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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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@ -57,12 +57,12 @@ If you want to contribute to this list (please do), send me a pull request or co
* [Stanford Parser] (http://nlp.stanford.edu/software/lex-parser.shtml) - A natural language parser is a program that works out the grammatical structure of sentences
* [Stanford POS Tagger] (http://nlp.stanford.edu/software/tagger.shtml) - A Part-Of-Speech Tagger (POS Tagger
* [Stanford Name Entity Recognizer] (http://nlp.stanford.edu/software/CRF-NER.shtml) - Stanford NER is a Java implementation of a Named Entity Recognizer.
* [Stanford Word Segmenter] (http://nlp.stanford.edu/software/segmenter.shtml) - Tokenization of raw text is a standard pre-processing step for many NLP tasks.
* [Stanford Word Segmenter] (http://nlp.stanford.edu/software/segmenter.shtml) - Tokenization of raw text is a standard pre-processing step for many NLP tasks.
* [Tregex, Tsurgeon and Semgrex](http://nlp.stanford.edu/software/tregex.shtml) - Tregex is a utility for matching patterns in trees, based on tree relationships and regular expression matches on nodes (the name is short for "tree regular expressions").
* [Stanford Phrasal: A Phrase-Based Translation System](http://nlp.stanford.edu/software/phrasal/)
* [Stanford English Tokenizer](http://nlp.stanford.edu/software/tokenizer.shtml) - Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java.
* [Stanford Tokens Regex](http://nlp.stanford.edu/software/tokensregex.shtml) - A tokenizer divides text into a sequence of tokens, which roughly correspond to "words"
* [Stanford Temporal Tagger](http://nlp.stanford.edu/software/sutime.shtml) - SUTime is a library for recognizing and normalizing time expressions.
* [Stanford Temporal Tagger](http://nlp.stanford.edu/software/sutime.shtml) - SUTime is a library for recognizing and normalizing time expressions.
* [Stanford SPIED](http://nlp.stanford.edu/software/patternslearning.shtml) - Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion
* [Stanford Topic Modeling Toolbox](http://nlp.stanford.edu/software/tmt/tmt-0.4/) - Topic modeling tools to social scientists and others who wish to perform analysis on datasets
* [Twitter Text Java](https://github.com/twitter/twitter-text-java) - A Java implementation of Twitter's text processing library
@ -79,7 +79,7 @@ If you want to contribute to this list (please do), send me a pull request or co
* [Stanford Classifier](http://nlp.stanford.edu/software/classifier.shtml) - A classifier is a machine learning tool that will take data items and place them into one of k classes.
* [Weka](http://www.cs.waikato.ac.nz/ml/weka/) - Weka is a collection of machine learning algorithms for data mining tasks
* [ORYX](https://github.com/cloudera/oryx) - Simple real-time large-scale machine learning infrastructure.
* [H2O](https://github.com/0xdata/h2o) - ML engine that supports distributed learning on data stored in HDFS.
* [H2O](https://github.com/0xdata/h2o) - ML engine that supports distributed learning on data stored in HDFS.
#### Data Analysis / Data Visualization
@ -159,7 +159,7 @@ If you want to contribute to this list (please do), send me a pull request or co
* [Data Read](https://github.com/WizardMac/DataRead.jl) - Read files from Stata, SAS, and SPSS
* [Hypothesis Tests](https://github.com/JuliaStats/HypothesisTests.jl) - Hypothesis tests for Julia
* [Gladfly](https://github.com/dcjones/Gadfly.jl) - Crafty statistical graphics for Julia.
* [Stats](https://github.com/johnmyleswhite/stats.jl) - Statistical tests for Julia
* [Stats](https://github.com/johnmyleswhite/stats.jl) - Statistical tests for Julia
* [RDataSets](https://github.com/johnmyleswhite/RDatasets.jl) - Julia package for loading many of the data sets available in R
* [DataFrames](https://github.com/JuliaStats/DataFrames.jl) - library for working with tabular data in Julia
@ -201,8 +201,8 @@ If you want to contribute to this list (please do), send me a pull request or co
#### General-Purpose Machine Learning
* [Training a deep autoencoder or a classifier
on MNIST digits](http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html) - Training a deep autoencoder or a classifier
* [Training a deep autoencoder or a classifier
on MNIST digits](http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html) - Training a deep autoencoder or a classifier
on MNIST digits[DEEP LEARNING]
* [t-Distributed Stochastic Neighbor Embedding](http://homepage.tudelft.nl/19j49/t-SNE.html) - t-Distributed Stochastic Neighbor Embedding (t-SNE) is a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.
* [Spider](http://people.kyb.tuebingen.mpg.de/spider/) - The spider is intended to be a complete object orientated environment for machine learning in Matlab.
@ -213,9 +213,30 @@ on MNIST digits[DEEP LEARNING]
#### Data Analysis / Data Visualization
* [matlab_gbl](https://www.cs.purdue.edu/homes/dgleich/packages/matlab_bgl/) - MatlabBGL is a Matlab package for working with graphs.
* [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
@ -250,7 +271,7 @@ on MNIST digits[DEEP LEARNING]
* [Bolt](https://github.com/pprett/bolt) - Bolt Online Learning Toolbox
* [CoverTree](https://github.com/patvarilly/CoverTree) - Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree
* [nilearn](https://github.com/nilearn/nilearn) - Machine learning for NeuroImaging in Python
* [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox
* [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox
* [Pyevolve](https://github.com/perone/Pyevolve) - Genetic algorithm framework.
* [Caffe](http://caffe.berkeleyvision.org) - A deep learning framework developed with cleanliness, readability, and speed in mind.
@ -298,15 +319,15 @@ on MNIST digits[DEEP LEARNING]
* [mne-python-notebooks](https://github.com/mne-tools/mne-python-notebooks) - IPython notebooks for EEG/MEG data processing using mne-python
* [pandas cookbook](https://github.com/jvns/pandas-cookbook) - Recipes for using Python's pandas library
#### Kaggle Competition Source Code
#### Kaggle Competition Source Code
* [wiki challange](https://github.com/hammer/wikichallenge) - An implementation of Dell Zhang's solution to Wikipedia's Participation Challenge on Kaggle
* [kaggle insults](https://github.com/amueller/kaggle_insults) - Kaggle Submission for "Detecting Insults in Social Commentary"
* [kaggle_acquire-valued-shoppers-challenge](https://github.com/MLWave/kaggle_acquire-valued-shoppers-challenge) - Code for the Kaggle acquire valued shoppers challenge
* [kaggle-cifar](https://github.com/zygmuntz/kaggle-cifar) - Code for the CIFAR-10 competition at Kaggle, uses cuda-convnet
* [kaggle-blackbox](https://github.com/zygmuntz/kaggle-blackbox) - Deep learning made easy
* [kaggle-accelerometer](https://github.com/zygmuntz/kaggle-accelerometer) - Code for Accelerometer Biometric Competition at Kaggle
* [kaggle-advertised-salaries](https://github.com/zygmuntz/kaggle-advertised-salaries) - Predicting job salaries from ads - a Kaggle competition
* [kaggle-accelerometer](https://github.com/zygmuntz/kaggle-accelerometer) - Code for Accelerometer Biometric Competition at Kaggle
* [kaggle-advertised-salaries](https://github.com/zygmuntz/kaggle-advertised-salaries) - Predicting job salaries from ads - a Kaggle competition
* [kaggle amazon](https://github.com/zygmuntz/kaggle-amazon) - Amazon access control challenge
* [kaggle-bestbuy_big](https://github.com/zygmuntz/kaggle-bestbuy_big) - Code for the Best Buy competition at Kaggle
* [kaggle-bestbuy_small](https://github.com/zygmuntz/kaggle-bestbuy_small)
@ -324,9 +345,9 @@ on MNIST digits[DEEP LEARNING]
* [Treat](https://github.com/louismullie/treat) - Text REtrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby
* [Ruby Linguistics](http://www.deveiate.org/projects/Linguistics/) - Linguistics is a framework for building linguistic utilities for Ruby objects in any language. It includes a generic language-independant front end, a module for mapping language codes into language names, and a module which contains various English-language utilities.
* [Stemmer](https://github.com/aurelian/ruby-stemmer) - Expose libstemmer_c to Ruby
* [Stemmer](https://github.com/aurelian/ruby-stemmer) - Expose libstemmer_c to Ruby
* [Ruby Wordnet](http://www.deveiate.org/projects/Ruby-WordNet/) - This library is a Ruby interface to WordNet
* [Raspel](http://sourceforge.net/projects/raspell/) - raspell is an interface binding for ruby
* [Raspel](http://sourceforge.net/projects/raspell/) - raspell is an interface binding for ruby
* [UEA Stemmer](https://github.com/ealdent/uea-stemmer) - Ruby port of UEALite Stemmer - a conservative stemmer for search and indexing
* [Twitter-text-rb](https://github.com/twitter/twitter-text-rb) - A library that does auto linking and extraction of usernames, lists and hashtags in tweets
@ -395,7 +416,7 @@ on MNIST digits[DEEP LEARNING]
* [Conjecture](https://github.com/etsy/Conjecture) - Scalable Machine Learning in Scalding
* [brushfire](https://github.com/avibryant/brushfire) - decision trees for scalding
* [ganitha](https://github.com/tresata/ganitha) - scalding powered machine learning
* [adam](https://github.com/bigdatagenomics/adam) - A genomics processing engine and specialized file format built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.
* [adam](https://github.com/bigdatagenomics/adam) - A genomics processing engine and specialized file format built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.
* [bioscala](https://github.com/bioscala/bioscala) - Bioinformatics for the Scala programming language
* [BIDMach](https://github.com/BIDData/BIDMach) - CPU and GPU-accelerated Machine Learning Library.
@ -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)