josephmisiti/awesome-machine-learning
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Adding some of the .NET libraries, and NUGET packages I've used with C#, and F#.

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Ryan Donahue 2014-07-16 09:36:57 -04:00
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@ -216,6 +216,27 @@ on MNIST digits[DEEP LEARNING]
* [matlab_gbl](https://www.cs.purdue.edu/homes/dgleich/packages/matlab_bgl/) - MatlabBGL is a Matlab package for working with graphs.
* [gamic](http://www.mathworks.com/matlabcentral/fileexchange/24134-gaimc---graph-algorithms-in-matlab-code) - Efficient pure-Matlab implementations of graph algorithms to complement MatlabBGL's mex functions.
## .NET
#### Computer Vision
#### Natural Language Processing
* [OpenCVDotNet](https://code.google.com/p/opencvdotnet/) - A wrapper for the OpenCV project to be used with .NET applications.
* [Emgu CV](http://www.emgu.com/wiki/index.php/Main_Page) - Cross platform wrapper of OpenCV which can be compiled in Mono to e run on Windows, Linus, Mac OS X, iOS, and Android.
#### General-Purpose Machine Learning
* [Accord.MachineLearning](http://www.nuget.org/packages/Accord.MachineLearning/) - Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications. This package is part of the Accord.NET Framework.
* [Vulpes](https://github.com/fsprojects/Vulpes) - Deep belief and deep learning implementation written in F# and leverages CUDA GPU execution with Alea.cuBase.
* [Encog](http://www.nuget.org/packages/encog-dotnet-core/) - An advanced neural network and machine learning framework. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trains using multithreaded resilient propagation. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train neural networks.
#### Data Analysis / Data Visualization
* [numl](http://www.nuget.org/packages/numl/) - numl is a machine learning library intended to ease the use of using standard modeling techniques for both prediction and clustering.
## Python
#### Natural Language Processing
@ -404,4 +425,3 @@ on MNIST digits[DEEP LEARNING]
* Some of the python libraries were cut-and-pasted from [vinta](https://github.com/vinta/awesome-python)
* The few go reference I found where pulled from [this page](https://code.google.com/p/go-wiki/wiki/Projects#Machine_Learning)