├── CODE_OF_CONDUCT.md ├── SUPPORT.md ├── LICENSE ├── SECURITY.md ├── .gitignore └── README.md /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Microsoft Open Source Code of Conduct 2 | 3 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 4 | 5 | Resources: 6 | 7 | - [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/) 8 | - [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) 9 | - Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns 10 | -------------------------------------------------------------------------------- /SUPPORT.md: -------------------------------------------------------------------------------- 1 | # Support 2 | 3 | ## How to file issues and get help 4 | 5 | This project uses GitHub Issues to track bugs and feature requests. Please search the existing 6 | issues before filing new issues to avoid duplicates. For new issues, file your bug or 7 | feature request as a new Issue. 8 | 9 | For help and questions about using this project, please create issues. We will check reguarly. 10 | 11 | ## Microsoft Support Policy 12 | 13 | Support for this project is limited to the resources listed above. 14 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) Microsoft Corporation. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE 22 | -------------------------------------------------------------------------------- /SECURITY.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ## Security 4 | 5 | Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). 6 | 7 | If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10)), please report it to us as described below. 8 | 9 | ## Reporting Security Issues 10 | 11 | **Please do not report security vulnerabilities through public GitHub issues.** 12 | 13 | Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report). 14 | 15 | If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/en-us/msrc/pgp-key-msrc). 16 | 17 | You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). 18 | 19 | Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: 20 | 21 | * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.) 22 | * Full paths of source file(s) related to the manifestation of the issue 23 | * The location of the affected source code (tag/branch/commit or direct URL) 24 | * Any special configuration required to reproduce the issue 25 | * Step-by-step instructions to reproduce the issue 26 | * Proof-of-concept or exploit code (if possible) 27 | * Impact of the issue, including how an attacker might exploit the issue 28 | 29 | This information will help us triage your report more quickly. 30 | 31 | If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://microsoft.com/msrc/bounty) page for more details about our active programs. 32 | 33 | ## Preferred Languages 34 | 35 | We prefer all communications to be in English. 36 | 37 | ## Policy 38 | 39 | Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd). 40 | 41 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | ## Ignore Visual Studio temporary files, build results, and 2 | ## files generated by popular Visual Studio add-ons. 3 | ## 4 | ## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore 5 | 6 | # User-specific files 7 | *.rsuser 8 | *.suo 9 | *.user 10 | *.userosscache 11 | *.sln.docstates 12 | 13 | # User-specific files (MonoDevelop/Xamarin Studio) 14 | *.userprefs 15 | 16 | # Mono auto generated files 17 | mono_crash.* 18 | 19 | # Build results 20 | [Dd]ebug/ 21 | [Dd]ebugPublic/ 22 | [Rr]elease/ 23 | [Rr]eleases/ 24 | x64/ 25 | x86/ 26 | [Aa][Rr][Mm]/ 27 | [Aa][Rr][Mm]64/ 28 | bld/ 29 | [Bb]in/ 30 | [Oo]bj/ 31 | [Ll]og/ 32 | [Ll]ogs/ 33 | 34 | # Visual Studio 2015/2017 cache/options directory 35 | .vs/ 36 | # Uncomment if you have tasks that create the project's static files in wwwroot 37 | #wwwroot/ 38 | 39 | # Visual Studio 2017 auto generated files 40 | Generated\ Files/ 41 | 42 | # MSTest test Results 43 | [Tt]est[Rr]esult*/ 44 | [Bb]uild[Ll]og.* 45 | 46 | # NUnit 47 | *.VisualState.xml 48 | TestResult.xml 49 | nunit-*.xml 50 | 51 | # Build Results of an ATL Project 52 | [Dd]ebugPS/ 53 | [Rr]eleasePS/ 54 | dlldata.c 55 | 56 | # Benchmark Results 57 | BenchmarkDotNet.Artifacts/ 58 | 59 | # .NET Core 60 | project.lock.json 61 | project.fragment.lock.json 62 | artifacts/ 63 | 64 | # StyleCop 65 | StyleCopReport.xml 66 | 67 | # Files built by Visual Studio 68 | *_i.c 69 | *_p.c 70 | *_h.h 71 | *.ilk 72 | *.meta 73 | *.obj 74 | *.iobj 75 | *.pch 76 | *.pdb 77 | *.ipdb 78 | *.pgc 79 | *.pgd 80 | *.rsp 81 | *.sbr 82 | *.tlb 83 | *.tli 84 | *.tlh 85 | *.tmp 86 | *.tmp_proj 87 | *_wpftmp.csproj 88 | *.log 89 | *.vspscc 90 | *.vssscc 91 | .builds 92 | *.pidb 93 | *.svclog 94 | *.scc 95 | 96 | # Chutzpah Test files 97 | _Chutzpah* 98 | 99 | # Visual C++ cache files 100 | ipch/ 101 | *.aps 102 | *.ncb 103 | *.opendb 104 | *.opensdf 105 | *.sdf 106 | *.cachefile 107 | *.VC.db 108 | *.VC.VC.opendb 109 | 110 | # Visual Studio profiler 111 | *.psess 112 | *.vsp 113 | *.vspx 114 | *.sap 115 | 116 | # Visual Studio Trace Files 117 | *.e2e 118 | 119 | # TFS 2012 Local Workspace 120 | $tf/ 121 | 122 | # Guidance Automation Toolkit 123 | *.gpState 124 | 125 | # ReSharper is a .NET coding add-in 126 | _ReSharper*/ 127 | *.[Rr]e[Ss]harper 128 | *.DotSettings.user 129 | 130 | # TeamCity is a build add-in 131 | _TeamCity* 132 | 133 | # DotCover is a Code Coverage Tool 134 | *.dotCover 135 | 136 | # AxoCover is a Code Coverage Tool 137 | .axoCover/* 138 | !.axoCover/settings.json 139 | 140 | # Visual Studio code coverage results 141 | *.coverage 142 | *.coveragexml 143 | 144 | # NCrunch 145 | _NCrunch_* 146 | .*crunch*.local.xml 147 | nCrunchTemp_* 148 | 149 | # MightyMoose 150 | *.mm.* 151 | AutoTest.Net/ 152 | 153 | # Web workbench (sass) 154 | .sass-cache/ 155 | 156 | # Installshield output folder 157 | [Ee]xpress/ 158 | 159 | # DocProject is a documentation generator add-in 160 | DocProject/buildhelp/ 161 | DocProject/Help/*.HxT 162 | DocProject/Help/*.HxC 163 | DocProject/Help/*.hhc 164 | DocProject/Help/*.hhk 165 | DocProject/Help/*.hhp 166 | DocProject/Help/Html2 167 | DocProject/Help/html 168 | 169 | # Click-Once directory 170 | publish/ 171 | 172 | # Publish Web Output 173 | *.[Pp]ublish.xml 174 | *.azurePubxml 175 | # Note: Comment the next line if you want to checkin your web deploy settings, 176 | # but database connection strings (with potential passwords) will be unencrypted 177 | *.pubxml 178 | *.publishproj 179 | 180 | # Microsoft Azure Web App publish settings. Comment the next line if you want to 181 | # checkin your Azure Web App publish settings, but sensitive information contained 182 | # in these scripts will be unencrypted 183 | PublishScripts/ 184 | 185 | # NuGet Packages 186 | *.nupkg 187 | # NuGet Symbol Packages 188 | *.snupkg 189 | # The packages folder can be ignored because of Package Restore 190 | **/[Pp]ackages/* 191 | # except build/, which is used as an MSBuild target. 192 | !**/[Pp]ackages/build/ 193 | # Uncomment if necessary however generally it will be regenerated when needed 194 | #!**/[Pp]ackages/repositories.config 195 | # NuGet v3's project.json files produces more ignorable files 196 | *.nuget.props 197 | *.nuget.targets 198 | 199 | # Microsoft Azure Build Output 200 | csx/ 201 | *.build.csdef 202 | 203 | # Microsoft Azure Emulator 204 | ecf/ 205 | rcf/ 206 | 207 | # Windows Store app package directories and files 208 | AppPackages/ 209 | BundleArtifacts/ 210 | Package.StoreAssociation.xml 211 | _pkginfo.txt 212 | *.appx 213 | *.appxbundle 214 | *.appxupload 215 | 216 | # Visual Studio cache files 217 | # files ending in .cache can be ignored 218 | *.[Cc]ache 219 | # but keep track of directories ending in .cache 220 | !?*.[Cc]ache/ 221 | 222 | # Others 223 | ClientBin/ 224 | ~$* 225 | *~ 226 | *.dbmdl 227 | *.dbproj.schemaview 228 | *.jfm 229 | *.pfx 230 | *.publishsettings 231 | orleans.codegen.cs 232 | 233 | # Including strong name files can present a security risk 234 | # (https://github.com/github/gitignore/pull/2483#issue-259490424) 235 | #*.snk 236 | 237 | # Since there are multiple workflows, uncomment next line to ignore bower_components 238 | # (https://github.com/github/gitignore/pull/1529#issuecomment-104372622) 239 | #bower_components/ 240 | 241 | # RIA/Silverlight projects 242 | Generated_Code/ 243 | 244 | # Backup & report files from converting an old project file 245 | # to a newer Visual Studio version. Backup files are not needed, 246 | # because we have git ;-) 247 | _UpgradeReport_Files/ 248 | Backup*/ 249 | UpgradeLog*.XML 250 | UpgradeLog*.htm 251 | ServiceFabricBackup/ 252 | *.rptproj.bak 253 | 254 | # SQL Server files 255 | *.mdf 256 | *.ldf 257 | *.ndf 258 | 259 | # Business Intelligence projects 260 | *.rdl.data 261 | *.bim.layout 262 | *.bim_*.settings 263 | *.rptproj.rsuser 264 | *- [Bb]ackup.rdl 265 | *- [Bb]ackup ([0-9]).rdl 266 | *- [Bb]ackup ([0-9][0-9]).rdl 267 | 268 | # Microsoft Fakes 269 | FakesAssemblies/ 270 | 271 | # GhostDoc plugin setting file 272 | *.GhostDoc.xml 273 | 274 | # Node.js Tools for Visual Studio 275 | .ntvs_analysis.dat 276 | node_modules/ 277 | 278 | # Visual Studio 6 build log 279 | *.plg 280 | 281 | # Visual Studio 6 workspace options file 282 | *.opt 283 | 284 | # Visual Studio 6 auto-generated workspace file (contains which files were open etc.) 285 | *.vbw 286 | 287 | # Visual Studio LightSwitch build output 288 | **/*.HTMLClient/GeneratedArtifacts 289 | **/*.DesktopClient/GeneratedArtifacts 290 | **/*.DesktopClient/ModelManifest.xml 291 | **/*.Server/GeneratedArtifacts 292 | **/*.Server/ModelManifest.xml 293 | _Pvt_Extensions 294 | 295 | # Paket dependency manager 296 | .paket/paket.exe 297 | paket-files/ 298 | 299 | # FAKE - F# Make 300 | .fake/ 301 | 302 | # CodeRush personal settings 303 | .cr/personal 304 | 305 | # Python Tools for Visual Studio (PTVS) 306 | __pycache__/ 307 | *.pyc 308 | 309 | # Cake - Uncomment if you are using it 310 | # tools/** 311 | # !tools/packages.config 312 | 313 | # Tabs Studio 314 | *.tss 315 | 316 | # Telerik's JustMock configuration file 317 | *.jmconfig 318 | 319 | # BizTalk build output 320 | *.btp.cs 321 | *.btm.cs 322 | *.odx.cs 323 | *.xsd.cs 324 | 325 | # OpenCover UI analysis results 326 | OpenCover/ 327 | 328 | # Azure Stream Analytics local run output 329 | ASALocalRun/ 330 | 331 | # MSBuild Binary and Structured Log 332 | *.binlog 333 | 334 | # NVidia Nsight GPU debugger configuration file 335 | *.nvuser 336 | 337 | # MFractors (Xamarin productivity tool) working folder 338 | .mfractor/ 339 | 340 | # Local History for Visual Studio 341 | .localhistory/ 342 | 343 | # BeatPulse healthcheck temp database 344 | healthchecksdb 345 | 346 | # Backup folder for Package Reference Convert tool in Visual Studio 2017 347 | MigrationBackup/ 348 | 349 | # Ionide (cross platform F# VS Code tools) working folder 350 | .ionide/ 351 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning Collection 2 | Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning. 3 | 4 | ## Table of Contents 5 | 6 | - [Boosting](#boosting) 7 | - [AutoML](#automl) 8 | - [Neural Network](#neural-network) 9 | - [Graph&Network](#graph--network) 10 | - [Vision](#vision) 11 | - [Time Series](#time-series) 12 | - [NLP](#nlp) 13 | - [Online Machine Learning](#interactive-machine-learning) 14 | - [Recommendation](#recommendation) 15 | - [Distributed](#distributed) 16 | - [Causal Inference](#causal-inference) 17 | - [Responsible AI](#responsible-ai) 18 | - [Optimization](#optimization) 19 | - [Reinforcement Learning](#reinforcement-learning) 20 | - [Windows](#windows) 21 | - [Datasets](#datasets) 22 | - [Debug](#debug) 23 | - [Pipeline](#pipeline) 24 | - [Platform](#platform) 25 | - [Tagging](#tagging) 26 | - [Developer tool](#developer-tool) 27 | - [Sample Code](#sample-code) 28 | - [Workshop](#workshop) 29 | - [Book](#book) 30 | - [Learning](#learning) 31 | - [Blog, News & Webinar](#blog-news--webinar) 32 | 33 | --- 34 | 35 | ### Boosting 36 | - [LightGBM](https://github.com/microsoft/LightGBM) - A fast, distributed, high performance gradient boosting framework. 37 | - [LightGBM benchmarking suite](https://github.com/microsoft/lightgbm-benchmark) - Benchmark tools for LightGBM. 38 | - [Explainable Boosting Machines](https://github.com/interpretml/interpret) - interpretable model developed in Microsoft Research using bagging, gradient boosting, and automatic interaction detection to estimated generalized additive models. 39 | - [Cyclic Boosting Machines](https://github.com/microsoft/CBM) - An explainable supervised machine learning algorithm specifically for predicting count-data, such as sales and demand. 40 | 41 | ### AutoML 42 | - [Neural Network Intelligence](https://github.com/microsoft/nni) - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning. 43 | - [Archai](https://github.com/microsoft/archai) - Reproducible Rapid Research for Neural Architecture Search (NAS). 44 | - [FLAML](https://github.com/microsoft/FLAML) - A fast and lightweight AutoML library. 45 | - [Azure Automated Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml) - Automated Machine Learning for Tabular data (regression, classification and forecasting) by Azure Machine Learning 46 | - [Cream](https://github.com/microsoft/Cream) - A collection of Microsoft NAS and Vision Transformer work. 47 | 48 | 49 | ### Neural Network 50 | - [PyMarlin](https://github.com/microsoft/PyMarlin) - Lightweight Deep Learning Model Training library based on PyTorch. 51 | - [bayesianize](https://github.com/microsoft/bayesianize) - A Bayesian neural network wrapper in pytorch. 52 | - [O-CNN](https://github.com/microsoft/O-CNN) - Octree-based convolutional neural networks for 3D shape analysis. 53 | - [ResNet](https://github.com/KaimingHe/deep-residual-networks) - deep residual network. 54 | - [CNTK](https://github.com/microsoft/CNTK) - microsoft cognitive toolkit (CNTK), open source deep-learning toolkit. 55 | - [InfiniBatch](https://github.com/microsoft/infinibatch) - Efficient, check-pointed data loading for deep learning with massive data sets. 56 | - [Models under Hugging Face](https://huggingface.co/microsoft) - Microsoft shares transformer models at Hugging Face. 51 pretrained models (as of June 28, 2021). 57 | - [Muzic](https://github.com/microsoft/muzic) - Music Understanding and Generation with Artificial Intelligence. 58 | 59 | ### Graph & Network 60 | - [graspologic](https://github.com/microsoft/graspologic) - utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms. 61 | - [TF Graph Neural Network Samples](https://github.com/microsoft/tf-gnn-samples) - tensorFlow implementations of graph neural networks. 62 | - [ptgnn](https://github.com/microsoft/ptgnn) - PyTorch Graph Neural Network Library 63 | - [StemGNN](https://github.com/microsoft/StemGNN) - spectral temporal graph neural network (StemGNN) for multivariate time-series forecasting. 64 | - [SPTAG](https://github.com/Microsoft/SPTAG) - a distributed approximate nearest neighborhood search (ANN) library. 65 | - [DiskANN](https://github.com/microsoft/DiskANN) - Scalable graph based indices for approximate nearest neighbor search. 66 | - 67 | ### Vision 68 | - [Microsoft Vision Model ResNet50](https://www.microsoft.com/en-us/research/blog/microsoft-vision-model-resnet-50-combines-web-scale-data-and-multi-task-learning-to-achieve-state-of-the-art/) - a large pretrained vision ResNet-50 model using search engine's web-scale image data. 69 | - [Oscar](https://github.com/microsoft/Oscar) - Object-Semantics Aligned Pre-training for Vision-Language Tasks. 70 | - [TorchGeo](https://github.com/microsoft/torchgeo) - a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data. 71 | - [Swin Transformer](https://github.com/microsoft/Swin-Transformer) - an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows". 72 | 73 | ### Time Series 74 | - [luminol](https://github.com/linkedin/luminol) - anomaly detection and correlation library. 75 | - [SR-CNN](https://github.com/microsoft/anomalydetector) - Spectral Residual based anomaly detection algorithm, SR-CNN implementation. 76 | - [Greykite](https://github.com/linkedin/greykite) - flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite. 77 | - [Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery](https://github.com/microsoft/temporal-cluster-matching) - An implementation of the temporal cluster matching method for detecting change in structure footprints from time series of remotely sensed imagery. 78 | - [Microsoft Finance Time Series Forecasting Framework](https://github.com/microsoft/finnts) - a forecasting package that utilizes cutting-edge time series forecasting and parallelization on the cloud to produce accurate forecasts for financial data. 79 | - [FOST](https://github.com/microsoft/FOST) - an easy-use tool for spatial-temporal forecasting 80 | 81 | ### NLP 82 | - [T-ULRv2](https://www.microsoft.com/en-us/research/blog/microsoft-turing-universal-language-representation-model-t-ulrv2-tops-xtreme-leaderboard/) - Turing multilingual language model. 83 | - [Turing-NLG](https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/) - Turing Natural Language Generation, 17 billion-parameter language model. 84 | - [DeBERTa](https://github.com/microsoft/DeBERTa) - Decoding-enhanced BERT with Disentangled Attention 85 | - [UniLM](https://github.com/microsoft/unilm) - Unified Language Model Pre-training / Pre-training for NLP and Beyond 86 | - [Unicoder](https://github.com/microsoft/Unicoder) - Unicoder model for understanding and generation. 87 | - [NeuronBlocks](https://github.com/microsoft/NeuronBlocks) - building your nlp dnn models like playing lego 88 | - [Multilingual Model Transfer](https://github.com/microsoft/Multilingual-Model-Transfer) - new deep learning models for bootstrapping language understanding models for languages with no labeled data using labeled data from other languages. 89 | - [MT-DNN](https://github.com/microsoft/MT-DNN) - multi-task deep neural networks for natural language understanding. 90 | - [inmt](https://github.com/microsoft/inmt) - interactive neural machine trainslation-lite 91 | - [OpenKP](https://github.com/microsoft/OpenKP) - automatically extracting keyphrases that are salient to the document meanings is an essential step in semantic document understanding. 92 | - [DeText](https://github.com/linkedin/detext) - a deep neural text understanding framework for ranking and classification tasks. 93 | - [Genalog](https://github.com/microsoft/genalog) - an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities. 94 | - [FastFormers](https://github.com/microsoft/fastformers) - highly efficient transformer models for NLU. 95 | - [VERSEAGILITY](https://github.com/microsoft/verseagility) - a Python-based toolkit to ramp up your custom natural language processing (NLP) task, allowing you to bring your own data and bring models into production. It is a central component of the Microsoft Data Science Toolkit. 96 | - [DPU Utilities](https://github.com/Microsoft/dpu-utils) - Utilities used by the Deep Program Understanding team. 97 | - [KEAR](https://github.com/microsoft/KEAR) - Official code for achieving human parity on CommonsenseQA with External Attention. 98 | - [Prompt Engine](https://github.com/microsoft/prompt-engine-py) - A utility library for creating and maintaining prompts for Large Language Models. 99 | 100 | ### Online Machine Learning 101 | - [Vowpal Wabbit](https://vowpalwabbit.org/) - fast, efficient, and flexible online machine learning techniques for reinforcement learning, supervised learning, and more. 102 | 103 | ### Recommendation 104 | - [Recommenders](https://github.com/microsoft/recommenders) - examples and best practics for building recommendation systems (A2SVD, DKN, xDeepFM, LightGBM, LSTUR, NAML, NPA, NRMS, RLRMC, SAR, Vowpal Wabbit are invented/contributed by Microsoft). 105 | - [GDMIX](https://github.com/linkedin/gdmix) - A deep ranking personalization framework 106 | - [rankerEval](https://github.com/microsoft/rankerEval) - A fast numpy-based implementation of ranking metrics for information retrieval and recommendation. 107 | 108 | 109 | ### Distributed 110 | - [DeepSpeed](https://github.com/microsoft/DeepSpeed) - DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. 111 | - [MMLSpark](https://github.com/Azure/mmlspark) - machine learning library on spark. 112 | - [photon-ml](https://github.com/linkedin/photon-ml) - a scalable machine learning library on apache spark. 113 | - [TonY](https://github.com/linkedin/TonY) - framework to natively run deep learning frameworks on apache hadoop. 114 | - [isolation-forest](https://github.com/linkedin/isolation-forest) - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm. 115 | 116 | ### Causal Inference 117 | - [EconML](https://github.com/microsoft/EconML) - Python package for estimating heterogeneous treatment effects from observational data via machine learning. 118 | - [DoWhy](https://github.com/microsoft/dowhy) - Python library for causal inference that supports explicit modeling and testing of causal assumptions. 119 | 120 | ### Responsible AI 121 | - [InterpretML](https://interpret.ml/) - a toolkit to help understand models and enable responsbile machine learning. 122 | - [Interpret Community](https://github.com/interpretml/interpret-community) - extends interpret repo with additional interpretability techniques and utility functions. 123 | - [DiCE](https://github.com/interpretml/DiCE) - diverse counterfactual explanations. 124 | - [Interpret-Text](https://github.com/interpretml/interpret-text) - state-of-the-art explainers for text-based ml models and visualize with dashboard. 125 | - [fairlearn](https://github.com/fairlearn/fairlearn) - python package to assess and improve fairness of machine learning models. 126 | - [LiFT](https://github.com/linkedin/LiFT) - linkedin fairness toolkit. 127 | - [RobustDG](https://github.com/microsoft/robustdg) - Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks. 128 | - [SHAP](https://github.com/slundberg/shap) - a game theoretic approach to explain the output of any machine learning model (scott lundbert, Microsoft Research). 129 | - [LIME](https://github.com/marcotcr/lime) - explaining the predictions of any machine learning classifier (Marco, Microsoft Research). 130 | - [BackwardCompatibilityML](https://github.com/microsoft/BackwardCompatibilityML) - Project for open sourcing research efforts on Backward Compatibility in Machine Learning 131 | - [confidential-ml-utils](https://github.com/Azure/confidential-ml-utils) - Python utilities for training and deploying ML models against data you can't see. 132 | - [presidio](https://github.com/Microsoft/presidio) - context aware, pluggable and customizable data protection and anonymization service for text and images. 133 | - [Presidio-research](https://github.com/microsoft/presidio-research) - This package features data-science related tasks for developing new recognizers for Presidio. 134 | - [Confidential ONNX Inference Server](https://github.com/microsoft/onnx-server-openenclave) - An Open Enclave port of the ONNX inference server with data encryption and attestation capabilities to enable confidential inference on Azure Confidential Computing. 135 | - [Responsible-AI-Widgets](https://github.com/microsoft/responsible-ai-widgets) - responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on. 136 | - [Error Analysis](https://erroranalysis.ai/) - A toolkit to help analyze and improve model accuracy. 137 | - [Secure Data Sandbox](https://github.com/microsoft/secure-data-sandbox) - A toolkit for conducting machine learning trials against confidential data. 138 | - [shrike](https://azure.github.io/shrike/) - Python utilities to aid "compliant experiment" in Azure Machine Learning - training ML models without seeing the training data. 139 | - [HAX Toolkit](http://aka.ms/haxtoolkit) - The Human-AI eXperience (HAX) Toolkit is a set of practical tools for creating human-AI experiences with people in mind from the beginning. 140 | - [GAM Changer](https://github.com/interpretml/gam-changer) - Edit machine learning models to reflect human knowledge and values. 141 | - [AdaTest](https://github.com/microsoft/adatest) - Find and fix bugs in natural language machine learning models using adaptive testing. 142 | 143 | 144 | ### Optimization 145 | - [ONNXRuntime](https://github.com/microsoft/onnxruntime) - cross-platfom, high performance ML inference and training accelerator. 146 | - [ONNX Runtime for PyTorch](https://github.com/pytorch/ort) - Accelerate PyTorch models with ONNX Runtime. 147 | - [ONNX Runtime Training Examples](https://github.com/microsoft/onnxruntime-training-examples) - examples for using onnx runtime for model training. 148 | - [ONNX Runtime Inference Examples](https://github.com/microsoft/onnxruntime-inference-examples) - examples for using onnx runtime for model inference. 149 | - [ONNX Converter](https://github.com/microsoft/onnxconverter-common) - common utilities for onnx converters. 150 | - [ONNX.js](https://github.com/microsoft/onnxjs) - run onnx models using javascript. 151 | - [ONNX.js Demo](https://github.com/microsoft/onnxjs-demo) - demos for ONNX.js. 152 | - [Olive](https://github.com/microsoft/OLive) - a sequence of docker images that automates the process of ONNX model shipping. 153 | - [Hummingbird](https://github.com/microsoft/hummingbird) - compile trained ml model into tensor computation for faster inference. 154 | - [EdgeML](https://github.com/microsoft/EdgeML) - provides code for machine learning algorithms for edge devices developed at Microsoft Research India. 155 | - [DirectML](https://github.com/microsoft/DirectML) - high-performance, hardware-accelerated DirectX 12 library for machine learning. 156 | - [MMdnn](https://github.com/microsoft/MMdnn) - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. 157 | - [inifinibatch](https://github.com/microsoft/infinibatch) - Efficient, check-pointed data loading for deep learning with massive data sets. 158 | - [InferenceSchema](https://github.com/Azure/InferenceSchema) - Schema decoration for inference code 159 | - [nnfusion](https://github.com/microsoft/nnfusion) - flexible and efficient deep neural network compiler. 160 | - [Accera](https://github.com/microsoft/Accera) - Open source cross-platform compiler for compute-intensive loops used in AI algorithms, from Microsoft Research. 161 | 162 | ### Reinforcement Learning 163 | - [AirSim](https://github.com/microsoft/AirSim) - open source simulator for autonomous vehicles build on unreal engine / unity from microsoft research. 164 | - [TextWorld](https://github.com/microsoft/TextWorld) - TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games. 165 | - [Moab](https://github.com/microsoft/moab) - Project Moab, a new open-source balancing robot to help engineers and developers learn how to build real-world autonomous control systems with Project Bonsai. 166 | - [MARO](https://github.com/microsoft/maro) - multi-agent resource optimization (MARO) platfom. 167 | - [Training Data-Driven or Surrogate Simulators](https://github.com/microsoft/datadrivenmodel) - build simulation from data for use in RL and Bonsai platform for machine teaching. 168 | - [Bonsai](https://azure.microsoft.com/en-us/services/project-bonsai/) - low code industrial machine teaching platform. 169 | - [Bonsai Python SDK](https://github.com/BonsaiAI/bonsai3-py) - A python library for integrating data sources with Bonsai BRAIN. 170 | 171 | - [SEGAR](https://github.com/microsoft/segar) - Sandbox environment for generalizable agent research. 172 | 173 | ### Security 174 | - [counterfit](https://github.com/Azure/counterfit) - a CLI that provides a generic automation layer for assessing the security of ML models. 175 | - [Federated Learning Simulation Framework](https://github.com/microsoft/fl-simulation) - a flexible framework for running experiments with PyTorch models in a simulated Federated Learning (FL) environment. 176 | - [FLUTE](https://github.com/microsoft/msrflute) - a platform for conducting high-performance federated learning simulations. 177 | 178 | 179 | ### Windows 180 | - [Windows Machine Learning](https://github.com/microsoft/Windows-Machine-Learning) - Machine Learning on Windows. 181 | 182 | ### Datasets 183 | - [COCO Dataset](https://cocodataset.org/#home) - COCO is a large-scale object detection, segmentation, and captioning dataset. 184 | - [MS MARCO](https://microsoft.github.io/msmarco/) - collection of datasets focused on deep learning in search. 185 | - [InnerEye CreateDataset](https://github.com/microsoft/InnerEye-CreateDataset) - InnerEye dataset creation tool for InnerEye-DeepLearning library. Transforms DICOM data into mask for training Deep Learning models. 186 | - [Sepsis Cohort from MIMIC III](https://github.com/microsoft/mimic_sepsis) - Sepsis cohort from MIMIC dataset. 187 | - [MIND : Microsoft News Dataset](https://msnews.github.io/) - a large-scale dataset for news recommendation research. 188 | - [Dataset for AI for Earth](https://github.com/microsoft/AIforEarthDataSets) - AIForEarthDataSets is a collection of datasets for AI research. 189 | - [ORBIT](https://github.com/microsoft/ORBIT-Dataset) - a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. 190 | - [EcoFlows](https://github.com/microsoft/Ecoflow) - Community-representation to collaborate on labelled AI data for ecological and agricultural scenarios in APAC, updated monthly. 191 | 192 | 193 | ### Debug & Benchmark 194 | - [tensorwatch](https://github.com/microsoft/tensorwatch) - debugging, monitoring and visualization for python machine learning and data science. 195 | - [PYRIGHT](https://github.com/microsoft/pyright) - static type checker for python. 196 | - [Bench ML](https://github.com/microsoft/benchml) - Python library to benchmark popular pre-built cloud AI APIs. 197 | - [debugpy](https://github.com/microsoft/debugpy) - An implementation of the Debug Adapter Protocol for Python 198 | - [kineto](https://github.com/pytorch/kineto) - A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters contributed by Azure AI Platform team. 199 | - [SuperBenchmark](https://github.com/microsoft/superbenchmark) - a benchmarking and diagnosis tool for AI infrastructure (software & hardware). 200 | - [tempeh](https://github.com/microsoft/tempeh) - tempeh is a framework to TEst Machine learning PErformance exHaustively which includes tracking memory usage and run time. 201 | 202 | 203 | 204 | ### Pipeline 205 | - [GitHub Actions](https://github.com/features/actions) - Automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. 206 | - [Azure Pipelines](https://azure.microsoft.com/en-us/services/devops/pipelines/) - Automate your builds and deployments with Pipelines so you spend less time with the nuts and bolts and more time being creative. 207 | - [Dagli](https://github.com/linkedin/dagli) - framework for defining machine learning models, including feature generation and transformations as DAG. 208 | 209 | ### Platform 210 | - [AI for Earth API Platform](https://github.com/microsoft/AIforEarth-API-Platform) - distributed infrastructure designed to provide a secure, scalable, and customizable API hosting, designed to handle the needs of long-running/asynchronous machine learning model inference. 211 | 212 | - [Open Platfom for AI (OpenPAI)](https://github.com/Microsoft/pai) - resource scheduling and cluster management for AI. 213 | - [OpenPAI Runtime](https://github.com/microsoft/openpai-runtime) - Runtime for deep learning workload. 214 | - [OpenPAI Protocol](https://github.com/microsoft/openpai-protocol) - OpenPAI protocol enables job sharing and portability. 215 | - [Openpaimarketplace](https://github.com/microsoft/openpaimarketplace) - A marketplace which stores examples and job templates of openpai. 216 | - [OpenPAI FrameworkController](https://github.com/microsoft/frameworkcontroller) - built to orchestrate all kinds of applications on Kubernetes by a single controller. 217 | - [HivedDScheduler](https://github.com/microsoft/hivedscheduler) - Kubernetes Scheduler for Deep Learning. 218 | - [OpenPAI JS SDK](https://github.com/microsoft/openpaisdk) - The JavaScript SDK is designed to facilitate the developers of OpenPAI to offer user friendly experience. 219 | - [OpenPAI VS Code Client](https://github.com/microsoft/openpaivscode) - Extension to connect OpenPAI clusters, submit AI jobs, simulate jobs locally, manage files, and so on. 220 | 221 | - [MLOS](https://github.com/microsoft/MLOS) - Data Science powered infrastructure and methodology to democratize and automate Performance Engineering. 222 | - [Platform for Situated Intelligence](https://github.com/Microsoft/psi) - an open-source framework for multimodal, integrative AI. 223 | - [Qlib](https://github.com/microsoft/qlib) - an AI-oriented quantitative investment platform. 224 | 225 | ### Feature Engineering 226 | - [Feast on Azure](https://github.com/Azure/feast-azure) - Azure plugins for Feast (FEAture STore). 227 | - [Feathr](https://github.com/linkedin/feathr) - An Enterprise-Grade, High Performance Feature Store. 228 | 229 | ### Tagging 230 | - [TagAnomaly](https://github.com/microsoft/TagAnomaly) - Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category) 231 | - [VoTT](https://github.com/microsoft/VoTT) - Visual object tagging tool 232 | - [Satellite imagery annotation tool](https://github.com/microsoft/satellite-imagery-labeling-tool) - A lightweight web-interface for creating and sharing vector annotations over satellite/aerial imagery scenes. 233 | 234 | ### Developer tool 235 | - [Visual Studio Code](https://github.com/microsoft/vscode) - Code editor redefined and optimized for building and debugging modern web and cloud applications. 236 | - [Gather](https://marketplace.visualstudio.com/items?itemName=ms-python.gather) - adds gather functionality in the Python language to the Jupyter Extension. 237 | - [Pylance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylances) - an extension that works alongside Python in Visual Studio Code to provide performant language support. 238 | - [Azure ML Snippets](https://github.com/Azure/azureml-snippets) - VSCode snippets for Azure Machine Learning 239 | 240 | ### Sample Code 241 | - [Microsoft AI for Earth](https://www.microsoft.com/en-us/ai/ai-for-earth-tech-resources) 242 | - [Shared utilities](https://github.com/microsoft/ai4eutils) - A collection of utilities for working with Azure Machine Learning. 243 | - [acoustic-bird-detection](https://github.com/microsoft/acoustic-bird-detection) - Tutorial: Accurate Bioacoustic Species Detection from Small Numbers of Training Clips Using the Biophony Model 244 | - [beluasound](https://github.com/microsoft/belugasounds) - Using machine learning to detect beluga whale calls in hydrophone recordings. 245 | - [arcticseals](https://github.com/microsoft/arcticseals) - detect & classify arctic seals in aerial imagery to understand how they’re adapting to a changing world. 246 | - [AIDE: Annotation Interface for Data-driven Ecology](https://github.com/microsoft/aerial_wildlife_detection) - Detecting and classifying wildlife in aerial imagery. 247 | - [Camera Trap Tool](https://github.com/microsoft/CameraTraps) - tools for training and running detectors and classifiers for wildlife images collected from motion-triggered cameras. 248 | - [Land cover mapping the Orinoquía region](https://github.com/microsoft/landcover-orinoquia) - A tool for predicting landcover in the Orinoquia region of Peru. 249 | - [Planetary Computer Hub](https://github.com/microsoft/PlanetaryComputerExamples) - a development environment that makes our data and APIs accessible through familiar, open-source tools, and allows users to easily scale their analyses. 250 | - [Poultry barn mapping](https://github.com/microsoft/poultry-cafos) - code for detecting poultry barns from high-resolution aerial imagery and an accompanying dataset of predicted barns over the United States. 251 | - [Planetary Computer SDK for Python](https://github.com/microsoft/planetary-computer-sdk-for-python) - A Python SDK for the Planetary Computer Hub. 252 | - [Species Classification](https://github.com/microsoft/SpeciesClassification) - A tool for classifying species in images. 253 | - [News Threads](https://github.com/microsoft/News-Threads) - The News Threads project analyzes news articles to help find similarities between news articles and trace news provenance across time. 254 | - [InnerEye DeepLearning](https://github.com/microsoft/InnerEye-DeepLearning) - Medical Imaging Deep Learning library to train and deploy models on Azure Machine Learning and Azure Stack 255 | - [Deep Seismic](https://github.com/microsoft/seismic-deeplearning) - Deep Learning for Seismic Imaging and Interpretation 256 | - [Multi-species bioacoustic classification](https://github.com/microsoft/Multi_Species_Bioacoustic_Classification) - Multi-species bioacoustic classification using deep learning algorithms. 257 | - [Nestle Acne Assessment](https://github.com/microsoft/nestle-acne-assessment) - deep learning models to assess the acne severity level based on selfie images. 258 | - [Visual Analogies](https://github.com/microsoft/art) - exploring the connections between artworks with deep "Visual Analogies". 259 | 260 | - [Forecasting Best Practices](https://github.com/microsoft/forecasting) - time series forecasting best practices & examples. 261 | 262 | - [Computer Vision Recipes](https://github.com/microsoft/computervision-recipes) - best practices, code samples, and documentation for Computer Vision. 263 | 264 | - [DeepSpeed Examples](https://github.com/microsoft/DeepSpeedExamples) - Example models using DeepSpeed 265 | 266 | - [A TALE OF THREE CITIES](https://github.com/microsoft/A-TALE-OF-THREE-CITIES) - Analyzing the safety (311) dataset published by Azure Open Datasets for Chicago, Boston and New York City using SparkR, SParkSQL, Azure Databricks, visualization using ggplot2 and leaflet. 267 | 268 | - [Microsoft Health Intelligence Machine Learning Toolbox](https://github.com/microsoft/hi-ml) - Microsoft Health Intelligence Azure Machine Learning Toolbox. 269 | 270 | - [Azure Machine Learning examples](https://github.com/Azure/azureml-examples) - official community-driven Azure Machine Learning examples, tested with GitHub Actions. 271 | 272 | - [Azure Machine Learning R Template](https://github.com/microsoft/AzureML-R-template) - patterns and examples for running R code with Azure Machine Learning. 273 | 274 | - [Azure Machine Learning Python SDK notebooks](https://github.com/Azure/MachineLearningNotebooks) - python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK. 275 | 276 | - [Azure Machine Learning Gallery](https://github.com/Azure/AzureMachineLearningGallery) - this repo enables our growing community of developers and data scientists to share their machine learning pipelines, components, etc. to accelerate productivity in the machine learning lifecycle. 277 | 278 | - [Azure Machine Learning previews](https://github.com/Azure/azureml-previews) - samples for preview features in Azure Machine Learning. 279 | 280 | - [AzureML Designer Sample](https://github.com/Azure/MachineLearningDesigner) - samples of Azure Machine Learning designer. 281 | - [Ship Detector](https://github.com/microsoft/ShipDetection) - train an object detection model that can detect and locate ships in a satellite image. 282 | 283 | - Microsoft Solution Accelerators 284 | - [MLOps Solution Accelerator](https://github.com/microsoft/dstoolkit-mlops-base) - this repository helps ML teams to accelerate their model deployment to production leveraging Azure. 285 | - [Anomaly Detection Solution Accelerator](https://github.com/microsoft/dstoolkit-anomaly-detection-ijungle) - implement Anomaly Detection which is the technique of identifying rare events or observations which can raise suspicions by being statistically different from the rest of the observations. 286 | - [Classification Solution Accelerator](https://github.com/microsoft/dstoolkit-classification-solution-accelerator) - This is a classification solution accelerator to help you build and deploy a binary classification project. 287 | 288 | - [Medical Imaging with Azure Machine Learning Demos](https://github.com/Azure/medical-imaging) - medical imaging demo repository. 289 | 290 | - [Federated Learning in Azure ML](https://github.com/Azure-Samples/azure-ml-federated-learning) - Examples and recipes around federated learning in Azure ML. 291 | 292 | - [Responsible AI Workshop](https://github.com/microsoft/responsible-ai-workshop) - Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice. 293 | 294 | - [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints) - Worldwide building footprints derived from satellite imagery. 295 | - [Genomics Data Analysis with Jupyter Notebooks on Azure](https://github.com/microsoft/genomicsnotebook) - Jupyter Notebooks on Azure for Genomics Data Analysis. 296 | 297 | 298 | ### Community 299 | - [AI@Edge Community](https://microsoft.github.io/ai-at-edge/) - find the resources you need to create solutions using intelligence at the edge through combinations of hardware, machine learning (ML), artificial intelligence (AI) and Microsoft Azure service. 300 | - [Global AI Community](https://globalai.community/) - empowers developers who are passionate about AI to share knowledge through events and meetups. 301 | - [Deep Learning Lab (Japan)](https://dllab.connpass.com/) - provides information on development cases and the latest technology trends related to deep learning. 302 | 303 | ### Workshop 304 | 305 | - [Workshop Wiki](https://github.com/microsoft/machine-learning-collection/wiki/Workshop) 306 | 307 | 308 | ### Competition 309 | - [2020 Machine Learning Security Evasion Competition](https://github.com/Azure/2020-machine-learning-security-evasion-competition) - code samples for the 2020 Machine Learning Security Evasion Competition. 310 | 311 | ### Book 312 | - [PRML](https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/) - Pattern Recognition and Machine Learning by Christopher Bishop 313 | - [Foundations of Data Science](https://www.cs.cornell.edu/jeh/book.pdf) - Basic Theory for Data Science. 314 | - [Mastering Azure Machine Learning: Perform large-scale end-to-end advanced machine learning in the cloud with Microsoft Azure Machine Learning](https://www.amazon.com/dp/1789807557/ref=cm_sw_r_tw_dp_SS0HC0EA2GXPK4QHF087) 315 | - [Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions](https://www.amazon.com/dp/149205559X/ref=cm_sw_r_tw_dp_EN0VKVBDE5MZ1VK39K3F) 316 | 317 | ## Learning 318 | - [Microsoft Learn](https://docs.microsoft.com/en-us/learn/) - Learning contents for Microsoft technology 319 | - [Data Scientist](https://docs.microsoft.com/en-us/learn/roles/data-scientist), [AI Engineer](https://docs.microsoft.com/en-us/learn/roles/ai-engineer) 320 | - [Data Science for Manager](https://github.com/microsoft/datascience4managers) - Generalization, Utility, and Experimentation: ML Concepts for Making Better Business Decisions 321 | - [Github Learning Lab](https://lab.github.com/) - learning contents for Github technology. 322 | - [Getting started with Python](https://github.com/microsoft/c9-python-getting-started) - Sample code for Channel 9 Python for Beginners course. 323 | - [Python for Beginners](https://channel9.msdn.com/Series/Intro-to-Python-Development?WT.mc_id=python-c9-niner) 324 | - [More Python for Beginners](https://channel9.msdn.com/Series/More-Python-for-Beginners) 325 | - [Even More Python for Beginners](https://channel9.msdn.com/Series/Even-More-Python-for-Beginners-Data-Tools) 326 | - [Get started with PyTorch](https://docs.microsoft.com/en-US/learn/paths/pytorch-fundamentals/)- learn the fundamentals of deep learning with PyTorch. 327 | - [Dev Intro to Data Science](https://channel9.msdn.com/Series/Dev-Intro-to-Data-Science) - In this 28-video series, you will learn important concepts and technologies to build your end-to-end machine learning applications on Azure. 328 | - [Machine Learning for Beginners - A Curriculum](https://github.com/microsoft/ML-For-Beginners) - 12 weeks, 24 lessons, classic Machine Learning for all 329 | - [Data Science for Beginners - A Curriculum](https://github.com/microsoft/Data-Science-For-Beginners) - 10 Weeks, 20 Lessons, Data Science for All! 330 | - [Artificial Intelligence for Beginners](https://github.com/microsoft/ai-for-beginners) - 12 Weeks, 24 Lessons, AI for All! 331 | - [Microsoft Cloud Workshop](https://github.com/microsoft/MCW-ML-Ops) - Wide World Importers (WWI) delivers innovative solutions for manufacturers. 332 | 333 | ## Blog, News & Webinar 334 | - [channel9 - AI Show](https://channel9.msdn.com/Shows/AI-Show) - videos for developers from people building Microsoft products and services. 335 | - [Microsoft Open Source Blog](https://cloudblogs.microsoft.com/opensource/) - blog about microsoft open source technology. 336 | - [Microsoft Research Event, Conference & Webinars](https://www.microsoft.com/en-us/research/webinar/) - Events, Conferences & Webinars by Microsoft Research. 337 | - [Microsoft Innovation Tech Hub](https://innovation.microsoft.com/en-us/developer) - AI project in Microsoft. 338 | - [LinkedIn Engineering Blog](https://engineering.linkedin.com/blog) - Blog by LinkedIn Engineering Team 339 | - [AI System](https://github.com/microsoft/AI-System) - system for AI Education Resource (Chinese). 340 | - [AI Edu](https://github.com/microsoft/ai-edu) - AI education materials for Chinese students, teachers and IT professionals (Chinese). 341 | 342 |
343 | --- 344 | 345 | 346 | ## Contributing 347 | 348 | This project welcomes contributions and suggestions. Most contributions require you to agree to a 349 | Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us 350 | the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. 351 | 352 | When you submit a pull request, a CLA bot will automatically determine whether you need to provide 353 | a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions 354 | provided by the bot. You will only need to do this once across all repos using our CLA. 355 | 356 | This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). 357 | For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or 358 | contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. 359 | 360 | ## Trademarks 361 | 362 | This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft 363 | trademarks or logos is subject to and must follow 364 | [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). 365 | Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. 366 | Any use of third-party trademarks or logos are subject to those third-party's policies. 367 | --------------------------------------------------------------------------------