft actor critic rl algorithm 2 ddpg rl algorithm 3 end to end controller trained on all tracks algorithm 4 end to end controller trained only on track barca algorithm 5 end to end controller trained on only racecar walker running the experiments native installation hr the experiments without obstacles can be run by the following bash script src race batch scripts run model comparison sh and those with obstacles can be run with this script src race batch scripts run model comparison obstacles sh model comparison launch src race launch model comparison launch this launch file allows for a selection of 5 different race tracks our experiments involved 3 of the 5 6 different controllers our experiments involved 4 of the 6 the speed is also is parametrizeable model comparison obstacles launch src race launch model comparison obstacles launch this file is similar to the above file but also allows for a number of obstacles to be randomly allocated within the environment we set a random seed to make the experiments reproducible example launch roslaunch race model comparison launch world number 0 algorithm 0 velocity 1 0 platooning algorithms a name platooning a disparity extender hr this algorithm was originally proposed by nathan otterness et al in the following blog post https www nathanotterness com 2019 04 the disparity extender algorithm and html the main idea behind this algorithm is to search for the farthest distance that the car can travel in a straight line and go in that direction the algorithm makes use of lidar scans and using the array of distances given by the lidar we must filter the ranges in order to find the set of distances tat are safely reachable by driving in a straight line the basis of this filtering comes from the fact that the lidar thinks of itself as a single point but in reality it s on a car that has some width thus each time we get a reading from the lidar we take the raw array of lidar samples mapping angles to distances and identify disparities between subsequent points in the lidar samples for each pair of points around a disparity we pick the point at the closer distance and calculate the number of lidar samples needed to cover half the width of the car at this distance using this filtered list of distances we find the index of the farthest index compute our steering angle and drive in that direction a more detailed analysis of this algorithm can be found in the above blog post to see a demonstration of this algorithm run the following in two seperate terminals bash roslaunch race multi parametrizeable launch roslaunch race multicar disparity extender launch two car sim images two car sim gif two car simulation following algorithm hr the task of this project was to get a series of 1 10 scales rc cars to platoon in a reliable manner as a proof of concept we made a few assumptions the first assumption is that each car precisely knows its own position and orientaion in the map frame the second assumption is that the the cars could reliably communicate their position and velocity with each other however the platooning would occur on a racetrack thus there would be times that the following cars would not be able to see the lead car thus we needed to design boh a lateral and longitudnal controller that maintained a specified distance between the ego car and the lead car however if any failures occured in the system the cars would need to be able to switch to a fallback controller from the following controller the following controller makes use of the pure pursuit algorithm proposed by r craig coulter in the paper implementation of the pure pursuit path tracking algorithm https www ri cmu edu pub files pub3 coulter r craig 1992 1 coulter r craig 1992 1 pdf to compute the steering angle we use the pure pursuit algorithm to find a goal point in the lead car s position history that consists of the most recent 100 positions these positions are transmitted from the lead car to the ego car via tcp the longitudnal distance is observed using the ego car s lidar and a pd controller is used to control the ego car s velocity the fallback controller is the disparity extender controller one of the benefits of this algorithm is that it can do some obstacle avoidance out of the box making it a reliable fallback controller to see a demonstration of this algorithm run the following in two seperate terminals terminal 1 bash roslaunch race multi parametrizeable launch terminal 2 bash roslaunch race platoon launch changing the number of cars and the track changing the number of cars can be done in the multi parametrizeable launch src f110 fall2018 skeletons simulator f1 10 sim race multi parametrizeable launch at the top of the file you can experiment with two or three car experiments beyond that gazebo operates painfully slow to experiment with two or three cars pass the argument number of cars to the multi paramterizable launch file as shown below roslaunch race multi parametrizable launch number of cars 2 or roslaunch race multi parametrizable launch number of cars 3 at the moment the launch files are designed to use the track porto world file however you can change this in the launch files by editing the world name parameter for single car experiments you can change the track by editing the world name parameter in f1 tenth devel launch src f110 fall2018 skeletons simulator f1 10 sim race f1 tenth devel launch at the top of the file running the experiments native installation if you would like to run the platooning experiments without the launch files you can execute the following commands in seperate terminals terminal 1 roslaunch race multi parametrizable launch number of cars 2 or roslaunch race multi parametrizable launch number of cars 3 terminal 2 the lead car will use whatever driving algorithm you select to navigate the track in this case we utilized the disparity extender rosrun race disparity extender vanderbilt py terminal 3 the syntax for running the following algorithm is the following rosrun race follow lead gen py lead car name ego car name example rosrun race follow lead gen py racecar racecar2 terminal 4 rosrun race follow lead gen py racecar2 racecar3 computer vision a name computervision a right now most things are limited to a single car multi car experiments are a work in progress details can be found in the computer vision src computer vision package error analysis images figure 2 png error analysis reinforcement learning a name reinforcementlearning a these methods are designed for training a vehicle follower which we plan on expanding to platooning however we might expand this to train a single racer in the future details can be found in the reinforcement learning src rl package reset the environment if the car crashes or you want to start the experiment again simply run to restart the experiment running teleoperation nodes to run a node to tele operate the car via the keyboard run the following in a new terminal bash rosrun race keyboard gen py racecar racecar can be replaced with racecar1 racecar2 if there are multiple cars additionally if using the f1 tenth devel launch file simply type the following bash roslaunch race f1 tenth devel launch enable keyboard true docker a name dockerinstall a the first thing you will need to install is nvidia docker https github com nvidia nvidia docker which is used to containerize and run gpu accelerated workloads the computer vision packages in this repository will run faster if you have a gpu visit the above link to do so if you don t have a gpu enabled computer don t fret skip the above step and follow the rest of the instructions additionally we make use of docker compose https docs docker com compose install to define and run the simulation kindly install this as well to build the docker image use the dockerfile located in this repository bash build docker sh in order to enable the use of graphical user interfaces within docker containers such as gazebo and rviz give docker the rights to access the x server with bash xhost local docker this command allows one to connect a container to a host s x server for display but it is not secure it compromises the access control to x server on your host so with a little effort someone could display something on your screen capture user input in addition to making it easier to exploit other vulnerabilities that might exist in x so when you are done run bash xhost local docker to return the access controls that were disabled with the previous command test if the image builds correctly by running bash source docker run docker sh to run the simulation if you have a gpu enabled computer bash source docker run docker sh otherwise run bash source docker run docker cpu sh this will open up a terminal in interactive mode to run any of the experiments source the setup file source install setup bash and run any of the commands found in the instructions for each package reproducing experiments for the following papers 1 on using real time reachability for the safety assurance of machine learning controllers http taylortjohnson com research musau2022icaa pdf musau et al icaa 2022 2 zero shot policy transfer in autonomous racing reinforcement learning vs imitation learning http taylortjohnson com research hamilton2022icaa pdf hamilton et al icaa 2022 3 an empirical analysis of the use of real time reachability for the safety assurance of autonomous vehicles https arxiv org abs 2205 01419 musau et al submitted to artificial intelligence special issue on risk aware autonomous systems theory and practice april 2022 4 integrating online reachability analysis with model predictive control for dynamic obstacle avoidance https pmusau17 github io stankaitis et al submitted to memcode 2022 there are batch scripts that reproduce each table contained in the above papers these scripts are located in two directories here https github com pmusau17 platooning f1tenth tree noetic port src race batch scripts here https github com pmusau17 platooning f1tenth tree noetic port src rtreach batch scripts and here https github com pmusau17 platooning f1tenth tree noetic port src mpc batch scripts each script will produce a series of log files that are stored in the log directory within each package once the experiments have been run they can be summarized using jupyter notebooks one such example is the following notebook https github com pmusau17 platooning f1tenth blob noetic port src mpc logs ifm ipynb for our most recent submission to aij the notebook summarizing the experiments can be found here https github com pmusau17 platooning f1tenth blob noetic port src rtreach logs uncertainty experiments uncertaintyexperiments ipynb feel free to reach out if you have any trouble reproducing the experiments i look forward to any comments and questions developers a name developers a this work was made possible by the following team of graduate and undergraduate students from vanderbilt university working at the institute for software integrated systems https www isis vanderbilt edu patrick musau https www linkedin com in musaup diego manzanas lopez https www linkedin com in diego manzanas 3b4841106 nathaniel nate hamilton https www linkedin com in nathaniel hamilton b01942112 diandry rutayisire https www linkedin com in diandry rutayisire 298a45153 tim darrah https www linkedin com in timothydarrah latif gbadamoshie https www linkedin com in abdul latif gbadamoshie shreyas ramakrishna https www linkedin com in shreyasramakrishna tim krentz https www linkedin com in tim krentz 15042585 | ai |
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EFM2RIOT | efm2riot generator for initializing the efm32 port for use with riot os introduction efm32 is a 32 bit micro controller mcu family manufactured by silicon labs these mcus are very energy efficient riot os http www riot os org is an operating system for internet of things applications it offers a real time kernel peripheral abstraction drivers and network stack this generator prepares the required files for using efm32 with riot os which includes initializing mcu targets initializing board targets usage as an end user when you have cloned this repository a ready to use version is available in the dist folder copy the desired cpu and board folder from cpu and boards to your riot os project as a developer you will need python 3 7 and the 32 bit mcu sdk 5 9 7 0 can be installed via simplicity studio v4 https www silabs com products development tools software simplicity studio the efm2riot configuration py defines all the jobs and boards the efm2riot templates folder contains all the files that need to be pre processed the efm2riot static folder contains files that will be copied to re generate all files run python3 efm2riot py sdk path to sdk platform svds path to svds dist dist to enable some features that are not yet part of riot os pass development you can download svd files from the keil keil com dd2 website categorize them in a folder per family see contributing md contributing md for more information on developing supported families most of the 32 bit families that are supported by emlib are supported by efm2riot efm32 gecko efm32 giant gecko efm32 happy gecko efm32 jade gecko efm32 leopard gecko efm32 pearl gecko efm32 tiny gecko efm32 wonder gecko efm32 zero gecko efr32 blue gecko efr32 flex gecko efr32 mighty gecko efr32 zen gecko ezr32 happy gecko ezr32 leopard gecko ezr32 wonder gecko note not all mcus may be supported or have sufficient resources for use with riot os for instance low memory mcus supported boards currently the following boards development kits are supported slstk3301a dist doc slstk3301a mdash efm32 tiny gecko 11b slstk3400a dist doc slstk3400a mdash efm32 happy gecko slstk3401a dist doc slstk3401a mdash efm32 pearl gecko 1b slstk3402a dist doc slstk3402a mdash efm32 pearl gecko 12b slstk3701a dist doc slstk3701a mdash efm32 giant gecko 11b untested sltb001a dist doc sltb001a mdash efr32 mighty gecko 1p sltb004a dist doc sltb004a mdash efr32 mighty gecko 12p untested sltb009a dist doc sltb009a mdash efr32 giant gecko 12b untested sltb010a dist doc sltb010a mdash efr32 blue gecko 22 slwstk6000b dist doc slwstk6000b mdash efr32 mighty gecko untested slwstk6220a dist doc slwstk6220a mdash ezr32 wonder gecko untested stk3200 dist doc stk3200 mdash efm32 zero gecko stk3600 dist doc stk3600 mdash efm32 leopard gecko stk3700 dist doc stk3700 mdash efm32 giant gecko stk3800 dist doc stk3800 mdash efm32 wonder gecko faq why a generator it started as an experiment on generating code from configuration but turned out to be pretty neat apart from that there are two other reasons this port patches the vendor header files for warning free usage with riot os after each update this process must be repeated this generator automates that process all supported mcus are extracted from the sdk using templates new mcus that are supported by the sdk will also be supported by this port why is this not yet part of riot os as of november 2017 support for the efm32 is included in riot os this generator still exists to initialize new targets albeit it needs to be in sync with riot os what is the benefit of using emlib the use of emlib abstracts away all the chip specific details while providing a clean interface to riot os emlib contains powerful assertions to help development new families are supported as long as the emlib api stays the same chip errata where possible ensures consistency across revisions of the same chip this is applied during chip setup when developing board specific applications chances are you will use emlib for accessing peripherals it reduces the number of errors and cuts the development time the library developers have good understanding of the hardware no need to reinvent the wheel there are doubts on the performance and memory overhead of emlib i have concluded the following based on a benchmarking script included code size does increase when using emlib the overhead is in the range of 5 to 10 kib of flash depending on the target and peripherals used a large part of the overhead is contained within the clock management unit this is an integral part for proper clock setup and low power support it is used by all peripherals and has a constant code size most get set read write enable clear methods are implemented as macros or inline methods they are equally efficient as their direct register alternative some benchmarks are available at http basilfx github io efm2riot as of october 2017 emlib is not included anymore but available as an external https github com basilfx riot gecko sdk package for riot os this reduces the number of source files included license see the license file some of the other port files have been copied from other riot os ports | riot-os efm32 rtos | os |
bbse | blockchain based systems engineering lecture slides burak z https wwwmatthes in tum de pages bjeix3pjs8og burak oez felix hoops https wwwmatthes in tum de pages 1g0a2eiwhl194 felix hoops ulrich gallersd rfer https ulig io research and florian matthes https wwwmatthes in tum de pages 88bkmvw6y7gx prof dr florian matthes br school of computation information and technology br technical university of munich br burak oez felix hoops ulrich gallersdoerfer matthes tum de br https wwwmatthes in tum de https wwwmatthes in tum de for citation please use the provided bib file references bib introduction this github repository contains all contents of the lecture blockchain based systems engineering in2359 held regularly in the summer term at the technical university of munich https www tum de starting in 2018 about 900 students regularly enroll in the course lecture prof dr florian matthes exercises tutorials burak z and felix hoops contact please open an issue pull requests for comments if you are interested in the pptx files drop us an email bbse 2023 the lecture material for bbse 2023 https wwwmatthes in tum de pages enf3vo4lqv74 blockchain based systems engineering will be uploaded in winter 2023 module description 2023 in this lecture we provide an overview of blockchain systems and systems engineering focusing on the technical details and applications of blockchain systems we introduce cryptographic hash functions and present their properties then the data structure and the working principles of the bitcoin blockchain are investigated in detail we analyze permissionless consensus and sybil control mechanisms like proof of work pow of bitcoin and illustrate the mining scheme moreover we inspect the risks challenges and limitations of the technology following this we demonstrate the system architecture of the ethereum blockchain with a focus on the ethereum virtual machine evm and smart contracts subsequently the solidity language is explained in terms of syntax types and design ethereum decentralized applications dapps are illustrated with current standards and frameworks and specifics to dapp developments are introduced the tezos blockchain is introduced with a focus on its advanced infrastructure consensus mechanism on chain governance and use cases alternative approaches to distributed ledger technologies in the enterprise space are also discussed accordingly the hyperledger project and the framework fabric are unfolded further we present an overview of the current state of the blockchain ecosystem with a focus on topics such as self sovereign identity ssi decentralized identity management decentralized finance defi automated market makers amm maximal extractable value mev and an upcoming blockchain algorand contents 2023 lectures and exercises 2023 content of lecture content of exercise 0 1 organization introduction 2 cryptographic basics slides 02 cryptographic basics pdf 2 cryptographic basics exercises ex1 pdf solution exercises ex1 sol pdf 3 bitcoin basics slides 03 bitcoin basics pdf 3 bitcoin basics exercises ex2 pdf solution exercises ex2 sol pdf 4 consensus in bitcoin slides 04 consensus in bitcoin pdf 4 consensus in bitcoin exercises ex3 pdf solution exercises ex3 sol pdf 5 bitcoin evolution and challenges slides 05 bitcoin evolution and challenges pdf 5 bitcoin evolution and challenges exercises ex4 pdf solution exercises ex4 sol pdf 6 ethereum basics slides 06 ethereum basics pdf 6 ethereum basics exercises ex5 pdf solution exercises ex5 sol pdf 7 ethereum smart contracts slides 07 ethereum smart contracts pdf 7 ethereum practical 1 exercises ex6 pdf solution exercises ex6 sol pdf 8 ethereum design patterns slides 08 ethereum design patterns pdf 8 ethereum practical 2 exercises ex7 pdf solution exercises ex7 sol pdf bonus ethereum dapps slides bonus ethereum dapps pdf 9 ethereum practical 3 exercises ex8 pdf solution exercises ex8 sol pdf 9 tezos basics slides 09 tezos basics pdf bonus ethereum practical 4 exercises ex9 pdf solution exercises ex9 sol pdf 10 ssi decentralized idm slides 10 decentralized idm pdf 10 ssi decentralized idm exercises ex10 pdf solution exercises ex10 sol pdf 11 micro lecture defi amms and mev slides 11 defi amms and mev pdf 11 micro lecture exploring algorand slides 11 exploring algorand pdf 12 hyperledger slides 12 hyperledger pdf guest lectures title speaker slides through the ethereum looking glass parithosh jayanthi slides slides guestlecture pdf we thank this year s speaker parithosh jayanthi for his guest lecture and allowing us to share his material within this repository exams 2018 version exams exam18 pdf 4 ects 60 minutes 2019 version exams exam19 pdf 5 ects 90 minutes 2020 version exams exam20 pdf 5 ects 90 minutes 2021 version exams exam21 pdf 5 ects 90 minutes 2022 version exams exam22 pdf 5 ects 90 minutes 2023 version exams exam23 pdf 5 ects 90 minutes license attribution sharealike 4 0 international cc by sa 4 0 https licensebuttons net l by sa 4 0 88x31 png https creativecommons org licenses by sa 4 0 this work is licensed under attribution sharealike 4 0 international cc by sa 4 0 https creativecommons org licenses by sa 4 0 acknowledgements we wish to thank alexander hefele kaan uzdogan christian ziegler and konstantin kuchenmeister for supporting the lecture and all others which provided valuable feedback other information symbols used from fontawesome https fontawesome com | blockchain |
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Reddit-Analytics-Integration-Platform | reddit etl pipeline a data pipeline to extract reddit data from r dataengineering https www reddit com r dataengineering output is a google data studio report providing insight into the data engineering official subreddit motivation project was based on an interest in data engineering and the types of q a found on the official subreddit it also provided a good opportunity to develop skills and experience in a range of tools as such project is more complex than required utilising dbt airflow docker and cloud based storage architecture img src images workflow png width 70 height 70 1 extract data using reddit api https www reddit com dev api 1 load into aws s3 https aws amazon com s3 1 copy into aws redshift https aws amazon com redshift 1 transform using dbt https www getdbt com 1 create powerbi https powerbi microsoft com en gb or google data studio https datastudio google com dashboard 1 orchestrate with airflow https airflow apache org in docker https www docker com 1 create aws resources with terraform https www terraform io output img src images gds dashboard png width 70 height 70 setup follow below steps to setup pipeline i ve tried to explain steps where i can feel free to make improvements changes note this was developed using an m1 macbook pro if you re on windows or linux you may need to amend certain components if issues are encountered as aws offer a free tier this shouldn t cost you anything unless you amend the pipeline to extract large amounts of data or keep infrastructure running for 2 months however please check aws free tier https aws amazon com free all free tier sort by item additionalfields sortrank all free tier sort order asc awsf free 20tier 20types all awsf free 20tier 20categories all limits as this may change first clone the repository into your home directory and follow the steps bash git clone https github com anmol12499 reddit analytics integration platform git getting started to begin using the project follow these steps 1 overview instructions overview md 1 reddit api configuration instructions reddit md 1 aws account instructions aws md 1 infrastructure with terraform instructions setup infrastructure md 1 configuration details instructions config md 1 docker airflow instructions docker airflow md 1 dbt instructions dbt md 1 dashboard instructions visualisation md 1 final notes termination instructions terminate md 1 improvements instructions improvements md more details project structure the project s structure includes directories for infrastructure terraform configuration aws and airflow data extraction python scripts and optional steps like dbt and bi tools integration customization feel free to customize the project by modifying configurations adding new data sources or integrating additional tools as needed | airflow aws-redshift aws-s3 dbt docker etl-pipeline google-studio python terraform | cloud |
nltk_book_exercises | nltk book exercises my solutions to the exercises of the natural language processing with python book these are the solutions i came up with while working through the book there s no guarantee that they are correct or complete the solutions are presented in the form of jupyter notebooks | ai |
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EDF-Scheduling | edf scheduling | rtos edf-scheduling edf scheduling-algorithms | os |
Embedded_Systems_Design | embedded systems design lecturer instructor arkady kluchev associate professor kluchev itmo ru sergei bykovskii associate professor sergei bykovskii itmo ru course description the course is dedicated to understanding the basic principles of embedded systems design in this course students will learn the fundamentals of embedded systems architectural design various models of computation the principles of system software developing and the basics of using real time operating systems these topics will be discussed during eight lectures the course consists the four laboratory works the laboratory works are devoted to practice in software development for stm32 microcontrollers using c programming language the students will study the freertos real time operating system organization and practice in writing programs for it | os |
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Mobile-App-Development | mobile app development this is a learning mobile app development setup in project expo go npx create expo app awesomeproject cd awesomeproject npx expo start run project npm run android create new branch and push it git checkout b your new branch git add git commit m message git push set upstream origin your new branch move to branches git add git stash git checkout your new branch clone this repository md https github com nimeshpiyumantha mobile app development git connect with me if you have any bugs or issues if you want to explain my code please contact me on div align center br b mail me b nbsp a href mailto nimeshpiyumantha11 gmail com img width 20px src https github com nimeshpiyumantha red alpha blob main gmail svg a p div p align center a href https twitter com npiyumantha60 img align center src https raw githubusercontent com rahuldkjain github profile readme generator master src images icons social twitter svg alt nimeshpiyumantha height 30 width 40 a a href https www linkedin com in nimesh piyumantha 33736a222 target blank img align center src https raw githubusercontent com rahuldkjain github profile readme generator master src images icons social linked in alt svg alt https www linkedin com public profile settings trk d flagship3 profile self view public profile height 30 width 40 a a href https www facebook com profile php id 100025931563090 target blank img align center src https raw githubusercontent com rahuldkjain github profile readme generator master src images icons social facebook svg alt nimesh piyumantha height 30 width 40 a a href https www instagram com nimmaa target blank img align center src https raw githubusercontent com rahuldkjain github profile readme generator master src images icons social instagram svg alt nimmaa height 30 width 40 a p div align center repo license https img shields io github license nimeshpiyumantha mobile app development labelcolor black color 3867d6 style for the badge repo size https img shields io github repo size nimeshpiyumantha mobile app development label repo 20size style for the badge labelcolor black color 20bf6b github forks https img shields io github forks nimeshpiyumantha mobile app development labelcolor black color 0fb9b1 style for the badge github stars https img shields io github stars nimeshpiyumantha mobile app development labelcolor black color f7b731 style for the badge github lastcommit https img shields io github last commit nimeshpiyumantha mobile app development logo github labelcolor black color d1d8e0 style for the badge div div align center 2023 nimesh piyumantha https github com nimeshpiyumantha inc all rights reserved div | javascript react-native | front_end |
Pi-Jukebox | pi jukebox a mpd front end for playing your raspberry pi with touchscreen documentation of code and raspberry pi setup http mark me github io pi jukebox licence pi jukebox is free software you can redistribute it and or modify it under the terms of the gnu affero general public license as published by the free software foundation either version 3 of the license or at your option any later version pi jukebox is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu affero general public license for more details you should have received a copy of the gnu affero general public license along with pi jukebox if not see http www gnu org licenses c 2015 by mark zwart mark zwart pobox com | front_end |
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cloudserver | zenko cloudserver zenko cloudserver logo res scality cloudserver logo png docker pulls badgedocker https hub docker com r zenko cloudserver docker pulls badgetwitter https twitter com zenko overview cloudserver formerly s3 server is an open source amazon s3 compatible object storage server that is part of zenko https www zenko io scality s open source multi cloud data controller cloudserver provides a single aws s3 api interface to access multiple backend data storage both on premise or public in the cloud cloudserver is useful for developers either to run as part of a continous integration test environment to emulate the aws s3 service locally or as an abstraction layer to develop object storage enabled application on the go learn more at www zenko io cloudserver https www zenko io cloudserver may i offer you some lovely documentation http s3 server readthedocs io en latest docker run your zenko cloudserver with docker https hub docker com r zenko cloudserver contributing in order to contribute please follow the contributing guidelines https github com scality guidelines blob master contributing md installation dependencies building and running the zenko cloudserver requires node js 10 x and yarn v1 17 x up to date versions can be found at nodesource https github com nodesource distributions clone source code shell git clone https github com scality s3 git install js dependencies go to the s3 folder shell yarn install frozen lockfile if you get an error regarding installation of the diskusage module please install g if you get an error regarding level down bindings try clearing your yarn cache shell yarn cache clean run it with a file backend shell yarn start this starts a zenko cloudserver on port 8000 two additional ports 9990 and 9991 are also open locally for internal transfer of metadata and data respectively the default access key is accesskey1 with a secret key of verysecretkey1 by default the metadata files will be saved in the localmetadata directory and the data files will be saved in the localdata directory within the s3 directory on your machine these directories have been pre created within the repository if you would like to save the data or metadata in different locations of your choice you must specify them with absolute paths so when starting the server shell mkdir m 700 pwd myfavoritedatapath mkdir m 700 pwd myfavoritemetadatapath export s3datapath pwd myfavoritedatapath export s3metadatapath pwd myfavoritemetadatapath yarn start run it with multiple data backends shell export s3data multiple yarn start this starts a zenko cloudserver on port 8000 the default access key is accesskey1 with a secret key of verysecretkey1 with multiple backends you have the ability to choose where each object will be saved by setting the following header with a locationconstraint on a put request shell x amz meta scal location constraint mylocationconstraint if no header is sent with a put object request the location constraint of the bucket will determine where the data is saved if the bucket has no location constraint the endpoint of the put request will be used to determine location see the configuration section in our documentation here http s3 server readthedocs io en latest getting started configuration to learn how to set location constraints run it with an in memory backend shell yarn run mem backend this starts a zenko cloudserver on port 8000 the default access key is accesskey1 with a secret key of verysecretkey1 run it with vault user management note vault is proprietary and must be accessed separately shell export s3vault vault yarn start this starts a zenko cloudserver using vault for user management badgetwitter https img shields io twitter follow zenko svg style social label follow badgedocker https img shields io docker pulls scality s3server svg badgepub https circleci com gh scality s3 svg style svg badgepriv http ci ironmann io gh scality s3 svg style svg circle token 1f105b7518b53853b5b7cf72302a3f75d8c598ae | aws-s3 docker multiple-backends storage cloud s3-storage cloud-storage cloud-native object-storage nodejs javascript | front_end |
kolibri-design-system | netlify status https api netlify com api v1 badges 9ae9ac56 7240 4480 b5a8 01645cb903ca deploy status https app netlify com sites kolibri design system deploys lint https github com learningequality kolibri design system workflows lint badge svg branch v0 2 x kolibri design system note that the kolibri design system is not intended to be used as a dependency outside of kolibri products if you are building a kolibri plugin you should not add the design system as a dependency because it will be made available as a dependency of kolibri itself what is kolibri design system design system learningequality org https design system learningequality org is a resource for designers and developers who are building kolibri products it includes the design system patterns and the library of ui components how can i use it documentation refer to design system learningequality org https design system learningequality org to learn about the design system patterns and components as well as the detailed technical documentation and guidance for the library components that are available for usage in kolibri products for the latest not yet released version visit the design system website built from the develop branch at develop kolibri design system netlify app https develop kolibri design system netlify app component library the component library is a node package hosted on github it contains front end components utilities and style definitions supporting the kolibri design system and used in kolibri products to add a particular pinned version to a project using yarn for example v1 0 1 run bash yarn add https github com learningequality kolibri design system v1 0 1 components and utilities will be accessible under the lib path for example javascript import kbutton from kolibri design system lib kbutton the library is provided as source code rather than a pre built distribution a project using it needs to supply both build and runtime dependencies how do i get help or give feedback contact us at the kolibri design system github discussions https github com learningequality kolibri design system discussions if you have found a bug you can create a github issue https github com learningequality kolibri design system issues following the instructions in the issue template also update readme md duplicate how can i contribute 1 follow the getting started dev docs 01 getting started md to set up your development server at the bottom of that page you will also find links that will take you to guidelines for the most common tasks 2 search for issues tagged as help wanted https github com learningequality kolibri design system issues q is 3aissue is 3aopen label 3a 22help wanted 22 no 3aassignee or good first issue https github com learningequality kolibri design system issues q is 3aissue is 3aopen label 3a 22good first issue 22 no 3aassignee 3 ask us for an assignment in the comments of an issue you ve chosen note that in times of increased contributions activity it may take us a few days to reply if you don t hear from us within a week please reach out via kolibri design system github discussions https github com learningequality kolibri design system discussions where to ask questions for anything development related refer to the developer documentation dev docs at first some answers may already be there for questions related to a specific issue or assignment requests use the corresponding issue s comments section visit kolibri design system github discussions https github com learningequality kolibri design system discussions to ask about anything related to contributing to kolibri design system troubleshoot development server issues or connect with other contributors thank you for your interest in contributing to kolibri design system the kolibri project was founded by volunteers dedicated to helping make educational materials more accessible to those in need and every contribution makes a difference | os |
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awesome-papers | awesome papers a list of papers on graphics computer vision and hci contents graphics graphics computer vision vision hci hci subtopics inpainting subtopics inpainting object selection subtopics object selection internal learning subtopics internal learning neural rendering subtopics neural rendering performance capture subtopics performance capture | siggraph cvpr iccv eccv chi uist pg eg | ai |
ax-samples | english readme en md ax samples license https img shields io badge license bsd 3 clause blue svg https raw githubusercontent com axera tech ax samples main license github workflow status https img shields io github actions workflow status axera tech ax samples build yml branch main https github com axera tech ax samples actions ax samples https www axera tech com ai soc ax650n docs ax650n md ax630a docs ax630a md ax620a docs ax620a md ax620u docs ax620u md axera pi https wiki sipeed com m3axpi ax620a axera pi pro https wiki sipeed com m4ndock ax650n docs compile md cmake examples examples cv issue modelzoo https pan baidu com s 1ccu okw8jueg2s3pehta4g pwd xq9f google drive google drive https drive google com drive folders 1jy59vofs2qxi8tkviz0phfxhmfkpw5ps usp sharing benchmark benchmark axera pi ax pipeline https github com axera tech ax pipeline axera pi isp npu ax models https github com axera tech ax models examples npu npu https pulsar docs readthedocs io zh cn latest github issues qq 139953715 | arm artificial-intelligence neural-network npu onnx yolov5 yolox yolov7 | ai |
Boston_House_Price_Prediction | boston house price prediction done eda and feature engineering on boston house dataset sklearn database and build a machine learning model to predict house price on the basis of certain feature how to access full code in your local 1 first fork this repositry by clicking on fork option in your github account 2 go to code copy the github repo url 3 wherever you want to clone this repo directory in local travel to that location in command prompt 4 then in cmd type this command git clone github repo url after this whole repo will get clone will get copied to your local 5 after this write this code in cmd to open the code in vs code code you can also open this to your any favourite ide from ide itself by open file folder option in file menu and select this folder boston house price prediction environment setup for code to run in local conda create p venv python 3 7 y conda activate venv pip install requirements txt note if some import gives error then do pip install individually and again re open ide after closing it steps to run the code on heroku server 1 go to heroku website create account https www heroku com home 2 create a new app and give any desired name 3 select the app in personal go to deploy under deployment method select 2nd method github connenct to github under manual deploy choose deploy branch 4 finally click to see build log in more option at top right if everything goes right you will get unique project url that can be accessed by anyone and on any platform | server |
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ESP8266_IOT_PLATFORM | esp8266 iot platform esp8266 sdk provides users with a simple fast and efficient development platform for internet of things products the esp8266 iot platform is based on the freertos esp8266 sdk https github com espressif esp iot rtos sdk and adds on to it some commonly used functionalities in an example application of a smart plug this application uses the esp touch protocol to realise smart configuration of the device the communication protocols used are json and http rest an android mobile apk https github com espressifapp iot espressif android is also included as a basic template for the users code structure usr directory user main c the entry point for the main program user webserver c creates the tcp webserver using json packets and rest architecture user devicefind c creates a udp service which recieves special finder message on port 1025 and allows the user to discover devices on the network user esp platform c provides the espressif smart configuration api esp touch example communicates with the espressif cloud servers customize this to connect to your own servers maintains the network status and data transmission to server user plug c implements the functionality of a smart plug in this example user esp platform timer c implements the timer functionalities user light c could be used to output pwm signals that can be used for smart lighting user cgi c implents an adapter between the http webserver and the sdk upgrade directory upgrade c firmware upgrade example upgrade lib c operations on flash devices pertaining the upgrade of firmware include directory the include directory includes the relevant headers needed for the project of interest is user config h which can be used to configure or select the examples by setting the macros we can enable the relevant functionality e g plug device and light device please note that you have to adjust these parameters based on your flash map for more details please refer to 2a esp8266 iot sdk user manuel user esp platform h define esp param start sec 0x7d user light h define priv param start sec 0x7c user plug h define priv param start sec 0x7c driver directory this contains the gpio interface libesphttpd directory this directory implements a small http server it is compatible with most web browsers core contains the parser implementing the http protocol and a simple file system espfs is a file system with simple compression capabilites built in util contains the interface with wifi and dns related codes html light and html plug directories these directories contain the javascript and html pages and user interface resources usage configuration target device can be configured through defining user config h macro this application default configuration is a smart power plug or smart power socket define plug device 1 and supports the http server function define httpd server 1 compiling the code run the compilation script gen misc sh you will prompted for some configuration parameters user the firmware download tool to flash the device with the bins generated for my version of freertos esp8266 sdk 1 2 0 3 i have used the following parameters in the upload boot v1 4 b1 bin downloads to flash 0x00000 user1 2048 new 3 bin downloads to flash 0x10000 esp init data default bin downloads to 0x1fc000 blank bin downloads to flash 0x1fe000 | os |
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cardregistration-js-kit | warning deprecated warning this sdk is deprecated as of july 21 2023 and will no longer be maintained or supported you should upgrade to the newest version of our javascript vault sdk https mangopay com docs sdk vault web the new sdk provides the same functionality of card registration flow https mangopay com docs endpoints direct card payins card registration object with typescript language support better performance and continued support if you have any other questions around this deprecation please reach out to our team via the hub https hub mangopay com mangopay card registration javascript kit build status https travis ci org mangopay cardregistration js kit svg branch master https travis ci org mangopay cardregistration js kit mangopay cardregistration javascript kit is a client library designed to take care of the card registration process refer to mangopay rest api documentation http docs mangopay com api references and mangopay card registration process http docs mangopay com api references card registration for more information browser compatibility the library makes cross origin ajax calls cors http en wikipedia org wiki cross origin resource sharing to get the card token and handle card registration with mangopay api therefore it requires a browser that supports cors the kit does not support internet explorer 6 7 and opera mini the payment page that collects card details should use https the kit will not work in internet explorer 8 and 9 if this page is not encrypted with ssl installation the kit has been written in pure javascript and has no external dependencies it is enough to include the kit file on your card payment page script type text javascript src mangopay kit js script typically you would also have mangopay sdk up and running to work with the mangopay api java https github com mangopay mangopay2 java sdk php https github com mangopay mangopay2 php sdk net https github com mangopay mangopay2 net sdk python https github com mangopay mangopay2 python sdk ruby https github com mangopay mangopay2 ruby sdk and node https github com mangopay mangopay2 nodejs sdk distributions are available the kit is also available for installation from npm https www npmjs com package mangopay cardregistration js kit if you prefer configuration set mangopay cardregistration clientid to your mangopay client id mangopay cardregistration baseurl is set to sandbox environment by default to enable production environment set it to https api mangopay com usage make sure you have the right configuration in place set mangopay api base url and client id mangopay cardregistration baseurl https api sandbox mangopay com mangopay cardregistration clientid your client id create a new cardregistration object on the server and pass it over to the javascript kit initialize with card register data prepared on the server mangopay cardregistration init cardregistrationurl cardregistrationurl property preregistrationdata preregistrationdata property accesskey accesskey property id id property collect card details from the user on a payment form card data collected from the user var carddata cardnumber card number val cardexpirationdate card exp date val cardcvx card cvx val cardtype card type val then call mangopay cardregistration registercard register card mangopay cardregistration registercard carddata function res success you can use res cardid now that points to registered card function res handle error see res resultcode and res resultmessage running the demo there is a simple php demo code included that shows how to use the kit the demo page requires mangopay sdk class to do the server side work make sure that mangopay php sdk https github com mangopay mangopay2 php sdk is accessible to the demo page unit tests jasmine unit tests are placed under tests dir and can be launched in a browser using tests index html unit test covers only validation classes and requests with invalid data license mangopay javascript kit is distributed under mit license contacts report bugs or suggest features using issue tracker at github | front_end |
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Awesome-Text2SQL | awesome text2sql github repo stars https img shields io github stars eosphoros ai awesome text2sql style social https github com eosphoros ai awesome text2sql github repo forks https img shields io github forks eosphoros ai awesome text2sql style social https github com eosphoros ai awesome text2sql awesome https camo githubusercontent com 64f8905651212a80869afbecbf0a9c52a5d1e70beab750dea40a994fa9a9f3c6 68747470733a2f2f617765736f6d652e72652f62616467652e737667 https github com eosphoros ai awesome text2sql license mit https img shields io badge license mit green svg https opensource org licenses mit last commit https img shields io github last commit eosphoros ai awesome text2sql color green english readme zh md curated tutorials and resources for large language models text2sql and more how to contribute we warmly welcome contributions from everyone whether you ve found a typo a bug have a suggestion or want to share a resource related to llm text2sql for detailed guidelines on how to contribute please see our contributing md contributing md file leaderboard wikisql https github com salesforce wikisql leaderboard spider https yale lily github io spider br exact match em spider https yale lily github io spider br exact execution ex bird https bird bench github io br valid efficiency score ves bird https bird bench github io br execution accuracy ex 1 93 0 br 2021 05 sead execution guided decoding https arxiv org pdf 2105 07911 pdf 74 0 br 2022 09 graphix 3b picard https arxiv org pdf 2301 07507 pdf 86 6 br 2023 08 dail sql gpt 4 self consistency https arxiv org pdf 2308 15363 pdf 64 22 br 2023 10 sft codes 15b 60 37 br 2023 10 sft codes 15b 2 92 7 br 2021 03 sdsql execution guided decoding https arxiv org pdf 2103 04399 pdf 73 9 br 2022 09 catsql grappa 86 2 br 2023 08 dail sql gpt 4 https arxiv org pdf 2308 15363 pdf 63 62 br 2023 10 sft codes 7b 59 25 br 2023 10 sft codes 7b 3 92 5 br 2020 11 ie sql execution guided decoding https aclanthology org 2020 emnlp main 563 pdf 73 1 br 2022 09 ship picard https arxiv org pdf 2212 08785 pdf 85 3 br 2023 04 din sql gpt 4 https arxiv org pdf 2304 11015 pdf 60 77 br 2023 07 gpt 4 55 90 br 2023 08 din sql gpt 4 https arxiv org pdf 2304 11015 pdf 4 92 2 br 2020 03 hydranet execution guided decoding https arxiv org pdf 2008 04759 pdf 72 9 br 2022 05 g r lgesql electra https aclanthology org 2023 findings acl 23 pdf 83 9 br 2023 07 hindsight chain of thought with gpt 4 59 44 br 2023 08 din sql gpt 4 https arxiv org pdf 2304 11015 pdf 54 89 br 2023 07 gpt 4 5 91 9 br 2020 12 bridge execution guided decoding https arxiv org pdf 2012 12627 pdf 72 4 br 2022 08 resdsql t5 1 1 lm100k xl 82 3 br 2023 06 c3 chatgpt zero shot https arxiv org pdf 2307 07306 pdf 56 99 br 2023 10 sft codes 15b 52 15 br 2023 10 sft codes 15b 6 91 8 br 2019 08 x sql execution guided decoding https arxiv org pdf 1908 08113 pdf 72 4 br 2022 05 t5 sr 80 8 br 2023 07 hindsight chain of thought with gpt 4 and instructions 56 56 br 2023 03 chatgpt cot https arxiv org pdf 2305 03111 pdf 50 25 br 2023 10 sft codes 7b 7 91 4 br 2021 03 sdsql https arxiv org pdf 2103 04399 pdf 72 2 br 2022 12 n best list rerankers picard https arxiv org pdf 2210 10668 pdf 79 9 br 2023 02 resdsql 3b natsq https arxiv org pdf 2302 05965 pdf 54 84 br 2023 10 sft codes 7b 49 02 br 2023 07 claude 2 8 91 1 br 2020 12 bridge https arxiv org pdf 2012 12627 pdf 72 1 br 2021 09 s sql electra https arxiv org pdf 2203 06958 pdf 78 5 br 2022 11 sead pql 51 40 br 2023 03 chatgpt 40 08 br 2023 03 chatgpt cot https arxiv org pdf 2305 03111 pdf 9 91 0 br 2021 04 text2sqlgen eg https www semanticscholar org reader b877233410484b2ff2add278105c53b6633d9d20 72 0 br 2023 02 resdsql 3b natsql https arxiv org pdf 2302 05965 pdf 78 2 br 2023 04 din sql codex https arxiv org pdf 2304 11015 pdf 49 69 br 2023 03 chatgpt cot https arxiv org pdf 2305 03111 pdf 39 30 br 2023 03 chatgpt 10 90 5 br 2020 11 seqgensql eg https arxiv org pdf 2011 03836 pdf 72 0 br 2021 06 lgesql electra https arxiv org pdf 2106 01093 pdf 78 0 br 2023 08 t5 3b natsql token preprocessing https arxiv org pdf 2305 17378 pdf 41 60 br 2023 02 codex 36 47 br 2023 02 codex contents awesome text2sql awesome text2sql how to contribute how to contribute contents contents introduction introduction survey survey classic model classic model base model base model fine tuning fine tuning dataset dataset evaluation index evaluation index practice project practice project friendship links friendship links introduction text to sql or text2sql as the name implies is to convert text into sql a more academic definition is to convert natural language problems in the database field into structured query languages that can be executed in relational databases therefore text to sql can also be abbreviated as nl2sql input natural language questions such as query the relevant information of the table t user and the results are sorted in descending order by id and only the first 10 data are kept output sql such as select from t user order by id desc limit 10 survey 2023 international conference on very large data bases vldb ccf a a survey on deep learning approaches for text to sql paper https link springer com article 10 1007 s00778 022 00776 8 2022 ieee transactions on knowledge and data engineering tkde ccf a a survey on text to sql parsing concepts methods and future directions paper https arxiv org pdf 2208 13629 pdf 2022 international conference on computational linguistics coloing ccf b recent advances in text to sql a survey of what we have and what we expect paper https arxiv org pdf 2208 10099v1 pdf 2022 arxiv deep learning driven natural languages text to sql query conversion a survey paper https arxiv org pdf 2208 04415 pdf classic model 2023 arxiv none text to sql empowered by large language models a benchmark evaluation paper https arxiv org pdf 2308 15363v2 pdf code https github com beachwang dail sql https img shields io badge spider green https yale lily github io spider https img shields io badge spider realistic yellow https aclanthology org 2021 naacl main 105 pdf 2023 aaai 2023 ccf a resdsql decoupling schema linking and skeleton parsing for text to sql paper https arxiv org pdf 2302 05965 pdf code https github com ruckbreasoning resdsql https img shields io badge spider green https yale lily github io spider https img shields io badge spider realistic yellow https aclanthology org 2021 naacl main 105 pdf https img shields io badge spider dk blue https arxiv org pdf 2109 05157 pdf https img shields io badge spider syn red https arxiv org pdf 2106 01065 pdf 2023 arxiv none can llm already serve as a database interface a big bench for large scale database grounded text to sqls paper https arxiv org pdf 2305 03111 pdf code https github com alibabaresearch damo convai tree main bird https img shields io badge bird green https bird bench github io 2023 arxiv none din sql decomposed in context learning of text to sql with self correction paper https arxiv org pdf 2304 11015v2 pdf code https github com mohammadrezapourreza few shot nl2sql with prompting https img shields io badge spider green https yale lily github io spider https img shields io badge wikisql yellow https github com salesforce wikisql blob master readme md 2023 arxiv none a comprehensive evaluation of chatgpt s zero shot text to sql capability paper https arxiv org pdf 2303 13547v1 pdf code https github com thu bpm chatgpt sql https img shields io badge spider green https yale lily github io spider https img shields io badge spider realistic yellow https aclanthology org 2021 naacl main 105 pdf https img shields io badge spider dk blue https arxiv org pdf 2109 05157 pdf https img shields io badge spider syn red https arxiv org pdf 2106 01065 pdf https img shields io badge spider cg organge https arxiv org pdf 2205 02054 pdf https img shields io badge adveta purple https aclanthology org 2022 acl long 142 pdf https img shields io badge cspider cyan https taolusi github io cspider explorer https img shields io badge dusql lightgreen https aclanthology org 2020 emnlp main 562 pdf https img shields io badge sparc pink https arxiv org pdf 1906 02285 pdf https img shields io badge cosql gray https arxiv org pdf 1909 05378 pdf 2023 iclr ccf a binding language models in symbolic languages paper https arxiv org pdf 2210 02875v2 pdf code https github com hkunlp binder https img shields io badge wikitq green https arxiv org pdf 1508 00305 pdf https img shields io badge tabfact yellow https arxiv org pdf 1909 02164 pdf https img shields io badge souall blue https arxiv org pdf 2010 11246 pdf 2023 sigmod ccf a few shot text to sql translation using structure and content prompt learning paper http iir ruc edu cn fanj papers sigmod2023 scprompt pdf code https github com ruc datalab sc prompt https img shields io badge spider green https yale lily github io spider https img shields io badge cosql yellow https arxiv org pdf 1909 05378 pdf https img shields io badge genquery blue https citeseerx ist psu edu document repid rep1 type pdf doi 1c9df99cce1903d34c53025e86e72331bbfbe08f 2023 icassp ccf b t5 sr a unified seq to seq decoding strategy for semantic parsing paper https arxiv org pdf 2306 08368v1 pdf https img shields io badge spider green https yale lily github io spider https img shields io badge spider syn yellow https arxiv org pdf 2106 01065 pdf 2022 acl ccf a s sup 2 sup sql injecting syntax to question schema interaction graph encoder for text to sql parsers paper https aclanthology org 2022 findings acl 99 pdf https img shields io badge spider green https yale lily github io spider https img shields io badge spider syn yellow https arxiv org pdf 2106 01065 pdf 2022 naacl ccf b sead end to end text to sql generation with schema aware denoising paper https arxiv org pdf 2105 07911v2 pdf https img shields io badge wikisql green https github com salesforce wikisql blob master readme md 2022 emnlp ccf b star sql guided pre training for context dependent text to sql parsing paper https arxiv org pdf 2210 11888v2 pdf code https github com alibabaresearch damo convai 2022 emnlp ccf b rasat integrating relational structures into pretrained seq2seq model for text to sql paper https arxiv org pdf 2205 06983v2 pdf code https github com lumia group rasat 2022 emnlp ccf b cqr sql conversational question reformulation enhanced context dependent text to sql parsers paper https arxiv org pdf 2205 07686 pdf 2022 acl ccf a hie sql history information enhanced network for context dependent text to sql semantic parsing paper https arxiv org pdf 2203 07376v2 pdf 2022 arxiv none importance of synthesizing high quality data for text to sql parsing paper https arxiv org pdf 2212 08785v1 pdf 2021 acl ccf a decoupled dialogue modeling and semantic parsing for multi turn text to sql paper https arxiv org pdf 2106 02282v2 pdf 2021 arxiv none pay more attention to history a context modelling strategy for conversational text to sql paper https arxiv org pdf 2112 08735v2 pdf code https github com juruomp rat sql tc 2021 iclr ccf a score pre training for context representation in conversational semantic parsing paper https openreview net pdf id oyzxhri2rie 2021 dasfaa ccf b an interactive nl2sql approach with reuse strategy paper https link springer com chapter 10 1007 978 3 030 73197 7 19 2021 naacl ccf b structure grounded pretraining for text to sql paper https arxiv org pdf 2010 12773v3 pdf 2021 emnlp ccf b picard parsing incrementally for constrained auto regressive decoding from language models paper https arxiv org pdf 2109 05093v1 pdf code https github com servicenow picard 2021 iclr ccf a grappa grammar augmented pre training for table semantic parsing paper https arxiv org pdf 2009 13845v2 pdf code https github com taoyds grappa 2021 acl ccf a lgesql line graph enhanced text to sql model with mixed local and non local relations paper https arxiv org pdf 2106 01093 pdf code https github com rhythmcao text2sql lgesql 2020 emnlp ccf b bridging textual and tabular data for cross domain text to sql semantic parsing paper https arxiv org pdf 2012 12627v2 pdf code https github com salesforce tabularsemanticparsing 2020 acl ccf a tabert pretraining for joint understanding of textual and tabular data paper https arxiv org pdf 2005 08314v1 pdf code https github com facebookresearch tabert 2020 acl ccf a rat sql relation aware schema encoding and linking for text to sql parsers paper https arxiv org pdf 1911 04942v5 pdf code https github com microsoft rat sql 2020 emnlp ccf b mention extraction and linking for sql query generation paper https arxiv org pdf 2012 10074v1 pdf 2020 emnlp ccf b igsql database schema interaction graph based neural model for context dependent text to sql generation paper https arxiv org pdf 2011 05744v1 pdf code https github com headacheboy igsql 2020 arxiv none hybrid ranking network for text to sql paper https arxiv org pdf 2008 04759 pdf code https github com lyuqin hydranet wikisql 2019 arxiv none x sql reinforce schema representation with context paper https arxiv org pdf 1908 08113 pdf 2019 emnlp ccf b global reasoning over database structures for text to sql parsing paper https arxiv org pdf 1908 11214v1 pdf code https github com benbogin spider schema gnn global 2019 emnlp ccf b editing based sql query generation for cross domain context dependent questions paper https arxiv org pdf 1909 00786v2 pdf code https github com ryanzhumich editsql 2019 acl ccf a representing schema structure with graph neural networks for text to sql parsing paper https arxiv org pdf 1905 06241v2 pdf code https github com benbogin spider schema gnn 2019 acl ccf a towards complex text to sql in cross domain database with intermediate representation paper https arxiv org pdf 1905 08205v2 pdf code https github com microsoft irnet 2018 emnlp ccf b syntaxsqlnet syntax tree networks for complex and cross domaintext to sql task paper https arxiv org pdf 1810 05237v2 pdf code https github com taoyds syntaxsql 2018 naacl ccf b typesql knowledge based type aware neural text to sql generation paper https arxiv org pdf 1804 09769 pdf code https github com taoyds typesql 2017 arxiv none sqlnet generating structured queries from natural language without reinforcement learning paper https arxiv org pdf 1711 04436 pdf code https github com xiaojunxu sqlnet base model llama paper https arxiv org pdf 2302 13971 pdf code https github com facebookresearch llama model https huggingface co meta llama 2023 02 meta ai proposes the open source llm llama which has four scales 7b 13b 33b and 65b chatglm paper https arxiv org pdf 2103 10360 pdf code https github com thudm chatglm 6b blob main readme md model https huggingface co thudm chatglm 6b 2023 03 tsinghua university proposes the open bilingual language model chatglm based on general language model https github com thudm glm framework with the specification of 7b alpaca paper https crfm stanford edu 2023 03 13 alpaca html code https github com tatsu lab stanford alpaca model https huggingface co tatsu lab alpaca 7b wdiff tree main 2023 03 stanford university proposes alpaca an open source llm fine tuned based on the llama 7b model there are 1 specification of 7b and the training is simpler and cheaper vicuna paper https lmsys org blog 2023 03 30 vicuna code https github com lm sys fastchat model https huggingface co lmsys 2023 03 uc berkeley university cmu and stanford university propose vicuna an open souce llm based on the llama model has two specifications 7b and 13b wizardlm paper https arxiv org pdf 2304 12244 pdf code https github com nlpxucan wizardlm model https huggingface co wizardlm 2023 04 peking university and microsoft propose wizardlm a llm of evolutionary instructions with three specifications of 7b 13b and 30b 2023 06 they propose wizardmath a llm in the field of mathematics 2023 08 they propose wizardcoder a llm in the field of code falcon paper https arxiv org pdf 2306 01116 pdf code https huggingface co tiiuae falcon 180b model https huggingface co tiiuae 2023 06 united arab emirates proposes falcon an open source llm trained solely on refinedweb datasets with four parameter specifications of 1b 7b 40b and 180b it is worth noting that the performance on model 40b exceeds that of 65b llama chatglm2 paper https arxiv org pdf 2210 02414 pdf code https github com thudm chatglm2 6b blob main readme en md model https huggingface co thudm chatglm2 6b 2023 06 tsinghua university proposes the second generation version of chatglm with the specification of 7b which has stronger performance longer context more efficient inference and more open license baichuan 7b code https github com baichuan inc baichuan 7b model https huggingface co baichuan inc baichuan 7b 2023 06 baichuan intelligent technology proposes the baichuan 7b an open source large scale pre trained language model based on transformer architecture which contains 7 billion parameters and trained on approximately 1 2 trillion tokens it supports both chinese and english languages with a context window length of 4096 baichuan 13b code https github com baichuan inc baichuan 13b model https huggingface co baichuan inc baichuan 13b base 2023 07 baichuan intelligent technology proposes the baichuan 13b an open source commercially available large scale language model following baichuan 7b which has two versions pre training baichuan 13b base and alignment baichuan 13b chat internlm paper https github com internlm internlm techreport blob main internlm pdf code https github com internlm internlm model https huggingface co internlm 2023 07 shanghai ai laboratory and sensetime propose the internlm which has open sourced a 7b and 20b parameter base models and chat models tailored for practical scenarios and the training system llama 2 paper https ai meta com research publications llama 2 open foundation and fine tuned chat models code https github com facebookresearch llama model https huggingface co meta llama 2023 07 meta ai proposes the second generation llama series open source llm llama 2 compared with llama 1 the training data is 40 more and the context length is doubled the model has four specifications 7b 13b 34b and 70b but 34b is not open source code llama paper https arxiv org pdf 2308 12950 pdf code https github com facebookresearch codellama model https huggingface co codellama 2023 08 meta ai proposes code llama based on llama 2 code llama reaches state of the art performance among open models on several code benchmarks there are foundation models code llama python specializations code llama python and instruction following models with 7b 13b and 34b parameters each qwen paper https qianwen res oss cn beijing aliyuncs com qwen technical report pdf code https github com qwenlm qwen model https huggingface co qwen 2023 08 alibaba cloud proposes the 7b parameter version of the large language model series qwen 7b abbr tongyi qianwen is pretrained on a large volume of data including web texts books codes etc which has open sourced two models with qwen 7b and qwen 7b chat 2023 09 alibaba cloud updated the qwen 7b and qwen 7b chat and open sourced qwen 14b and qwen 14b chat baichuan 2 code https github com baichuan inc baichuan2 model https huggingface co baichuan inc 2023 09 baichuan intelligent technology proposes the new generation of open source large language models baichuan 2 trained on a high quality corpus with 2 6 trillion tokens which has base and chat versions for 7b and 13b and a 4bits quantized version for the chat model phi 1 5 paper https arxiv org pdf 2309 05463 pdf model https huggingface co microsoft phi 1 5 2023 09 microsoft research proposes the open source language model phi 1 5 a transformer with 1 3 billion parameters which was trained using the same data sources as phi 1 https huggingface co microsoft phi 1 augmented with a new data source that consists of various nlp synthetic texts when assessed against benchmarks testing common sense language understanding and logical reasoning phi 1 5 demonstrates a nearly state of the art performance among models with less than 10 billion parameters fine tuning p tuning paper https arxiv org pdf 2103 10385 pdf code https github com thudm p tuning 2021 03 tsinghua university and others propose p tuning a fine tuning method for llm which uses trainable continuous prompt word embeddings to reduce the cost of fine tuning lora paper https arxiv org pdf 2106 09685 pdf code https github com microsoft lora 2021 06 microsoft proposes the low rank adaptation method for fine tuning llm by freezing the pre training weights p tuning v2 paper https arxiv org pdf 2110 07602 pdf code https github com thudm p tuning v2 2021 10 tsinghua university proposes p tuning v2 an improved version of p tuning with better performance rlhf paper https huggingface co blog rlhf code https github com huggingface blog blob main zh rlhf md 2022 12 openai uses the rlhf reinforcement learning from human feedback method to train chatgpt and uses human feedback signals to directly optimize the language model with excellent performance rrhf paper https arxiv org pdf 2304 05302 pdf code https github com ganjinzero rrhf 2023 04 alibaba proposes a novel learning paradigm called rrhf rank responses to align language models with human feedback without tears which can be tuned as easily as fine tuning and achieve a similar performance as ppo in hh dataset qlora paper https arxiv org pdf 2305 14314 pdf code https github com artidoro qlora 2023 05 washington university proposes the qlora method based on the frozen 4bit quantization model combined with lora method training which further reduces the cost of fine tuning rltf paper https arxiv org pdf 2307 04349 pdf code https github com zyq scut rltf 2023 07 tencent proposes rltf reinforcement learning from unit test feedback a novel online rl framework with unit test feedback of multi granularity for refining code llms rrtf paper https arxiv org pdf 2307 14936v1 pdf 2023 07 huawei proposes rrtf rank responses to align test teacher feedback compared with rlhf rrhf can efficiently align the output probabilities of a language model with human preferences with only 1 2 models required during the tuning period and it is simpler than ppo in terms of implementation hyperparameter tuning and training rlaif paper https arxiv org pdf 2309 00267 pdf 2023 09 google proposes rlaif rl from ai feedback a technique where preferences are labeled by an off the shelf llm in lieu of humans they find that the rlhf and rlaif methods achieve the similar results on the task of summarization dataset wikisql paper https arxiv org pdf 1709 00103 pdf code https github com salesforce wikisql dataset https github com salesforce wikisql 2017 09 salesforce proposes a large text to sql dataset wikisql the data comes from wikipedia which belongs to a single domain contains 80 654 natural language questions and 77 840 sql statements the form of sql statements is relatively simple and does not include sorting grouping and subqueries and other complex operations spider paper https arxiv org pdf 1809 08887 pdf code https github com taoyds spider dataset https yale lily github io spider 2018 09 yale university proposes the text to sql dataset spider with multiple databases multiple tables and single round query it is also recognized as the most difficult large scale cross domain evaluation list in the industry it contains 10 181 natural language questions and 5 693 sql statements involving more than 200 databases in 138 different fields the difficulty level is divided into easy medium difficult and extremely difficult sparc paper https arxiv org pdf 1906 02285 pdf code https github com taoyds sparc dataset https drive google com uc export download id 1uu7nmhtr1tdqw1t7baum7opu4lelvkfg 2019 06 yale university proposes a large dataset sparc for complex cross domain and context dependent multi turn semantic parsing and text to sql task which consists of 4 298 coherent question sequences 12k unique individual questions annotated with sql queries annotated by 14 yale students obtained from user interactions with 200 complex databases over 138 domains cspider paper https arxiv org pdf 1909 13293 pdf code https github com taolusi chisp dataset https drive google com drive folders 1txcuq1ydpubdddhf3mkht 8zixluqula usp sharing 2019 09 westlake university propposes a large chinese dataset cspider for complex and cross domain semantic parsing and text to sql task translated from spider by 2 nlp researchers and 1 computer science student which consists of 10 181 questions and 5 693 unique complex sql queries on 200 databases with multiple tables covering 138 different domains cosql paper https arxiv org pdf 1909 05378 pdf code https yale lily github io cosql dataset https yale lily github io cosql 2019 09 yale university and salesforce research propose a cross domain database cosql which consists of 30k turns plus 10k annotated sql queries obtained from a wizard of oz woz collection of 3k dialogues querying 200 complex dbs spanning 138 domains tableqa paper https arxiv org pdf 2006 06434 pdf dataset https www luge ai luge datadetail id 12 2020 06 zhuiyi technology proposes a large scale cross domain natural language to sql dataset tableqa in chinese language consisting 64 891 questions and 20 311 unique sql queries on over 6 000 tables dusql paper https aclanthology org 2020 emnlp main 562 pdf dataset https www luge ai luge datadetail id 13 2020 11 baidu proposes a larges scale and pragmatic chinese dataset dusql for the cross domain text tosql task containing 200 databases 813 tables and 23 797 question sql pairs chase paper https aclanthology org 2021 acl long 180 pdf code https github com xjtu intsoft chase dataset https github com xjtu intsoft chase tree page data 2021 08 xi an jiaotong university and microsoft propose the first cross domain multi round text to sql chinese dataset which contains a list of 5459 multi round questions and 17940 query sql binary groups bird sql paper https arxiv org pdf 2305 03111 pdf code https github com alibabaresearch damo convai tree main bird dataset https bird bench github io 2023 05 the university of hong kong and alibaba propose a large scale cross domain dataset bird which contains over 12 751 unique question sql pairs 95 big databases with a total size of 33 4 gb it also covers more than 37 professional domains such as blockchain hockey healthcare and education etc evaluation index execution accuracy ex paper https arxiv org pdf 2208 13629 pdf definition calculate the proportion of the correct number of sql execution results in the data set and the result may be overestimated exact match em paper https arxiv org pdf 2208 13629 pdf definition calculate the matching degree between the sql generated by the model and the marked sql and the result may be underestimated practice project db gpt hub https github com eosphoros ai db gpt hub github repo stars https img shields io github stars eosphoros ai db gpt hub style social https github com eosphoros ai db gpt hub stargazers last commit https img shields io github last commit eosphoros ai db gpt hub color green the eosphoros organization proposes an open source project focusing on text to sql fine tuning based on llm including large scale model download dataset preprocessing fine tuning technologies such as lora and qlora model prediction model evaluation and other steps sqlcoder https github com defog ai sqlcoder github repo stars https img shields io github stars defog ai sqlcoder style social https github com defog ai sqlcoder stargazers last commit https img shields io github last commit defog ai sqlcoder color green the defog organization proposes an advanced text to sql llm which has outstanding performance and is better than gpt3 5 wizardcoder and starcoder etc second only to gpt4 modal finetune sql https github com run llama modal finetune sql github repo stars https img shields io github stars run llama modal finetune sql style social https github com run llama modal finetune sql stargazers last commit https img shields io github last commit run llama modal finetune sql color green this project is based on the llama 2 7b model for text to sql fine tuning which includes a complete training fine tuning and evaluation process llama efficient tuning https github com hiyouga llama efficient tuning github repo stars https img shields io github stars hiyouga llama efficient tuning style social https github com hiyouga llama efficient tuning stargazers last commit https img shields io github last commit hiyouga llama efficient tuning color green easy to use llm fine tuning framework llama 2 bloom falcon baichuan qwen chat friendship links eosphoros https github com eosphoros ai github repo stars https img shields io github stars eosphoros ai style social https github com eosphoros ai last commit https img shields io github last commit eosphoros ai db gpt color green they are a team of technology enthusiasts from internet companies and nlp graduate students who are passionate about open source projects their focus is on developing solutions that protect the privacy and security of databases and large language models their aim is to ensure that the abilities of these models remain absolutely private secure and under control awesome aigc tutorials https github com luban agi awesome aigc tutorials github repo stars https img shields io github stars luban agi awesome aigc tutorials style social https github com luban agi awesome aigc tutorials stargazers last commit https img shields io github last commit luban agi awesome aigc tutorials color green awesome aigc tutorials houses a curated collection of tutorials and resources spanning across large language models ai painting and related fields discover in depth insights and knowledge catered for both beginners and advanced ai enthusiasts star history chart https api star history com svg repos eosphoros ai awesome text2sql type date https star history com eosphoros ai awesome text2sql | ai awesome datset deep-learning finetuning llm nlp survey text2sql nl-to-sql nl2sql text-to-sql | ai |
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