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Awesome Machine Learning Tutorial
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<h1 id="Awesome-Machine-Learning"><a href="#Awesome-Machine-Learning" class="headerlink" title="Awesome Machine Learning "></a>Awesome Machine Learning <a href="https://github.com/sindresorhus/awesome" target="_blank" rel="external"><img src="https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg" alt="Awesome"></a></h1><p>A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php.</p>
<p>If you want to contribute to this list (please do), send me a pull request or contact me <a href="https://twitter.com/josephmisiti" target="_blank" rel="external">@josephmisiti</a><br>Also, a listed repository should be deprecated if:</p>
<ul>
<li>Repository’s owner explicitly say that “this library is not maintained”.</li>
<li>Not committed for long time (2~3 years).</li>
</ul>
<p>For a list of free machine learning books available for download, go <a href="https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md" target="_blank" rel="external">here</a>.</p>
<p>For a list of free-to-attend meetups and local events, go <a href="https://github.com/josephmisiti/awesome-machine-learning/blob/master/meetups.md" target="_blank" rel="external">here</a>.</p>
<h2 id="Table-of-Contents"><a href="#Table-of-Contents" class="headerlink" title="Table of Contents"></a>Table of Contents</h2><!-- MarkdownTOC depth=4 -->
<ul>
<li><a href="#apl">APL</a><ul>
<li><a href="#apl-general-purpose">General-Purpose Machine Learning</a></li>
</ul>
</li>
<li><a href="#c">C</a><ul>
<li><a href="#c-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#c-cv">Computer Vision</a></li>
</ul>
</li>
<li><a href="#cpp">C++</a><ul>
<li><a href="#cpp-cv">Computer Vision</a></li>
<li><a href="#cpp-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#cpp-nlp">Natural Language Processing</a></li>
<li><a href="#cpp-sequence">Sequence Analysis</a></li>
<li><a href="#cpp-gestures">Gesture Recognition</a></li>
</ul>
</li>
<li><a href="#common-lisp">Common Lisp</a><ul>
<li><a href="#common-lisp-general-purpose">General-Purpose Machine Learning</a></li>
</ul>
</li>
<li><a href="#clojure">Clojure</a><ul>
<li><a href="#clojure-nlp">Natural Language Processing</a></li>
<li><a href="#clojure-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#clojure-data-analysis">Data Analysis / Data Visualization</a></li>
</ul>
</li>
<li><a href="#elixir">Elixir</a><ul>
<li><a href="#elixir-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#elixir-nlp">Natural Language Processing</a></li>
</ul>
</li>
<li><a href="#erlang">Erlang</a><ul>
<li><a href="#erlang-general-purpose">General-Purpose Machine Learning</a></li>
</ul>
</li>
<li><a href="#go">Go</a><ul>
<li><a href="#go-nlp">Natural Language Processing</a></li>
<li><a href="#go-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#go-data-analysis">Data Analysis / Data Visualization</a></li>
</ul>
</li>
<li><a href="#haskell">Haskell</a><ul>
<li><a href="#haskell-general-purpose">General-Purpose Machine Learning</a></li>
</ul>
</li>
<li><a href="#java">Java</a><ul>
<li><a href="#java-nlp">Natural Language Processing</a></li>
<li><a href="#java-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#java-data-analysis">Data Analysis / Data Visualization</a></li>
<li><a href="#java-deep-learning">Deep Learning</a></li>
</ul>
</li>
<li><a href="#javascript">Javascript</a><ul>
<li><a href="#javascript-nlp">Natural Language Processing</a></li>
<li><a href="#javascript-data-analysis">Data Analysis / Data Visualization</a></li>
<li><a href="#javascript-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#javascript-misc">Misc</a></li>
</ul>
</li>
<li><a href="#julia">Julia</a><ul>
<li><a href="#julia-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#julia-nlp">Natural Language Processing</a></li>
<li><a href="#julia-data-analysis">Data Analysis / Data Visualization</a></li>
<li><a href="#julia-misc">Misc Stuff / Presentations</a></li>
</ul>
</li>
<li><a href="#lua">Lua</a><ul>
<li><a href="#lua-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#lua-demos">Demos and Scripts</a></li>
</ul>
</li>
<li><a href="#matlab">Matlab</a><ul>
<li><a href="#matlab-cv">Computer Vision</a></li>
<li><a href="#matlab-nlp">Natural Language Processing</a></li>
<li><a href="#matlab-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#matlab-data-analysis">Data Analysis / Data Visualization</a></li>
</ul>
</li>
<li><a href="#net">.NET</a><ul>
<li><a href="#net-cv">Computer Vision</a></li>
<li><a href="#net-nlp">Natural Language Processing</a></li>
<li><a href="#net-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#net-data-analysis">Data Analysis / Data Visualization</a></li>
</ul>
</li>
<li><a href="#objectivec">Objective C</a><ul>
<li><a href="#objectivec-general-purpose">General-Purpose Machine Learning</a></li>
</ul>
</li>
<li><a href="#ocaml">OCaml</a><ul>
<li><a href="#ocaml-general-purpose">General-Purpose Machine Learning</a></li>
</ul>
</li>
<li><a href="#perl">Perl</a><ul>
<li><a href="#perl-data">Data Analysis / Data Visualization</a></li>
<li><a href="#perl-ml">General-Purpose Machine Learning</a></li>
</ul>
</li>
<li><a href="#perl6">Perl 6</a></li>
<li><a href="#php">PHP</a><ul>
<li><a href="#php-nlp">Natural Language Processing</a></li>
<li><a href="#php-general-purpose">General-Purpose Machine Learning</a></li>
</ul>
</li>
<li><a href="#python">Python</a><ul>
<li><a href="#python-cv">Computer Vision</a></li>
<li><a href="#python-nlp">Natural Language Processing</a></li>
<li><a href="#python-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#python-data-analysis">Data Analysis / Data Visualization</a></li>
<li><a href="#python-misc">Misc Scripts / iPython Notebooks / Codebases</a></li>
<li><a href="#python-kaggle">Kaggle Competition Source Code</a></li>
<li><a href="#python-neural-networks">Neural Networks</a></li>
<li><a href="#python-reinforcement-learning">Reinforcement Learning</a></li>
</ul>
</li>
<li><a href="#ruby">Ruby</a><ul>
<li><a href="#ruby-nlp">Natural Language Processing</a></li>
<li><a href="#ruby-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#ruby-data-analysis">Data Analysis / Data Visualization</a></li>
<li><a href="#ruby-misc">Misc</a></li>
</ul>
</li>
<li><a href="#rust">Rust</a><ul>
<li><a href="#rust-general-purpose">General-Purpose Machine Learning</a></li>
</ul>
</li>
<li><a href="#r">R</a><ul>
<li><a href="#r-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#r-data-analysis">Data Analysis / Data Visualization</a></li>
</ul>
</li>
<li><a href="#sas">SAS</a><ul>
<li><a href="#sas-general-purpose">General-Purpose Machine Learning</a></li>
<li><a href="#sas-data-analysis">Data Analysis / Data Visualization</a></li>
<li><a href="#sas-mpp">High Performance Machine Learning (MPP)</a></li>
<li><a href="#sas-nlp">Natural Language Processing</a></li>
<li><a href="#sas-demos">Demos and Scripts</a></li>
</ul>
</li>
<li><a href="#scala">Scala</a><ul>
<li><a href="#scala-nlp">Natural Language Processing</a></li>
<li><a href="#scala-data-analysis">Data Analysis / Data Visualization</a></li>
<li><a href="#scala-general-purpose">General-Purpose Machine Learning</a></li>
</ul>
</li>
<li><a href="#swift">Swift</a><ul>
<li><a href="#swift-general-purpose">General-Purpose Machine Learning</a></li>
</ul>
</li>
<li><a href="#tensor">TensorFlow</a><ul>
<li><a href="#tensor-general-purpose">General-Purpose Machine Learning</a></li>
</ul>
</li>
<li><a href="#credits">Credits</a></li>
</ul>
<!-- /MarkdownTOC -->
<p><a name="apl"></a></p>
<h2 id="APL"><a href="#APL" class="headerlink" title="APL"></a>APL</h2><p><a name="apl-general-purpose"></a></p>
<h4 id="General-Purpose-Machine-Learning"><a href="#General-Purpose-Machine-Learning" class="headerlink" title="General-Purpose Machine Learning"></a>General-Purpose Machine Learning</h4><ul>
<li><a href="https://github.com/mattcunningham/naive-apl" target="_blank" rel="external">naive-apl</a> - Naive Bayesian Classifier implementation in APL</li>
</ul>
<p><a name="c"></a></p>
<h2 id="C"><a href="#C" class="headerlink" title="C"></a>C</h2><p><a name="c-general-purpose"></a></p>
<h4 id="General-Purpose-Machine-Learning-1"><a href="#General-Purpose-Machine-Learning-1" class="headerlink" title="General-Purpose Machine Learning"></a>General-Purpose Machine Learning</h4><ul>
<li><a href="https://github.com/pjreddie/darknet" target="_blank" rel="external">Darknet</a> - Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.</li>
<li><a href="https://github.com/GHamrouni/Recommender" target="_blank" rel="external">Recommender</a> - A C library for product recommendations/suggestions using collaborative filtering (CF).</li>
<li><a href="https://github.com/SeniorSA/hybrid-rs-trainner" target="_blank" rel="external">Hybrid Recommender System</a> - A hybrid recomender system based upon scikit-learn algorithms.</li>
</ul>
<p><a name="c-cv"></a></p>
<h4 id="Computer-Vision"><a href="#Computer-Vision" class="headerlink" title="Computer Vision"></a>Computer Vision</h4><ul>
<li><a href="https://github.com/liuliu/ccv" target="_blank" rel="external">CCV</a> - C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library</li>
<li><a href="http://www.vlfeat.org/" target="_blank" rel="external">VLFeat</a> - VLFeat is an open and portable library of computer vision algorithms, which has Matlab toolbox</li>
</ul>
<h3 id="Speech-Recognition"><a href="#Speech-Recognition" class="headerlink" title="Speech Recognition"></a>Speech Recognition</h3><ul>
<li><a href="http://htk.eng.cam.ac.uk/" target="_blank" rel="external">HTK</a> -The Hidden Markov Model Toolkit (HTK) is a portable toolkit for building and manipulating hidden Markov models.</li>
</ul>
<p><a name="cpp"></a></p>
<h2 id="C-1"><a href="#C-1" class="headerlink" title="C++"></a>C++</h2><p><a name="cpp-cv"></a></p>
<h4 id="Computer-Vision-1"><a href="#Computer-Vision-1" class="headerlink" title="Computer Vision"></a>Computer Vision</h4><ul>
<li><a href="http://dlib.net/imaging.html" target="_blank" rel="external">DLib</a> - DLib has C++ and Python interfaces for face detection and training general object detectors.</li>
<li><a href="http://eblearn.sourceforge.net/" target="_blank" rel="external">EBLearn</a> - Eblearn is an object-oriented C++ library that implements various machine learning models</li>
<li><a href="http://opencv.org" target="_blank" rel="external">OpenCV</a> - OpenCV has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS.</li>
<li><a href="https://github.com/ukoethe/vigra" target="_blank" rel="external">VIGRA</a> - VIGRA is a generic cross-platform C++ computer vision and machine learning library for volumes of arbitrary dimensionality with Python bindings.</li>
</ul>
<p><a name="cpp-general-purpose"></a></p>
<h4 id="General-Purpose-Machine-Learning-2"><a href="#General-Purpose-Machine-Learning-2" class="headerlink" title="General-Purpose Machine Learning"></a>General-Purpose Machine Learning</h4><ul>
<li><a href="https://github.com/jkomiyama/banditlib" target="_blank" rel="external">BanditLib</a> - A simple Multi-armed Bandit library.</li>
<li><a href="http://caffe.berkeleyvision.org" target="_blank" rel="external">Caffe</a> - A deep learning framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING]</li>
<li><a href="https://github.com/Microsoft/CNTK" target="_blank" rel="external">CNTK</a> - The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.</li>
<li><a href="https://code.google.com/p/cuda-convnet/" target="_blank" rel="external">CUDA</a> - This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING]</li>
<li><a href="https://github.com/antinucleon/cxxnet" target="_blank" rel="external">CXXNET</a> - Yet another deep learning framework with less than 1000 lines core code [DEEP LEARNING]</li>
<li><a href="https://github.com/beniz/deepdetect" target="_blank" rel="external">DeepDetect</a> - A machine learning API and server written in C++11. It makes state of the art machine learning easy to work with and integrate into existing applications.</li>
<li><a href="http://www.dmtk.io/" target="_blank" rel="external">Disrtibuted Machine learning Tool Kit (DMTK)</a> - A distributed machine learning (parameter server) framework by Microsoft. Enables training models on large data sets across multiple machines. Current tools bundled with it include: LightLDA and Distributed (Multisense) Word Embedding.</li>
<li><a href="http://dlib.net/ml.html" target="_blank" rel="external">DLib</a> - A suite of ML tools designed to be easy to imbed in other applications</li>
<li><a href="https://github.com/amznlabs/amazon-dsstne" target="_blank" rel="external">DSSTNE</a> - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility.</li>
<li><a href="https://github.com/clab/dynet" target="_blank" rel="external">DyNet</a> - A dynamic neural network library working well with networks that have dynamic structures that change for every training instance. Written in C++ with bindings in Python.</li>
<li><a href="https://code.google.com/archive/p/encog-cpp" target="_blank" rel="external">encog-cpp</a></li>
<li><a href="https://github.com/FidoProject/Fido" target="_blank" rel="external">Fido</a> - A highly-modular C++ machine learning library for embedded electronics and robotics.</li>
<li><a href="http://igraph.org/c/" target="_blank" rel="external">igraph</a> - General purpose graph library</li>
<li><a href="https://github.com/01org/daal" target="_blank" rel="external">Intel(R) DAAL</a> - A high performance software library developed by Intel and optimized for Intel’s architectures. Library provides algorithmic building blocks for all stages of data analytics and allows to process data in batch, online and distributed modes.</li>
<li><a href="https://github.com/Microsoft/LightGBM" target="_blank" rel="external">LightGBM</a> - Microsoft’s fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.</li>
<li><a href="https://mldb.ai" target="_blank" rel="external">MLDB</a> - The Machine Learning Database is a database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.</li>
<li><a href="http://www.mlpack.org/" target="_blank" rel="external">mlpack</a> - A scalable C++ machine learning library</li>
<li><a href="https://root.cern.ch" target="_blank" rel="external">ROOT</a> - A modular scientific software framework. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualization and storage.</li>
<li><a href="http://image.diku.dk/shark/sphinx_pages/build/html/index.html" target="_blank" rel="external">shark</a> - A fast, modular, feature-rich open-source C++ machine learning library.</li>
<li><a href="https://github.com/shogun-toolbox/shogun" target="_blank" rel="external">Shogun</a> - The Shogun Machine Learning Toolbox</li>
<li><a href="https://code.google.com/archive/p/sofia-ml" target="_blank" rel="external">sofia-ml</a> - Suite of fast incremental algorithms.</li>
<li><a href="http://mc-stan.org/" target="_blank" rel="external">Stan</a> - A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling</li>
<li><a href="https://languagemachines.github.io/timbl/" target="_blank" rel="external">Timbl</a> - A software package/C++ library implementing several memory-based learning algorithms, among which IB1-IG, an implementation of k-nearest neighbor classification, and IGTree, a decision-tree approximation of IB1-IG. Commonly used for NLP.</li>
<li><a href="https://github.com/JohnLangford/vowpal_wabbit/wiki" target="_blank" rel="external">Vowpal Wabbit (VW)</a> - A fast out-of-core learning system.</li>
<li><a href="https://github.com/baidu-research/warp-ctc" target="_blank" rel="external">Warp-CTC</a> - A fast parallel implementation of Connectionist Temporal Classification (CTC), on both CPU and GPU.</li>
<li><a href="https://github.com/dmlc/xgboost" target="_blank" rel="external">XGBoost</a> - A parallelized optimized general purpose gradient boosting library.</li>
</ul>
<p><a name="cpp-nlp"></a></p>
<h4 id="Natural-Language-Processing"><a href="#Natural-Language-Processing" class="headerlink" title="Natural Language Processing"></a>Natural Language Processing</h4><ul>
<li><a href="https://github.com/BLLIP/bllip-parser" target="_blank" rel="external">BLLIP Parser</a> - BLLIP Natural Language Parser (also known as the Charniak-Johnson parser)</li>
<li><a href="https://github.com/proycon/colibri-core" target="_blank" rel="external">colibri-core</a> - C++ library, command line tools, and Python binding for extracting and working with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.</li>
<li><a href="https://taku910.github.io/crfpp/" target="_blank" rel="external">CRF++</a> - Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks.</li>
<li><a href="http://www.chokkan.org/software/crfsuite/" target="_blank" rel="external">CRFsuite</a> - CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data.</li>
<li><a href="https://github.com/LanguageMachines/frog" target="_blank" rel="external">frog</a> - Memory-based NLP suite developed for Dutch: PoS tagger, lemmatiser, dependency parser, NER, shallow parser, morphological analyzer.</li>
<li><a href="https://github.com/LanguageMachines/libfolia" target="_blank" rel="external">libfolia</a> - C++ library for the <a href="http://proycon.github.io/folia/" target="_blank" rel="external">FoLiA format</a></li>
<li><a href="https://github.com/meta-toolkit/meta" target="_blank" rel="external">MeTA</a> - <a href="https://meta-toolkit.org/" target="_blank" rel="external">MeTA : ModErn Text Analysis</a> is a C++ Data Sciences Toolkit that facilitates mining big text data.</li>
<li><a href="https://github.com/mit-nlp/MITIE" target="_blank" rel="external">MIT Information Extraction Toolkit</a> - C, C++, and Python tools for named entity recognition and relation extraction</li>
<li><a href="https://github.com/LanguageMachines/ucto" target="_blank" rel="external">ucto</a> - Unicode-aware regular-expression based tokenizer for various languages. Tool and C++ library. Supports FoLiA format.</li>
</ul>
<h4 id="Speech-Recognition-1"><a href="#Speech-Recognition-1" class="headerlink" title="Speech Recognition"></a>Speech Recognition</h4><ul>
<li><a href="https://github.com/kaldi-asr/kaldi" target="_blank" rel="external">Kaldi</a> - Kaldi is a toolkit for speech recognition written in C++ and licensed under the Apache License v2.0. Kaldi is intended for use by speech recognition researchers.</li>
</ul>
<p><a name="cpp-sequence"></a></p>
<h4 id="Sequence-Analysis"><a href="#Sequence-Analysis" class="headerlink" title="Sequence Analysis"></a>Sequence Analysis</h4><ul>
<li><a href="https://github.com/ayoshiaki/tops" target="_blank" rel="external">ToPS</a> - This is an objected-oriented framework that facilitates the integration of probabilistic models for sequences over a user defined alphabet.</li>
</ul>
<p><a name="cpp-gestures"></a></p>
<h4 id="Gesture-Detection"><a href="#Gesture-Detection" class="headerlink" title="Gesture Detection"></a>Gesture Detection</h4><ul>
<li><a href="https://github.com/nickgillian/grt" target="_blank" rel="external">grt</a> - The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition.</li>
</ul>
<p><a name="common-lisp"></a></p>
<h2 id="Common-Lisp"><a href="#Common-Lisp" class="headerlink" title="Common Lisp"></a>Common Lisp</h2><p><a name="common-lisp-general-purpose"></a></p>
<h4 id="General-Purpose-Machine-Learning-3"><a href="#General-Purpose-Machine-Learning-3" class="headerlink" title="General-Purpose Machine Learning"></a>General-Purpose Machine Learning</h4><ul>
<li><a href="https://github.com/melisgl/mgl/" target="_blank" rel="external">mgl</a> - Neural networks (boltzmann machines, feed-forward and recurrent nets), Gaussian Processes</li>
<li><a href="https://github.com/melisgl/mgl-gpr/" target="_blank" rel="external">mgl-gpr</a> - Evolutionary algorithms</li>
<li><a href="https://github.com/melisgl/cl-libsvm/" target="_blank" rel="external">cl-libsvm</a> - Wrapper for the libsvm support vector machine library</li>
</ul>
<p><a name="clojure"></a></p>
<h2 id="Clojure"><a href="#Clojure" class="headerlink" title="Clojure"></a>Clojure</h2><p><a name="clojure-nlp"></a></p>
<h4 id="Natural-Language-Processing-1"><a href="#Natural-Language-Processing-1" class="headerlink" title="Natural Language Processing"></a>Natural Language Processing</h4><ul>
<li><a href="https://github.com/dakrone/clojure-opennlp" target="_blank" rel="external">Clojure-openNLP</a> - Natural Language Processing in Clojure (opennlp)</li>
<li><a href="https://github.com/r0man/inflections-clj" target="_blank" rel="external">Infections-clj</a> - Rails-like inflection library for Clojure and ClojureScript</li>
</ul>
<p><a name="clojure-general-purpose"></a></p>
<h4 id="General-Purpose-Machine-Learning-4"><a href="#General-Purpose-Machine-Learning-4" class="headerlink" title="General-Purpose Machine Learning"></a>General-Purpose Machine Learning</h4><ul>
<li><a href="https://github.com/ptaoussanis/touchstone" target="_blank" rel="external">Touchstone</a> - Clojure A/B testing library</li>
<li><a href="https://github.com/lspector/Clojush" target="_blank" rel="external">Clojush</a> - The Push programming language and the PushGP genetic programming system implemented in Clojure</li>
<li><a href="https://github.com/aria42/infer" target="_blank" rel="external">Infer</a> - Inference and machine learning in clojure</li>
<li><a href="https://github.com/antoniogarrote/clj-ml" target="_blank" rel="external">Clj-ML</a> - A machine learning library for Clojure built on top of Weka and friends</li>
<li><a href="https://github.com/engagor/dl4clj/" target="_blank" rel="external">DL4CLJ</a> - Clojure wrapper for Deeplearning4j</li>
<li><a href="https://github.com/jimpil/enclog" target="_blank" rel="external">Encog</a> - Clojure wrapper for Encog (v3) (Machine-Learning framework that specializes in neural-nets)</li>
<li><a href="https://github.com/vollmerm/fungp" target="_blank" rel="external">Fungp</a> - A genetic programming library for Clojure</li>
<li><a href="https://github.com/clojurewerkz/statistiker" target="_blank" rel="external">Statistiker</a> - Basic Machine Learning algorithms in Clojure.</li>
<li><a href="https://github.com/htm-community/clortex" target="_blank" rel="external">clortex</a> - General Machine Learning library using Numenta’s Cortical Learning Algorithm</li>
<li><a href="https://github.com/htm-community/comportex" target="_blank" rel="external">comportex</a> - Functionally composable Machine Learning library using Numenta’s Cortical Learning Algorithm</li>
<li><a href="https://github.com/thinktopic/cortex" target="_blank" rel="external">cortex</a> - Neural networks, regression and feature learning in Clojure.</li>
<li><a href="https://github.com/cloudkj/lambda-ml" target="_blank" rel="external">lambda-ml</a> - Simple, concise implementations of machine learning techniques and utilities in Clojure.</li>
</ul>
<p><a name="clojure-data-analysis"></a></p>
<h4 id="Data-Analysis-Data-Visualization"><a href="#Data-Analysis-Data-Visualization" class="headerlink" title="Data Analysis / Data Visualization"></a>Data Analysis / Data Visualization</h4><ul>
<li><a href="http://incanter.org/" target="_blank" rel="external">Incanter</a> - Incanter is a Clojure-based, R-like platform for statistical computing and graphics.</li>
<li><a href="https://github.com/Netflix/PigPen" target="_blank" rel="external">PigPen</a> - Map-Reduce for Clojure.</li>
<li><a href="https://github.com/clojurewerkz/envision" target="_blank" rel="external">Envision</a> - Clojure Data Visualisation library, based on Statistiker and D3</li>
</ul>
<p><a name="elixir"></a></p>
<h2 id="Elixir"><a href="#Elixir" class="headerlink" title="Elixir"></a>Elixir</h2><p><a name="elixir-general-purpose"></a></p>
<h4 id="General-Purpose-Machine-Learning-5"><a href="#General-Purpose-Machine-Learning-5" class="headerlink" title="General-Purpose Machine Learning"></a>General-Purpose Machine Learning</h4><ul>
<li><a href="https://github.com/fredwu/simple_bayes" target="_blank" rel="external">Simple Bayes</a> - A Simple Bayes / Naive Bayes implementation in Elixir.</li>
</ul>
<p><a name="elixir-nlp"></a></p>
<h4 id="Natural-Language-Processing-2"><a href="#Natural-Language-Processing-2" class="headerlink" title="Natural Language Processing"></a>Natural Language Processing</h4><ul>
<li><a href="https://github.com/fredwu/stemmer" target="_blank" rel="external">Stemmer</a> - An English (Porter2) stemming implementation in Elixir.</li>
</ul>
<p><a name="erlang"></a></p>
<h2 id="Erlang"><a href="#Erlang" class="headerlink" title="Erlang"></a>Erlang</h2><p><a name="erlang-general-purpose"></a></p>
<h4 id="General-Purpose-Machine-Learning-6"><a href="#General-Purpose-Machine-Learning-6" class="headerlink" title="General-Purpose Machine Learning"></a>General-Purpose Machine Learning</h4><ul>
<li><a href="https://github.com/discoproject/disco/" target="_blank" rel="external">Disco</a> - Map Reduce in Erlang</li>
</ul>
<p><a name="go"></a></p>
<h2 id="Go"><a href="#Go" class="headerlink" title="Go"></a>Go</h2><p><a name="go-nlp"></a></p>
<h4 id="Natural-Language-Processing-3"><a href="#Natural-Language-Processing-3" class="headerlink" title="Natural Language Processing"></a>Natural Language Processing</h4><ul>
<li><a href="https://github.com/reiver/go-porterstemmer" target="_blank" rel="external">go-porterstemmer</a> - A native Go clean room implementation of the Porter Stemming algorithm.</li>
<li><a href="https://github.com/Rookii/paicehusk" target="_blank" rel="external">paicehusk</a> - Golang implementation of the Paice/Husk Stemming Algorithm.</li>
<li><a href="https://github.com/tebeka/snowball" target="_blank" rel="external">snowball</a> - Snowball Stemmer for Go.</li>
<li><a href="https://github.com/Lazin/go-ngram" target="_blank" rel="external">go-ngram</a> - In-memory n-gram index with compression.</li>
<li><a href="https://github.com/ynqa/word-embedding" target="_blank" rel="external">word-embedding</a> - Word Embeddings: the full implementation of word2vec, GloVe in Go.</li>
</ul>
<p><a name="go-general-purpose"></a></p>
<h4 id="General-Purpose-Machine-Learning-7"><a href="#General-Purpose-Machine-Learning-7" class="headerlink" title="General-Purpose Machine Learning"></a>General-Purpose Machine Learning</h4><ul>
<li><a href="https://github.com/MaxHalford/gago" target="_blank" rel="external">gago</a> - Multi-population, flexible, parallel genetic algorithm.</li>
<li><a href="https://github.com/sjwhitworth/golearn" target="_blank" rel="external">Go Learn</a> - Machine Learning for Go</li>
<li><a href="https://github.com/daviddengcn/go-pr" target="_blank" rel="external">go-pr</a> - Pattern recognition package in Go lang.</li>
<li><a href="https://github.com/alonsovidales/go_ml" target="_blank" rel="external">go-ml</a> - Linear / Logistic regression, Neural Networks, Collaborative Filtering and Gaussian Multivariate Distribution</li>
<li><a href="https://github.com/jbrukh/bayesian" target="_blank" rel="external">bayesian</a> - Naive Bayesian Classification for Golang.</li>
<li><a href="https://github.com/thoj/go-galib" target="_blank" rel="external">go-galib</a> - Genetic Algorithms library written in Go / golang</li>
<li><a href="https://github.com/ryanbressler/CloudForest" target="_blank" rel="external">Cloudforest</a> - Ensembles of decision trees in go/golang.</li>
<li><a href="https://github.com/goml/gobrain" target="_blank" rel="external">gobrain</a> - Neural Networks written in go</li>
<li><a href="https://github.com/fxsjy/gonn" target="_blank" rel="external">GoNN</a> - GoNN is an implementation of Neural Network in Go Language, which includes BPNN, RBF, PCN</li>
<li><a href="https://github.com/dmlc/mxnet" target="_blank" rel="external">MXNet</a> - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.</li>
<li><a href="https://github.com/songtianyi/go-mxnet-predictor" target="_blank" rel="external">go-mxnet-predictor</a> - Go binding for MXNet c_predict_api to do inference with pre-trained model</li>
<li><a href="https://github.com/jinyeom/neat" target="_blank" rel="external">neat</a> - Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT)</li>
</ul>
<p><a name="go-data-analysis"></a></p>
<h4 id="Data-Analysis-Data-Visualization-1"><a href="#Data-Analysis-Data-Visualization-1" class="headerlink" title="Data Analysis / Data Visualization"></a>Data Analysis / Data Visualization</h4><ul>
<li><a href="https://github.com/StepLg/go-graph" target="_blank" rel="external">go-graph</a> - Graph library for Go/golang language.</li>
<li><a href="http://www.svgopen.org/2011/papers/34-SVGo_a_Go_Library_for_SVG_generation/" target="_blank" rel="external">SVGo</a> - The Go Language library for SVG generation</li>
<li><a href="https://github.com/fxsjy/RF.go" target="_blank" rel="external">RF</a> - Random forests implementation in Go</li>
</ul>
<p><a name="haskell"></a></p>
<h2 id="Haskell"><a href="#Haskell" class="headerlink" title="Haskell"></a>Haskell</h2><p><a name="haskell-general-purpose"></a></p>
<h4 id="General-Purpose-Machine-Learning-8"><a href="#General-Purpose-Machine-Learning-8" class="headerlink" title="General-Purpose Machine Learning"></a>General-Purpose Machine Learning</h4><ul>
<li><a href="https://github.com/ajtulloch/haskell-ml" target="_blank" rel="external">haskell-ml</a> - Haskell implementations of various ML algorithms.</li>
<li><a href="https://github.com/mikeizbicki/HLearn" target="_blank" rel="external">HLearn</a> - a suite of libraries for interpreting machine learning models according to their algebraic structure.</li>
<li><a href="https://wiki.haskell.org/HNN" target="_blank" rel="external">hnn</a> - Haskell Neural Network library.</li>
<li><a href="https://github.com/ajtulloch/hopfield-networks" target="_blank" rel="external">hopfield-networks</a> - Hopfield Networks for unsupervised learning in Haskell.</li>
<li><a href="https://github.com/ajtulloch/dnngraph" target="_blank" rel="external">caffegraph</a> - A DSL for deep neural networks</li>
<li><a href="https://github.com/jbarrow/LambdaNet" target="_blank" rel="external">LambdaNet</a> - Configurable Neural Networks in Haskell</li>
</ul>
<p><a name="java"></a></p>
<h2 id="Java"><a href="#Java" class="headerlink" title="Java"></a>Java</h2><p><a name="java-nlp"></a></p>
<h4 id="Natural-Language-Processing-4"><a href="#Natural-Language-Processing-4" class="headerlink" title="Natural Language Processing"></a>Natural Language Processing</h4><ul>
<li><a href="http://www.cortical.io/" target="_blank" rel="external">Cortical.io</a> - Retina: an API performing complex NLP operations (disambiguation, classification, streaming text filtering, etc…) as quickly and intuitively as the brain.</li>
<li><a href="http://nlp.stanford.edu/software/corenlp.shtml" target="_blank" rel="external">CoreNLP</a> - Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words</li>
<li><a href="http://nlp.stanford.edu/software/lex-parser.shtml" target="_blank" rel="external">Stanford Parser</a> - A natural language parser is a program that works out the grammatical structure of sentences</li>
<li><a href="http://nlp.stanford.edu/software/tagger.shtml" target="_blank" rel="external">Stanford POS Tagger</a> - A Part-Of-Speech Tagger (POS Tagger</li>
<li><a href="http://nlp.stanford.edu/software/CRF-NER.shtml" target="_blank" rel="external">Stanford Name Entity Recognizer</a> - Stanford NER is a Java implementation of a Named Entity Recognizer.</li>
<li><a href="http://nlp.stanford.edu/software/segmenter.shtml" target="_blank" rel="external">Stanford Word Segmenter</a> - Tokenization of raw text is a standard pre-processing step for many NLP tasks.</li>
<li><a href="http://nlp.stanford.edu/software/tregex.shtml" target="_blank" rel="external">Tregex, Tsurgeon and Semgrex</a> - 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”).</li>
<li><a href="http://nlp.stanford.edu/phrasal/" target="_blank" rel="external">Stanford Phrasal: A Phrase-Based Translation System</a></li>
<li><a href="http://nlp.stanford.edu/software/tokenizer.shtml" target="_blank" rel="external">Stanford English Tokenizer</a> - Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java.</li>
<li><a href="http://nlp.stanford.edu/software/tokensregex.shtml" target="_blank" rel="external">Stanford Tokens Regex</a> - A tokenizer divides text into a sequence of tokens, which roughly correspond to “words”</li>
<li><a href="http://nlp.stanford.edu/software/sutime.shtml" target="_blank" rel="external">Stanford Temporal Tagger</a> - SUTime is a library for recognizing and normalizing time expressions.</li>
<li><a href="http://nlp.stanford.edu/software/patternslearning.shtml" target="_blank" rel="external">Stanford SPIED</a> - Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion</li>
<li><a href="http://nlp.stanford.edu/software/tmt/tmt-0.4/" target="_blank" rel="external">Stanford Topic Modeling Toolbox</a> - Topic modeling tools to social scientists and others who wish to perform analysis on datasets</li>
<li><a href="https://github.com/twitter/twitter-text-java" target="_blank" rel="external">Twitter Text Java</a> - A Java implementation of Twitter’s text processing library</li>
<li><a href="http://mallet.cs.umass.edu/" target="_blank" rel="external">MALLET</a> - A Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.</li>
<li><a href="https://opennlp.apache.org/" target="_blank" rel="external">OpenNLP</a> - a machine learning based toolkit for the processing of natural language text.</li>
<li><a href="http://alias-i.com/lingpipe/index.html" target="_blank" rel="external">LingPipe</a> - A tool kit for processing text using computational linguistics.</li>
<li><a href="https://code.google.com/archive/p/cleartk" target="_blank" rel="external">ClearTK</a> - ClearTK provides a framework for developing statistical natural language processing (NLP) components in Java and is built on top of Apache UIMA.</li>
<li><a href="http://ctakes.apache.org/" target="_blank" rel="external">Apache cTAKES</a> - Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing system for information extraction from electronic medical record clinical free-text.</li>
<li><a href="https://github.com/clir/clearnlp" target="_blank" rel="external">ClearNLP</a> - The ClearNLP project provides software and resources for natural language processing. The project started at the Center for Computational Language and EducAtion Research, and is currently developed by the Center for Language and Information Research at Emory University. This project is under the Apache 2 license.</li>
<li><a href="https://github.com/IllinoisCogComp/illinois-cogcomp-nlp" target="_blank" rel="external">CogcompNLP</a> - This project collects a number of core libraries for Natural Language Processing (NLP) developed in the University of Illinois’ Cognitive Computation Group, for example <code>illinois-core-utilities</code> which provides a set of NLP-friendly data structures and a number of NLP-related utilities that support writing NLP applications, running experiments, etc, <code>illinois-edison</code> a library for feature extraction from illinois-core-utilities data structures and many other packages.</li>
</ul>
<p><a name="java-general-purpose"></a></p>
<h4 id="General-Purpose-Machine-Learning-9"><a href="#General-Purpose-Machine-Learning-9" class="headerlink" title="General-Purpose Machine Learning"></a>General-Purpose Machine Learning</h4><ul>
<li><a href="https://github.com/airbnb/aerosolve" target="_blank" rel="external">aerosolve</a> - A machine learning library by Airbnb designed from the ground up to be human friendly.</li>
<li><a href="http://www.amidsttoolbox.com/" target="_blank" rel="external">AMIDST Toolbox</a> - A Java Toolbox for Scalable Probabilistic Machine Learning.</li>
<li><a href="https://github.com/datumbox/datumbox-framework" target="_blank" rel="external">Datumbox</a> - Machine Learning framework for rapid development of Machine Learning and Statistical applications</li>
<li><a href="https://elki-project.github.io/" target="_blank" rel="external">ELKI</a> - Java toolkit for data mining. (unsupervised: clustering, outlier detection etc.)</li>
<li><a href="https://github.com/encog/encog-java-core" target="_blank" rel="external">Encog</a> - 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.</li>
<li><a href="https://ci.apache.org/projects/flink/flink-docs-master/apis/batch/libs/ml/index.html" target="_blank" rel="external">FlinkML in Apache Flink</a> - Distributed machine learning library in Flink</li>
<li><a href="https://github.com/h2oai/h2o-3" target="_blank" rel="external">H2O</a> - ML engine that supports distributed learning on Hadoop, Spark or your laptop via APIs in R, Python, Scala, REST/JSON.</li>
<li><a href="https://github.com/numenta/htm.java" target="_blank" rel="external">htm.java</a> - General Machine Learning library using Numenta’s Cortical Learning Algorithm</li>
<li><a href="https://github.com/deeplearning4j/deeplearning4j" target="_blank" rel="external">java-deeplearning</a> - Distributed Deep Learning Platform for Java, Clojure,Scala</li>
<li><a href="https://github.com/apache/mahout" target="_blank" rel="external">Mahout</a> - Distributed machine learning</li>
<li><a href="http://meka.sourceforge.net/" target="_blank" rel="external">Meka</a> - An open source implementation of methods for multi-label classification and evaluation (extension to Weka).</li>
<li><a href="http://spark.apache.org/docs/latest/mllib-guide.html" target="_blank" rel="external">MLlib in Apache Spark</a> - Distributed machine learning library in Spark</li>
<li><a href="https://github.com/Hydrospheredata/mist" target="_blank" rel="external">Hydrosphere Mist</a> - a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.</li>
<li><a href="http://neuroph.sourceforge.net/" target="_blank" rel="external">Neuroph</a> - Neuroph is lightweight Java neural network framework</li>
<li><a href="https://github.com/oryxproject/oryx" target="_blank" rel="external">ORYX</a> - Lambda Architecture Framework using Apache Spark and Apache Kafka with a specialization for real-time large-scale machine learning.</li>
<li><a href="https://samoa.incubator.apache.org/" target="_blank" rel="external">Samoa</a> SAMOA is a framework that includes distributed machine learning for data streams with an interface to plug-in different stream processing platforms.</li>
<li><a href="https://sourceforge.net/p/lemur/wiki/RankLib/" target="_blank" rel="external">RankLib</a> - RankLib is a library of learning to rank algorithms</li>
<li><a href="https://github.com/padreati/rapaio" target="_blank" rel="external">rapaio</a> - statistics, data mining and machine learning toolbox in Java</li>
<li><a href="https://rapidminer.com" target="_blank" rel="external">RapidMiner</a> - RapidMiner integration into Java code</li>
<li><a href="http://nlp.stanford.edu/software/classifier.shtml" target="_blank" rel="external">Stanford Classifier</a> - A classifier is a machine learning tool that will take data items and place them into one of k classes.</li>
<li><a href="https://github.com/haifengl/smile" target="_blank" rel="external">SmileMiner</a> - Statistical Machine Intelligence & Learning Engine</li>
<li><a href="https://github.com/apache/incubator-systemml" target="_blank" rel="external">SystemML</a> - flexible, scalable machine learning (ML) language.</li>
<li><a href="https://github.com/WalnutiQ/wAlnut" target="_blank" rel="external">WalnutiQ</a> - object oriented model of the human brain</li>
<li><a href="http://www.cs.waikato.ac.nz/ml/weka/" target="_blank" rel="external">Weka</a> - Weka is a collection of machine learning algorithms for data mining tasks</li>
<li><a href="https://github.com/IllinoisCogComp/lbjava/" target="_blank" rel="external">LBJava</a> - Learning Based Java is a modeling language for the rapid development of software systems, offers a convenient, declarative syntax for classifier and constraint definition directly in terms of the objects in the programmer’s application.</li>
</ul>
<h4 id="Speech-Recognition-2"><a href="#Speech-Recognition-2" class="headerlink" title="Speech Recognition"></a>Speech Recognition</h4><ul>
<li><a href="http://cmusphinx.sourceforge.net/" target="_blank" rel="external">CMU Sphinx</a> - Open Source Toolkit For Speech Recognition purely based on Java speech recognition library.</li>
</ul>
<p><a name="java-data-analysis"></a></p>
<h4 id="Data-Analysis-Data-Visualization-2"><a href="#Data-Analysis-Data-Visualization-2" class="headerlink" title="Data Analysis / Data Visualization"></a>Data Analysis / Data Visualization</h4><ul>
<li><a href="http://flink.apache.org/" target="_blank" rel="external">Flink</a> - Open source platform for distributed stream and batch data processing.</li>
<li><a href="https://github.com/apache/hadoop-mapreduce" target="_blank" rel="external">Hadoop</a> - Hadoop/HDFS</li>
<li><a href="https://github.com/apache/spark" target="_blank" rel="external">Spark</a> - Spark is a fast and general engine for large-scale data processing.</li>
<li><a href="http://storm.apache.org/" target="_blank" rel="external">Storm</a> - Storm is a distributed realtime computation system.</li>
<li><a href="https://github.com/cloudera/impala" target="_blank" rel="external">Impala</a> - Real-time Query for Hadoop</li>
<li><a href="http://jwork.org/dmelt/" target="_blank" rel="external">DataMelt</a> - Mathematics software for numeric computation, statistics, symbolic calculations, data analysis and data visualization.</li>
<li><a href="http://www.ee.ucl.ac.uk/~mflanaga/java/" target="_blank" rel="external">Dr. Michael Thomas Flanagan’s Java Scientific Library</a></li>
</ul>
<p><a name="java-deep-learning"></a></p>
<h4 id="Deep-Learning"><a href="#Deep-Learning" class="headerlink" title="Deep Learning"></a>Deep Learning</h4><ul>
<li><a href="https://github.com/deeplearning4j/deeplearning4j" target="_blank" rel="external">Deeplearning4j</a> - Scalable deep learning for industry with parallel GPUs</li>
</ul>
<p><a name="javascript"></a></p>
<h2 id="Javascript"><a href="#Javascript" class="headerlink" title="Javascript"></a>Javascript</h2><p><a name="javascript-nlp"></a></p>
<h4 id="Natural-Language-Processing-5"><a href="#Natural-Language-Processing-5" class="headerlink" title="Natural Language Processing"></a>Natural Language Processing</h4><ul>
<li><a href="https://github.com/twitter/twitter-text" target="_blank" rel="external">Twitter-text</a> - A JavaScript implementation of Twitter’s text processing library</li>
<li><a href="https://github.com/NaturalNode/natural" target="_blank" rel="external">natural</a> - General natural language facilities for node</li>
<li><a href="https://github.com/loadfive/Knwl.js" target="_blank" rel="external">Knwl.js</a> - A Natural Language Processor in JS</li>
<li><a href="https://github.com/wooorm/retext" target="_blank" rel="external">Retext</a> - Extensible system for analyzing and manipulating natural language</li>
<li><a href="https://market.mashape.com/japerk/text-processing/support" target="_blank" rel="external">TextProcessing</a> - Sentiment analysis, stemming and lemmatization, part-of-speech tagging and chunking, phrase extraction and named entity recognition.</li>
<li><a href="https://github.com/nlp-compromise/compromise" target="_blank" rel="external">NLP Compromise</a> - Natural Language processing in the browser</li>
</ul>
<p><a name="javascript-data-analysis"></a></p>
<h4 id="Data-Analysis-Data-Visualization-3"><a href="#Data-Analysis-Data-Visualization-3" class="headerlink" title="Data Analysis / Data Visualization"></a>Data Analysis / Data Visualization</h4><ul>
<li><a href="https://d3js.org/" target="_blank" rel="external">D3.js</a></li>
<li><a href="http://www.highcharts.com/" target="_blank" rel="external">High Charts</a></li>
<li><a href="http://nvd3.org/" target="_blank" rel="external">NVD3.js</a></li>
<li><a href="http://dc-js.github.io/dc.js/" target="_blank" rel="external">dc.js</a></li>
<li><a href="http://www.chartjs.org/" target="_blank" rel="external">chartjs</a></li>
<li><a href="http://dimplejs.org/" target="_blank" rel="external">dimple</a></li>
<li><a href="https://www.amcharts.com/" target="_blank" rel="external">amCharts</a></li>
<li><a href="https://github.com/NathanEpstein/D3xter" target="_blank" rel="external">D3xter</a> - Straight forward plotting built on D3</li>
<li><a href="https://github.com/rigtorp/statkit" target="_blank" rel="external">statkit</a> - Statistics kit for JavaScript</li>
<li><a href="https://github.com/nathanepstein/datakit" target="_blank" rel="external">datakit</a> - A lightweight framework for data analysis in JavaScript</li>
<li><a href="https://github.com/jasondavies/science.js/" target="_blank" rel="external">science.js</a> - Scientific and statistical computing in JavaScript.</li>
<li><a href="https://github.com/NathanEpstein/Z3d" target="_blank" rel="external">Z3d</a> - Easily make interactive 3d plots built on Three.js</li>
<li><a href="http://sigmajs.org/" target="_blank" rel="external">Sigma.js</a> - JavaScript library dedicated to graph drawing.</li>
<li><a href="http://c3js.org/" target="_blank" rel="external">C3.js</a>- customizable library based on D3.js for easy chart drawing.</li>
<li><a href="http://datamaps.github.io/" target="_blank" rel="external">Datamaps</a>- Customizable SVG map/geo visualizations using D3.js.</li>
<li><a href="https://www.zingchart.com/" target="_blank" rel="external">ZingChart</a>- library written on Vanilla JS for big data visualization.</li>
<li><a href="http://www.cheminfo.org/" target="_blank" rel="external">cheminfo</a> - Platform for data visualization and analysis, using the <a href="https://github.com/npellet/visualizer" target="_blank" rel="external">visualizer</a> project.</li>
<li><a href="http://learnjsdata.com/" target="_blank" rel="external">Learn JS Data</a></li>
<li><a href="http://www.anychart.com/" target="_blank" rel="external">AnyChart</a></li>
<li><a href="http://www.fusioncharts.com/" target="_blank" rel="external">FusionCharts</a></li>
</ul>
<p><a name="javascript-general-purpose"></a></p>
<h4 id="General-Purpose-Machine-Learning-10"><a href="#General-Purpose-Machine-Learning-10" class="headerlink" title="General-Purpose Machine Learning"></a>General-Purpose Machine Learning</h4><ul>
<li><a href="http://cs.stanford.edu/people/karpathy/convnetjs/" target="_blank" rel="external">Convnet.js</a> - ConvNetJS is a Javascript library for training Deep Learning models[DEEP LEARNING]</li>
<li><a href="http://harthur.github.io/clusterfck/" target="_blank" rel="external">Clusterfck</a> - Agglomerative hierarchical clustering implemented in Javascript for Node.js and the browser</li>
<li><a href="https://github.com/emilbayes/clustering.js" target="_blank" rel="external">Clustering.js</a> - Clustering algorithms implemented in Javascript for Node.js and the browser</li>
<li><a href="https://github.com/serendipious/nodejs-decision-tree-id3" target="_blank" rel="external">Decision Trees</a> - NodeJS Implementation of Decision Tree using ID3 Algorithm</li>
<li><a href="https://github.com/dn2a/dn2a-javascript" target="_blank" rel="external">DN2A</a> - Digital Neural Networks Architecture</li>
<li><a href="https://code.google.com/archive/p/figue" target="_blank" rel="external">figue</a> - K-means, fuzzy c-means and agglomerative clustering</li>
<li><a href="https://github.com/lukapopijac/gaussian-mixture-model" target="_blank" rel="external">Gaussian Mixture Model</a> - Unsupervised machine learning with multivariate Gaussian mixture model</li>
<li><a href="https://github.com/rlidwka/node-fann" target="_blank" rel="external">Node-fann</a> - FANN (Fast Artificial Neural Network Library) bindings for Node.js</li>
<li><a href="https://github.com/emilbayes/kMeans.js" target="_blank" rel="external">Kmeans.js</a> - Simple Javascript implementation of the k-means algorithm, for node.js and the browser</li>
<li><a href="https://github.com/primaryobjects/lda" target="_blank" rel="external">LDA.js</a> - LDA topic modeling for node.js</li>
<li><a href="https://github.com/yandongliu/learningjs" target="_blank" rel="external">Learning.js</a> - Javascript implementation of logistic regression/c4.5 decision tree</li>
<li><a href="http://joonku.com/project/machine_learning" target="_blank" rel="external">Machine Learning</a> - Machine learning library for Node.js</li>
<li><a href="https://github.com/ClimbsRocks/machineJS" target="_blank" rel="external">machineJS</a> - Automated machine learning, data formatting, ensembling, and hyperparameter optimization for competitions and exploration- just give it a .csv file!</li>
<li><a href="https://github.com/mil-tokyo" target="_blank" rel="external">mil-tokyo</a> - List of several machine learning libraries</li>
<li><a href="https://github.com/nicolaspanel/node-svm" target="_blank" rel="external">Node-SVM</a> - Support Vector Machine for nodejs</li>
<li><a href="https://github.com/harthur/brain" target="_blank" rel="external">Brain</a> - Neural networks in JavaScript <strong>[Deprecated]</strong></li>
<li><a href="https://github.com/omphalos/bayesian-bandit.js" target="_blank" rel="external">Bayesian-Bandit</a> - Bayesian bandit implementation for Node and the browser.</li>
<li><a href="https://github.com/cazala/synaptic" target="_blank" rel="external">Synaptic</a> - Architecture-free neural network library for node.js and the browser</li>
<li><a href="https://github.com/NathanEpstein/kNear" target="_blank" rel="external">kNear</a> - JavaScript implementation of the k nearest neighbors algorithm for supervised learning</li>
<li><a href="https://github.com/totemstech/neuraln" target="_blank" rel="external">NeuralN</a> - C++ Neural Network library for Node.js. It has advantage on large dataset and multi-threaded training.</li>
<li><a href="https://github.com/itamarwe/kalman" target="_blank" rel="external">kalman</a> - Kalman filter for Javascript.</li>
<li><a href="https://github.com/luccastera/shaman" target="_blank" rel="external">shaman</a> - node.js library with support for both simple and multiple linear regression.</li>
<li><a href="https://github.com/mljs/ml" target="_blank" rel="external">ml.js</a> - Machine learning and numerical analysis tools for Node.js and the Browser!</li>
<li><a href="https://github.com/NathanEpstein/Pavlov.js" target="_blank" rel="external">Pavlov.js</a> - Reinforcement learning using Markov Decision Processes</li>
<li><a href="https://github.com/dmlc/mxnet" target="_blank" rel="external">MXNet</a> - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.</li>
</ul>
<p><a name="javascript-misc"></a></p>
<h4 id="Misc"><a href="#Misc" class="headerlink" title="Misc"></a>Misc</h4><ul>
<li><a href="https://github.com/jcoglan/sylvester" target="_blank" rel="external">sylvester</a> - Vector and Matrix math for JavaScript.</li>
<li><a href="https://github.com/simple-statistics/simple-statistics" target="_blank" rel="external">simple-statistics</a> - A JavaScript implementation of descriptive, regression, and inference statistics. Implemented in literate JavaScript with no dependencies, designed to work in all modern browsers (including IE) as well as in node.js.</li>
<li><a href="https://github.com/Tom-Alexander/regression-js" target="_blank" rel="external">regression-js</a> - A javascript library containing a collection of least squares fitting methods for finding a trend in a set of data.</li>
<li><a href="https://github.com/flurry/Lyric" target="_blank" rel="external">Lyric</a> - Linear Regression library.</li>
<li><a href="https://github.com/mwgg/GreatCircle" target="_blank" rel="external">GreatCircle</a> - Library for calculating great circle distance.</li>
</ul>
<p><a name="julia"></a></p>
<h2 id="Julia"><a href="#Julia" class="headerlink" title="Julia"></a>Julia</h2><p><a name="julia-general-purpose"></a></p>
<h4 id="General-Purpose-Machine-Learning-11"><a href="#General-Purpose-Machine-Learning-11" class="headerlink" title="General-Purpose Machine Learning"></a>General-Purpose Machine Learning</h4><ul>
<li><a href="https://github.com/benhamner/MachineLearning.jl" target="_blank" rel="external">MachineLearning</a> - Julia Machine Learning library</li>
<li><a href="https://github.com/JuliaStats/MLBase.jl" target="_blank" rel="external">MLBase</a> - A set of functions to support the development of machine learning algorithms</li>
<li><a href="https://github.com/JuliaStats/PGM.jl" target="_blank" rel="external">PGM</a> - A Julia framework for probabilistic graphical models.</li>
<li><a href="https://github.com/trthatcher/DiscriminantAnalysis.jl" target="_blank" rel="external">DA</a> - Julia package for Regularized Discriminant Analysis</li>
<li><a href="https://github.com/lindahua/Regression.jl" target="_blank" rel="external">Regression</a> - Algorithms for regression analysis (e.g. linear regression and logistic regression)</li>
<li><a href="https://github.com/JuliaStats/Loess.jl" target="_blank" rel="external">Local Regression</a> - Local regression, so smooooth!</li>
<li><a href="https://github.com/nutsiepully/NaiveBayes.jl" target="_blank" rel="external">Naive Bayes</a> - Simple Naive Bayes implementation in Julia</li>
<li><a href="https://github.com/dmbates/MixedModels.jl" target="_blank" rel="external">Mixed Models</a> - A Julia package for fitting (statistical) mixed-effects models</li>
<li><a href="https://github.com/fredo-dedup/SimpleMCMC.jl" target="_blank" rel="external">Simple MCMC</a> - basic mcmc sampler implemented in Julia</li>
<li><a href="https://github.com/JuliaStats/Distance.jl" target="_blank" rel="external">Distance</a> - Julia module for Distance evaluation</li>
<li><a href="https://github.com/bensadeghi/DecisionTree.jl" target="_blank" rel="external">Decision Tree</a> - Decision Tree Classifier and Regressor</li>
<li><a href="https://github.com/compressed/BackpropNeuralNet.jl" target="_blank" rel="external">Neural</a> - A neural network in Julia</li>
<li><a href="https://github.com/doobwa/MCMC.jl" target="_blank" rel="external">MCMC</a> - MCMC tools for Julia</li>
<li><a href="https://github.com/brian-j-smith/Mamba.jl" target="_blank" rel="external">Mamba</a> - Markov chain Monte Carlo (MCMC) for Bayesian analysis in Julia</li>
<li><a href="https://github.com/JuliaStats/GLM.jl" target="_blank" rel="external">GLM</a> - Generalized linear models in Julia</li>
<li><a href="https://github.com/lendle/OnlineLearning.jl" target="_blank" rel="external">Online Learning</a></li>
<li><a href="https://github.com/simonster/GLMNet.jl" target="_blank" rel="external">GLMNet</a> - Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet</li>
<li><a href="https://github.com/JuliaStats/Clustering.jl" target="_blank" rel="external">Clustering</a> - Basic functions for clustering data: k-means, dp-means, etc.</li>
<li><a href="https://github.com/JuliaStats/SVM.jl" target="_blank" rel="external">SVM</a> - SVM’s for Julia</li>
<li><a href="https://github.com/JuliaStats/KernelDensity.jl" target="_blank" rel="external">Kernal Density</a> - Kernel density estimators for julia</li>
<li><a href="https://github.com/JuliaStats/DimensionalityReduction.jl" target="_blank" rel="external">Dimensionality Reduction</a> - Methods for dimensionality reduction</li>
<li><a href="https://github.com/JuliaStats/NMF.jl" target="_blank" rel="external">NMF</a> - A Julia package for non-negative matrix factorization</li>
<li><a href="https://github.com/EricChiang/ANN.jl" target="_blank" rel="external">ANN</a> - Julia artificial neural networks</li>
<li><a href="https://github.com/pluskid/Mocha.jl" target="_blank" rel="external">Mocha</a> - Deep Learning framework for Julia inspired by Caffe</li>
<li><a href="https://github.com/dmlc/XGBoost.jl" target="_blank" rel="external">XGBoost</a> - eXtreme Gradient Boosting Package in Julia</li>
<li><a href="https://github.com/wildart/ManifoldLearning.jl" target="_blank" rel="external">ManifoldLearning</a> - A Julia package for manifold learning and nonlinear dimensionality reduction</li>
<li><a href="https://github.com/dmlc/mxnet" target="_blank" rel="external">MXNet</a> - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.</li>
<li><a href="https://github.com/hshindo/Merlin.jl" target="_blank" rel="external">Merlin</a> - Flexible Deep Learning Framework in Julia</li>
<li><a href="https://github.com/davidavdav/ROCAnalysis.jl" target="_blank" rel="external">ROCAnalysis</a> - Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers</li>
<li><a href="https://github.com/davidavdav/GaussianMixtures.jl" target="_blank" rel="external">GaussianMixtures</a> - Large scale Gaussian Mixture Models</li>
<li><a href="https://github.com/cstjean/ScikitLearn.jl" target="_blank" rel="external">ScikitLearn</a> - Julia implementation of the scikit-learn API</li>