How to: Machine Learning and Data Science
* [Accord](https://github.com/accord-net/framework) - Machine learning, computer vision, statistics and general scientific computing for .NET.
* [Catalyst](https://github.com/curiosity-ai/catalyst) Cross-platform Natural Language Processing (NLP) library inspired by spaCy, with pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models. Part of the [SciSharp Stack](https://scisharp.github.io/SciSharp/)
* [ML.NET](https://github.com/dotnet/machinelearning) - Cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers [http://dot.net/ml](http://dot.net/ml).
* [Spreads](https://github.com/Spreads/Spreads/) - Series and Panels for Real-time and Exploratory Analysis of Data Streams.
* [TensorFlowSharp](https://github.com/migueldeicaza/TensorFlowSharp) - TensorFlow API for .NET languages.
* [WaveFunctionCollapse](https://github.com/mxgmn/WaveFunctionCollapse) - itmap & tilemap generation from a single example with the help of ideas from quantum mechanics.
* [SiaNet](https://github.com/SciSharp/SiaNet) - A C# deep learning library, human friendly, CUDA/OpenCL supported, well structured, easy to extend