General-Purpose Machine Learning

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General-Purpose Machine Learning

• Awesome Machine Learning with Ruby - Curated list of ML related resources for Ruby.
• Awesome NLP with Ruby - Curated link list for practical natural language processing in Ruby.
• Raspel - raspell is an interface binding for ruby.
• ruby-plot - gnuplot wrapper for Ruby, especially for plotting ROC curves into SVG files.
• SciRuby
• scruffy - A beautiful graphing toolkit for Ruby.
• rb-libsvm - Ruby language bindings for LIBSVM which is a Library for Support Vector Machines.
• Treat - Text REtrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby.
• Bioruby
• Listof - Community based data collection, packed in gem. Get list of pretty much anything (stop words, countries, non words) in txt, json or hash. [Demo/Search for a list](http://kevincobain2000.github.io/listof/)
• CardMagic-Classifier - A general classifier module to allow Bayesian and other types of classifications.
• plot-rb - A plotting library in Ruby built on top of Vega and D3.
• data-visualization-ruby - Source code and supporting content for my Ruby Manor presentation on Data Visualisation with Ruby.
• Arel
• jRuby Mahout - JRuby Mahout is a gem that unleashes the power of Apache Mahout in the world of JRuby.
• Stemmer - Expose libstemmer_c to Ruby.
• UEA Stemmer - Ruby port of UEALite Stemmer - a conservative stemmer for search and indexing.
• Ruby Machine Learning - Some Machine Learning algorithms, implemented in Ruby.
• Machine Learning Ruby
• rsruby - Ruby - R bridge.
• SciRuby
• Glean - A data management tool for humans.
• Big Data For Chimps
• Twitter-text-rb - A library that does auto linking and extraction of usernames, lists and hashtags in tweets.
• scruffy - A beautiful graphing toolkit for Ruby.
• Twitter-text-rb - A library that does auto linking and extraction of usernames, lists and hashtags in tweets.
• Ruby Linguistics - Linguistics is a framework for building linguistic utilities for Ruby objects in any language. It includes a generic language-independent front end, a module for mapping language codes into language names, and a module which contains various English-language utilities.
• Random Forester - Creates Random Forest classifiers from PMML files.

General-Purpose Machine Learning

• Awesome Core ML Models - A curated list of machine learning models in CoreML format.
• Swift Brain - The first neural network / machine learning library written in Swift. This is a project for AI algorithms in Swift for iOS and OS X development. This project includes algorithms focused on Bayes theorem, neural networks, SVMs, Matrices, etc...
• AIToolbox - A toolbox framework of AI modules written in Swift: Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians.
• Bender - Fast Neural Networks framework built on top of Metal. Supports TensorFlow models.
• DeepLearningKit
• swix - A bare bones library that
• MLKit - A simple Machine Learning Framework written in Swift. Currently features Simple Linear Regression, Polynomial Regression, and Ridge Regression.
• PredictionBuilder - A library for machine learning that builds predictions using a linear regression.
• Perfect TensorFlow - Swift Language Bindings of TensorFlow. Using native TensorFlow models on both macOS / Linux.
• BrainCore - The iOS and OS X neural network framework.

General-Purpose Machine Learning

• Awesome TensorFlow - A list of all things related to TensorFlow.
• Golden TensorFlow - A page of content on TensorFlow, including academic papers and links to related topics.

Speech Recognition

• Twitter-text - A JavaScript implementation of Twitter's text processing library.
• dimple
• amCharts
• D3.js
• ZingChart - library written on Vanilla JS for big data visualization.
• Learn JS Data
• figue - K-means, fuzzy c-means and agglomerative clustering.
• mil-tokyo - List of several machine learning libraries.
• Datamaps - Customizable SVG map/geo visualizations using D3.js.
• Machine Learning - Machine learning library for Node.js
• Machine Learning - Machine learning library for Node.js
• Machine Learning - Machine learning library for Node.js
• Machine Learning - Machine learning library for Node.js
• natural - General natural language facilities for node.
• Keras.js - Run Keras models in the browser, with GPU support provided by WebGL 2.
• Pavlov.js - Reinforcement learning using Markov Decision Processes.
• ml.js - Machine learning and numerical analysis tools for Node.js and the Browser!
• simple-statistics - 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.
• nlp.js - An NLP library built in node over Natural, with entity extraction, sentiment analysis, automatic language identify, and so more
• regression-js - A javascript library containing a collection of least squares fitting methods for finding a trend in a set of data.
• Synaptic - Architecture-free neural network library for Node.js and the browser.
• Brain - Neural networks in JavaScript **[Deprecated]**
• stdlib - A standard library for JavaScript and Node.js, with an emphasis on numeric computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
• D3xter - Straight forward plotting built on D3.
• shaman - Node.js library with support for both simple and multiple linear regression.
• LDA.js - LDA topic modeling for Node.js
• NeuralN - C++ Neural Network library for Node.js. It has advantage on large dataset and multi-threaded training.
• machineJS - Automated machine learning, data formatting, ensembling, and hyperparameter optimization for competitions and exploration- just give it a .csv file!
• sylvester - Vector and Matrix math for JavaScript.
• Learning.js - Javascript implementation of logistic regression/c4.5 decision tree
• Node-fann - FANN (Fast Artificial Neural Network Library) bindings for Node.js
• Clustering.js - Clustering algorithms implemented in Javascript for Node.js and the browser.
• statkit - Statistics kit for JavaScript.
• Node-SVM - Support Vector Machine for Node.js
• Bayesian-Bandit - Bayesian bandit implementation for Node and the browser.
• Knwl.js - A Natural Language Processor in JS.
• NLP Compromise - Natural Language processing in the browser.
• xgboost-node - Run XGBoost model and make predictions in Node.js.
• dimple
• datakit - A lightweight framework for data analysis in JavaScript
• Z3d - Easily make interactive 3d plots built on Three.js
• Learn JS Data
• Gaussian Mixture Model - Unsupervised machine learning with multivariate Gaussian mixture model.
• Kmeans.js - Simple Javascript implementation of the k-means algorithm, for node.js and the browser.
• kNear - JavaScript implementation of the k nearest neighbors algorithm for supervised learning.
• kalman - Kalman filter for Javascript.
• JSMLT - Machine learning toolkit with classification and clustering for Node.js; supports visualization (see [visualml.io](https://visualml.io)).
• Lyric - Linear Regression library.
• GreatCircle - Library for calculating great circle distance.
• MLPleaseHelp - MLPleaseHelp is a simple ML resource search engine. You can use this search engine right now at [https://jgreenemi.github.io/MLPleaseHelp/](https://jgreenemi.github.io/MLPleaseHelp/), provided via Github Pages.
• The Bot - Example of how the neural network learns to predict the angle between two points created with [Synaptic](https://github.com/cazala/synaptic).
• Half Beer - Beer glass classifier created with [Synaptic](https://github.com/cazala/synaptic).
• dc.js
• TensorFlow.js - A WebGL accelerated, browser based JavaScript library for training and deploying ML models.
• Clusterfck - Agglomerative hierarchical clustering implemented in Javascript for Node.js and the browser.
• TensorFlow.js - A WebGL accelerated, browser based JavaScript library for training and deploying ML models.
• DN2A - Digital Neural Networks Architecture.
• Decision Trees - NodeJS Implementation of Decision Tree using ID3 Algorithm.
• Retext - Extensible system for analyzing and manipulating natural language.
• High Charts
• science.js - Scientific and statistical computing in JavaScript.
• AnyChart
• NVD3.js
• Nivo - built on top of the awesome d3 and Reactjs libraries
• Machine Learning - Machine learning library for Node.js

Speech Recognition

• Emgu CV - Cross platform wrapper of OpenCV which can be compiled in Mono to be run on Windows, Linus, Mac OS X, iOS, and Android.
• Accord-Framework - The Accord.NET Framework is a complete framework for building machine learning, computer vision, computer audition, signal processing and statistical applications.
• OpenCVDotNet - A wrapper for the OpenCV project to be used with .NET applications.
• Neural Network Designer - DBMS management system and designer for neural networks. The designer application is developed using WPF, and is a user interface which allows you to design your neural network, query the network, create and configure chat bots that are capable of asking questions and learning from your feed back. The chat bots can even scrape the internet for information to return in their output as well as to use for learning.
• Infer.NET - Infer.NET is a framework for running Bayesian inference in graphical models. One can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification, recommendation or clustering through to customised solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others.
• Sho - Sho is an interactive environment for data analysis and scientific computing that lets you seamlessly connect scripts (in IronPython) with compiled code (in .NET) to enable fast and flexible prototyping. The environment includes powerful and efficient libraries for linear algebra as well as data visualization that can be used from any .NET language, as well as a feature-rich interactive shell for rapid development.
• ML.NET - ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers. ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.
• GeneticSharp - Multi-platform genetic algorithm library for .NET Core and .NET Framework. The library has several implementations of GA operators, like: selection, crossover, mutation, reinsertion and termination.
• Emgu CV - Cross platform wrapper of OpenCV which can be compiled in Mono to be run on Windows, Linus, Mac OS X, iOS, and Android.
• Accord-Framework - The Accord.NET Framework is a complete framework for building machine learning, computer vision, computer audition, signal processing and statistical applications.
• Vulpes - Deep belief and deep learning implementation written in F# and leverages CUDA GPU execution with Alea.cuBase.
• DiffSharp - An automatic differentiation (AD) library providing exact and efficient derivatives (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products) for machine learning and optimization applications. Operations can be nested to any level, meaning that you can compute exact higher-order derivatives and differentiate functions that are internally making use of differentiation, for applications such as hyperparameter optimization.
• numl - numl is a machine learning library intended to ease the use of using standard modeling techniques for both prediction and clustering.
• Vulpes - Deep belief and deep learning implementation written in F# and leverages CUDA GPU execution with Alea.cuBase.
• Stanford.NLP for .NET - A full port of Stanford NLP packages to .NET and also available precompiled as a NuGet package.

General-Purpose Machine Learning

• Pandas - A library providing high-performance, easy-to-use data structures and data analysis tools.
• Numba - Python JIT (just in time) complier to LLVM aimed at scientific Python by the developers of Cython and NumPy.
• PyDy - Short for Python Dynamics, used to assist with workflow in the modeling of dynamic motion based around NumPy, SciPy, IPython, and matplotlib.
• A gallery of interesting IPython notebooks
• SimpleCV - An open source computer vision framework that gives access to several high-powered computer vision libraries, such as OpenCV. Written on Python and runs on Mac, Windows, and Ubuntu Linux.
• Edward - A library for probabilistic modeling, inference, and criticism. Built on top of TensorFlow.
• cerebro2 - based visualization and debugging platform for NuPIC.
• astroML - Machine Learning and Data Mining for Astronomy.
• Seaborn - A python visualization library based on matplotlib.
• Python Programming for the Humanities - Course for Python programming for the Humanities, assuming no prior knowledge. Heavy focus on text processing / NLP.
• Practical XGBoost in Python - comprehensive online course about using XGBoost in Python.
• deap - Evolutionary algorithm framework.
• Bayesian Methods for Hackers - Book/iPython notebooks on Probabilistic Programming in Python.
• zipline - A Pythonic algorithmic trading library.
• Scikit-Image - A collection of algorithms for image processing in Python.
• PyMC - Markov Chain Monte Carlo sampling toolkit.
• skflow - Simplified interface for TensorFlow, mimicking Scikit Learn.
• Brainstorm - Fast, flexible and fun neural networks. This is the successor of PyBrain.
• ggplot - Same API as ggplot2 for R.
• Blaze - NumPy and Pandas interface to Big Data.
• data-science-ipython-notebooks - Continually updated Data Science Python Notebooks: Spark, Hadoop MapReduce, HDFS, AWS, Kaggle, scikit-learn, matplotlib, pandas, NumPy, SciPy, and various command lines.
• neon - Nervana's [high-performance](https://github.com/soumith/convnet-benchmarks) Python-based Deep Learning framework [DEEP LEARNING].
• keras - High-level neural networks frontend for [TensorFlow](https://github.com/tensorflow/tensorflow), [CNTK](https://github.com/Microsoft/CNTK) and [Theano](https://github.com/Theano/Theano).
• Lasagne - Lightweight library to build and train neural networks in Theano.
• pydeep - Deep Learning In Python.
• bokeh - Interactive Web Plotting for Python.
• hebel - GPU-Accelerated Deep Learning Library in Python.
• jieba - Chinese Words Segmentation Utilities.
• Pylearn2 - A Machine Learning library based on [Theano](https://github.com/Theano/Theano).
• SymPy - A Python library for symbolic mathematics.
• Introduction to Machine Learning with Python - Notebooks and code for the book "Introduction to Machine Learning with Python"
• Pydata book - Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
• DIGITS - The Deep Learning GPU Training System (DIGITS) is a web application for training deep learning models.
• Gym - OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms.
• PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
• Dash - A framework for creating analytical web applications built on top of Plotly.js, React, and Flask
• visualize_ML - A python package for data exploration and data analysis.
• scikit-plot - A visualization library for quick and easy generation of common plots in data analysis and machine learning.
• TDB - TensorDebugger (TDB) is a visual debugger for deep learning. It features interactive, node-by-node debugging and visualization for TensorFlow.
• pastalog - Simple, realtime visualization of neural network training performance.
• jellyfish - a python library for doing approximate and phonetic matching of strings.
• Annoy - Approximate nearest neighbours implementation.
• DrQA - Reading Wikipedia to answer open-domain questions.
• ML-From-Scratch - Implementations of Machine Learning models from scratch in Python with a focus on transparency. Aims to showcase the nuts and bolts of ML in an accessible way.
• vincent - A Python to Vega translator.
• mlxtend - A library consisting of useful tools for data science and machine learning tasks.
• SKLL - A wrapper around scikit-learn that makes it simpler to conduct experiments.
• statsmodels - Statistical modeling and econometrics in Python.
• pgmpy
• PyQtGraph - A pure-python graphics and GUI library built on PyQt4 / PySide and NumPy.
• Fuzzy Wuzzy - Fuzzy String Matching in Python.
• Open Mining - Business Intelligence (BI) in Python (Pandas web interface)
• Dedupe - A python library for accurate and scaleable fuzzy matching, record deduplication and entity-resolution.
• Turi Create - Machine learning from Apple. Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.
• lime - Lime is about explaining what machine learning classifiers (or models) are doing. It is able to explain any black box classifier, with two or more classes.
• PCV - Open source Python module for computer vision.
• Spearmint - Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012.
• Neural Networks and Deep Learning - Code samples for my book "Neural Networks and Deep Learning" [DEEP LEARNING].
• face_recognition - Face recognition library that recognize and manipulate faces from Python or from the command line.
• TFLearn - Deep learning library featuring a higher-level API for TensorFlow.
• pattern - Web mining module for Python.
• Bowtie - A dashboard library for interactive visualizations using flask socketio and react.
• Snips NLU - Natural Language Understanding library for intent classification and entity extraction
• Serpent.AI - Serpent.AI is a game agent framework that allows you to turn any video game you own into a sandbox to develop AI and machine learning experiments. For both researchers and hobbyists.
• pattern_classification
• SOMPY - Self Organizing Map written in Python (Uses neural networks for data analysis).
• textacy - higher-level NLP built on Spacy.
• Rosetta - Text processing tools and wrappers (e.g. Vowpal Wabbit)
• PyNLPl - Python Natural Language Processing Library. General purpose NLP library for Python. Also contains some specific modules for parsing common NLP formats, most notably for [FoLiA](http://proycon.github.io/folia/), but also ARPA language models, Moses phrasetables, GIZA++ alignments.
• sentiment_classifier - Sentiment classifier using word sense disambiguation.
• jProcessing - Kanji / Hiragana / Katakana to Romaji Converter. Edict Dictionary & parallel sentences Search. Sentence Similarity between two JP Sentences. Sentiment Analysis of Japanese Text. Run Cabocha(ISO--8859-1 configured) in Python.
• SnowNLP - A library for processing Chinese text.
• skbayes - Python package for Bayesian Machine Learning with scikit-learn API.
• nilearn - Machine learning for NeuroImaging in Python.
• pandas cookbook - Recipes for using Python's pandas library.
• Polyglot - Multilingual text (NLP) processing toolkit.
• Crab - A recommendation engine library for Python.
• Introduction to machine learning with scikit-learn - IPython notebooks from Data School's video tutorials on scikit-learn.
• SLM Lab - Modular Deep Reinforcement Learning framework in PyTorch.
• Retro - Retro Games in Gym
• Roboschool - Open-source software for robot simulation, integrated with OpenAI Gym.
• Detectron - FAIR's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.
• Universe - Universe is a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications.
• vispy - GPU-based high-performance interactive OpenGL 2D/3D data visualization library.
• mlens - A high performance, memory efficient, maximally parallelized ensemble learning, integrated with scikit-learn.
• Allen Downey’s Think OS Code - Text and supporting code for Think OS: A Brief Introduction to Operating Systems.
• dockerface - Easy to install and use deep learning Faster R-CNN face detection for images and video in a docker container.
• auto_ml - Automated machine learning for production and analytics. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and results. Includes support for NLP, XGBoost, CatBoost, LightGBM, and soon, deep learning.
• hyperopt
• CLTK - The Classical Language Toolkit.
• Quepy - A python framework to transform natural language questions to queries in a database query language.
• SimpleAI
• neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful.
• pyhsmm - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.
• neurolab - https://github.com/zueve/neurolab
• fuku-ml - Simple machine learning library, including Perceptron, Regression, Support Vector Machine, Decision Tree and more, it's easy to use and easy to learn for beginners.
• stacked_generalization - Implementation of machine learning stacking technic as handy library in Python.
• PyCM - PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters
• python-timbl - A Python extension module wrapping the full TiMBL C++ programming interface. Timbl is an elaborate k-Nearest Neighbours machine learning toolkit.
• YAlign - A sentence aligner, a friendly tool for extracting parallel sentences from comparable corpora.
• NeuralTalk - NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.
• REP - an IPython-based environment for conducting data-driven research in a consistent and reproducible way. REP is not trying to substitute scikit-learn, but extends it and provides better user experience.
• emcee - The Python ensemble sampling toolkit for affine-invariant MCMC.
• PyBrain - Another Python Machine Learning Library.
• Cogitare
• Xcessiv - A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling.
• stanford-corenlp-python - Python wrapper for [Stanford CoreNLP](https://github.com/stanfordnlp/CoreNLP)
• d3py - A plotting library for Python, based on [D3.js](https://d3js.org/).
• SVM Explorer - Interactive SVM Explorer, using Dash and scikit-learn
• OpenFace - Free and open source face recognition with deep neural networks.
• Petrel - Tools for writing, submitting, debugging, and monitoring Storm topologies in pure Python.
• genius - A Chinese segment base on Conditional Random Field.
• ggfortify - Unified interface to ggplot2 popular R packages.
• somoclu - organizing maps: accelerate training on multicore CPUs, GPUs, and clusters, has python API.
• python-recsys - A Python library for implementing a Recommender System.
• Crab - A flexible, fast recommender engine.
• milk - Machine learning toolkit focused on supervised classification.
• Neon Course - IPython notebooks for a complete course around understanding Nervana's Neon.
• ipython-notebooks
• Parris - Parris, the automated infrastructure setup tool for machine learning algorithms.
• PyDexter - Simple plotting for Python. Wrapper for D3xterjs; easily render charts in-browser.
• SparklingPandas
• kaggle_acquire-valued-shoppers-challenge - Code for the Kaggle acquire valued shoppers challenge.
• Featureforge - learn compatible API.
• Kartograph.py - Rendering beautiful SVG maps in Python.
• NuPIC Studio - in-one NuPIC Hierarchical Temporal Memory visualization and debugging super-tool!
• machine learning - automated build consisting of a [web-interface](https://github.com/jeff1evesque/machine-learning#web-interface), and set of [programmatic-interface](https://github.com/jeff1evesque/machine-learning#programmatic-interface) API, for support vector machines. Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL datastore.
• group-lasso - Some experiments with the coordinate descent algorithm used in the (Sparse) Group Lasso model.
• python-frog - Python binding to Frog, an NLP suite for Dutch. (pos tagging, lemmatisation, dependency parsing, NER)
• python-ucto - Python binding to ucto (a unicode-aware rule-based tokenizer for various languages).
• Kaggle Dogs vs. Cats - Code for Kaggle Dogs vs. Cats competition.
• Data Driven Code - Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.
• Kaggle Galaxy Challenge - Winning solution for the Galaxy Challenge on Kaggle.
• wiki challenge - An implementation of Dell Zhang's solution to Wikipedia's Participation Challenge on Kaggle.
• nut - Natural language Understanding Toolkit.
• Bolt - Bolt Online Learning Toolbox.
• sentiment-analyzer - Tweets Sentiment Analyzer
• Dive into Machine Learning with Python Jupyter notebook and scikit-learn - "I learned Python by hacking first, and getting serious *later.* I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself."
• gensim - Topic Modelling for Humans.
• DeepMind Lab - DeepMind Lab is a 3D learning environment based on id Software's Quake III Arena via ioquake3 and other open source software. Its primary purpose is to act as a testbed for research in artificial intelligence, especially deep reinforcement learning.
• Distance - Levenshtein and Hamming distance computation.
• Chainer - Flexible neural network framework.
• steppy - > Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design.
• ViZDoom - ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular.
• scikit-learn - A Python module for machine learning built on top of SciPy.
• SimpleCV - An open source computer vision framework that gives access to several high-powered computer vision libraries, such as OpenCV. Written on Python and runs on Mac, Windows, and Ubuntu Linux.
• python-zpar - Python bindings for [ZPar](https://github.com/frcchang/zpar), a statistical part-of-speech-tagger, constiuency parser, and dependency parser for English.
• PyStanfordDependencies - Python interface for converting Penn Treebank trees to Stanford Dependencies.
• yase - Transcode sentence (or other sequence) to list of word vector .
• topik - Topic modelling toolkit.
• thinking bayes - Book on Bayesian Analysis.
• Image-to-Image Translation with Conditional Adversarial Networks - Implementation of image to image (pix2pix) translation from the paper by [isola et al](https://arxiv.org/pdf/1611.07004.pdf).[DEEP LEARNING]
• Restricted Boltzmann Machines - Restricted Boltzmann Machines in Python. [DEEP LEARNING]
• CoverTree - Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree
• Pyevolve - Genetic algorithm framework.
• breze - Theano based library for deep and recurrent neural networks.
• Pebl - Python Environment for Bayesian Learning.
• xRBM - A library for Restricted Boltzmann Machine (RBM) and its conditional variants in Tensorflow.
• pycascading
• Dora - Tools for exploratory data analysis in Python.
• Ruffus - Computation Pipeline library for python.
• HDBScan - implementation of the hdbscan algorithm in Python - used for clustering
• Map/Reduce implementations of common ML algorithms - means, alternating least squares), using Python NumPy, and how to then make these implementations scalable using Map/Reduce and Spark.
• BioPy - Biologically-Inspired and Machine Learning Algorithms in Python.
• thinking stats 2
• 2012-paper-diginorm
• decision-weights
• Sarah Palin LDA - Topic Modeling the Sarah Palin emails.
• Diffusion Segmentation - A collection of image segmentation algorithms based on diffusion methods.
• Scipy Tutorials - SciPy tutorials. This is outdated, check out scipy-lecture-notes.
• BayesPy - Bayesian Inference Tools in Python.
• scikit-learn tutorials - Series of notebooks for learning scikit-learn.
• mne-python-notebooks - IPython notebooks for EEG/MEG data processing using mne-python.
• climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others.
• Allen Downey’s Data Science Course - Code for Data Science at Olin College, Spring 2014.
• Allen Downey’s Think Complexity Code - Code for Allen Downey's book Think Complexity.
• Neuron - Neuron is simple class for time series predictions. It's utilize LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neural networks learned with Gradient descent or LeLevenberg–Marquardt algorithm.
• open-solution-home-credit - > source code and [experiments results](https://app.neptune.ml/neptune-ml/Home-Credit-Default-Risk) for [Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk).
• open-solution-googleai-object-detection - > source code and [experiments results](https://app.neptune.ml/neptune-ml/Google-AI-Object-Detection-Challenge) for [Google AI Open Images - Object Detection Track](https://www.kaggle.com/c/google-ai-open-images-object-detection-track).
• open-solution-salt-identification - > source code and [experiments results](https://app.neptune.ml/neptune-ml/Salt-Detection) for [TGS Salt Identification Challenge](https://www.kaggle.com/c/tgs-salt-identification-challenge).
• open-solution-ship-detection - > source code and [experiments results](https://app.neptune.ml/neptune-ml/Ships) for [Airbus Ship Detection Challenge](https://www.kaggle.com/c/airbus-ship-detection).
• open-solution-data-science-bowl-2018 - > source code and [experiments results](https://app.neptune.ml/neptune-ml/Data-Science-Bowl-2018) for [2018 Data Science Bowl](https://www.kaggle.com/c/data-science-bowl-2018).
• open-solution-value-prediction - > source code and [experiments results](https://app.neptune.ml/neptune-ml/Santander-Value-Prediction-Challenge) for [Santander Value Prediction Challenge](https://www.kaggle.com/c/santander-value-prediction-challenge).
• kaggle insults - Kaggle Submission for "Detecting Insults in Social Commentary".
• kaggle-cifar - Code for the CIFAR-10 competition at Kaggle, uses cuda-convnet.
• kaggle-blackbox - Deep learning made easy.
• kaggle-accelerometer - Code for Accelerometer Biometric Competition at Kaggle.
• kaggle-advertised-salaries - Predicting job salaries from ads - a Kaggle competition.
• kaggle amazon - Amazon access control challenge.
• kaggle-bestbuy_big - Code for the Best Buy competition at Kaggle.
• kaggle-bestbuy_small
• Kaggle Gender - A Kaggle competition: discriminate gender based on handwriting.
• Kaggle Merck - Merck challenge at Kaggle.
• Kaggle Stackoverflow - Predicting closed questions on Stack Overflow.
• wine-quality - Predicting wine quality.
• editdistance - fast implementation of edit distance.
• matplotlib - A Python 2D plotting library.
• NumPy - A fundamental package for scientific computing with Python.
• Surprise - A scikit for building and analyzing recommender systems.
• graphlab-create - A library with various machine learning models (regression, clustering, recommender systems, graph analytics, etc.) implemented on top of a disk-backed DataFrame.
• rgf_python - Python bindings for Regularized Greedy Forest (Tree) Library.
• NLTK - A leading platform for building Python programs to work with human language data.
• TextBlob - Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of NLTK and Pattern, and plays nicely with both.
• pygal - A Python SVG Charts Creator.
• Pebl - Python Environment for Bayesian Learning.
• albumentations - А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops.
• spaCy - Industrial strength NLP with Python and Cython.
• yahmm - Hidden Markov Models for Python, implemented in Cython for speed and efficiency.
• caravel - A data exploration platform designed to be visual, intuitive, and interactive.
• steppy-toolkit - > Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.
• Theano - Optimizing GPU-meta-programming code generating array oriented optimizing math compiler in Python.
• TensorFlow - Open source software library for numerical computation using data flow graphs.
• pycascading
• Pattern - A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
• NuPIC - Numenta Platform for Intelligent Computing.
• bqplot - An API for plotting in Jupyter (IPython).
• Suiron - Machine Learning for RC Cars.
• igraph - binding to igraph library - General purpose graph library.
• astropy - A community Python library for Astronomy.
• metric-learn - A Python module for metric learning.
• open-solution-home-credit - > source code and [experiments results](https://app.neptune.ml/neptune-ml/Home-Credit-Default-Risk) for [Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk).
• open-solution-googleai-object-detection - > source code and [experiments results](https://app.neptune.ml/neptune-ml/Google-AI-Object-Detection-Challenge) for [Google AI Open Images - Object Detection Track](https://www.kaggle.com/c/google-ai-open-images-object-detection-track).
• open-solution-ship-detection - > source code and [experiments results](https://app.neptune.ml/neptune-ml/Ships) for [Airbus Ship Detection Challenge](https://www.kaggle.com/c/airbus-ship-detection).
• open-solution-data-science-bowl-2018 - > source code and [experiments results](https://app.neptune.ml/neptune-ml/Data-Science-Bowl-2018) for [2018 Data Science Bowl](https://www.kaggle.com/c/data-science-bowl-2018).
• open-solution-value-prediction - > source code and [experiments results](https://app.neptune.ml/neptune-ml/Santander-Value-Prediction-Challenge) for [Santander Value Prediction Challenge](https://www.kaggle.com/c/santander-value-prediction-challenge).
• open-solution-toxic-comments - > source code for [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge).
• loso - Another Chinese segmentation library.
• windML - A Python Framework for Wind Energy Analysis and Prediction.

Speech Recognition

• PyCUDA - Python interface to CUDA
• sofia-ml - Suite of fast incremental algorithms.
• DLib - DLib has C++ and Python interfaces for face detection and training general object detectors.
• Distributed Machine learning Tool Kit (DMTK) - 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.
• DLib - A suite of ML tools designed to be easy to imbed in other applications.
• shark - A fast, modular, feature-rich open-source C++ machine learning library.
• CRFsuite - CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data.
• encog-cpp
• libfm - A generic approach that allows to mimic most factorization models by feature engineering.
• CatBoost - General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, contains fast inference implementation and supports CPU and GPU (even multi-GPU) computation.
• xLearn - A high performance, easy-to-use, and scalable machine learning package, which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data, which is very common in Internet services such as online advertisement and recommender systems.
• DeepDetect - 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.
• Warp-CTC - A fast parallel implementation of Connectionist Temporal Classification (CTC), on both CPU and GPU.
• MeTA - [MeTA : ModErn Text Analysis](https://meta-toolkit.org/) is a C++ Data Sciences Toolkit that facilitates mining big text data.
• Fido - A highly-modular C++ machine learning library for embedded electronics and robotics.
• VIGRA - VIGRA is a generic cross-platform C++ computer vision and machine learning library for volumes of arbitrary dimensionality with Python bindings.
• Kaldi - 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.
• Shogun - The Shogun Machine Learning Toolbox.
• MIT Information Extraction Toolkit - C, C++, and Python tools for named entity recognition and relation extraction
• DyNet - 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.
• XGBoost - A parallelized optimized general purpose gradient boosting library.
• CRF++ - Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks.
• BLLIP Parser - BLLIP Natural Language Parser (also known as the Charniak-Johnson parser).
• colibri-core - 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.
• ucto - Unicode-aware regular-expression based tokenizer for various languages. Tool and C++ library. Supports FoLiA format.
• libfolia - C++ library for the [FoLiA format](http://proycon.github.io/folia/)
• frog - Memory-based NLP suite developed for Dutch: PoS tagger, lemmatiser, dependency parser, NER, shallow parser, morphological analyzer.
• BanditLib - A simple Multi-armed Bandit library.
• CNTK - 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.
• LightGBM - 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.
• Timbl - 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.
• proNet-core - A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
• LKYDeepNN - A header-only C++11 Neural Network library. Low dependency, native traditional chinese document.
• CRFsuite - CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data.
• ToPS - This is an objected-oriented framework that facilitates the integration of probabilistic models for sequences over a user defined alphabet.
• grt - The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition.
• OpenCV - OpenCV has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS.
• libfm - A generic approach that allows to mimic most factorization models by feature engineering.
• CUDA - This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING]
• Vowpal Wabbit (VW) - A fast out-of-core learning system.
• EBLearn - Eblearn is an object-oriented C++ library that implements various machine learning models
• CXXNET - Yet another deep learning framework with less than 1000 lines core code [DEEP LEARNING]
• Featuretools - A library for automated feature engineering. It excels at transforming transactional and relational datasets into feature matrices for machine learning using reusable feature engineering "primitives".
• DLib - A suite of ML tools designed to be easy to imbed in other applications.
• DSSTNE - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility.
• Stan - A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling.

Speech Recognition

• Torch7
• Numeric Lua
• Lua - Numerical Algorithms
• Music Tagging - Music Tagging scripts for torch7.
• signal - A signal processing toolbox for Torch-7. FFT, DCT, Hilbert, cepstrums, stft.
• Lunatic Python
• Numeric Lua
• autograd - Autograd automatically differentiates native Torch code. Inspired by the original Python version.
• rnn - A Recurrent Neural Network library that extends Torch's nn. RNNs, LSTMs, GRUs, BRNNs, BLSTMs, etc.
• dp - A deep learning library designed for streamlining research and development using the Torch7 distribution. It emphasizes flexibility through the elegant use of object-oriented design patterns.
• cutorch - Torch CUDA Implementation.
• cunn - Torch CUDA Neural Network Implementation.
• nn - Neural Network package for Torch.
• dpnn - Many useful features that aren't part of the main nn package.
• Training a Convnet for the Galaxy-Zoo Kaggle challenge(CUDA demo)
• torch-datasets - Scripts to load several popular datasets including:
• graph - Graph package for Torch.
• optim - An optimization library for Torch. SGD, Adagrad, Conjugate-Gradient, LBFGS, RProp and more.
• nngraph - This package provides graphical computation for nn library in Torch7.
• OverFeat - A state-of-the-art generic dense feature extractor.
• nnx - A completely unstable and experimental package that extends Torch's builtin nn library.
• unsup - A package for unsupervised learning in Torch. Provides modules that are compatible with nn (LinearPsd, ConvPsd, AutoEncoder, ...), and self-contained algorithms (k-means, PCA).
• manifold - A package to manipulate manifolds.
• svm - Torch-SVM library.
• lbfgs - FFI Wrapper for liblbfgs.
• vowpalwabbit - An old vowpalwabbit interface to torch.
• OpenGM - OpenGM is a C++ library for graphical modeling, and inference. The Lua bindings provide a simple way of describing graphs, from Lua, and then optimizing them with OpenGM.
• sphagetti - Spaghetti (sparse linear) module for torch7 by @MichaelMathieu
• LuaSHKit - A lua wrapper around the Locality sensitive hashing library SHKit
• kernel smoothing - KNN, kernel-weighted average, local linear regression smoothers.
• imgraph - An image/graph library for Torch. This package provides routines to construct graphs on images, segment them, build trees out of them, and convert them back to images.
• videograph - A video/graph library for Torch. This package provides routines to construct graphs on videos, segment them, build trees out of them, and convert them back to videos.
• saliency - code and tools around integral images. A library for finding interest points based on fast integral histograms.
• stitch - allows us to use hugin to stitch images and apply same stitching to a video sequence.
• sfm - A bundle adjustment/structure from motion package.
• fex - A package for feature extraction in Torch. Provides SIFT and dSIFT modules.
• Numeric Lua
• Lunum
• Core torch7 demos repository
• Atari2600 - Scripts to generate a dataset with static frames from the Arcade Learning Environment.
• signal - A signal processing toolbox for Torch-7. FFT, DCT, Hilbert, cepstrums, stft.
• torchnet - framework for torch which provides a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming.
• wav2letter - a simple and efficient end-to-end Automatic Speech Recognition (ASR) system from Facebook AI Research.
• cephes - Cephes mathematical functions library, wrapped for Torch. Provides and wraps the 180+ special mathematical functions from the Cephes mathematical library, developed by Stephen L. Moshier. It is used, among many other places, at the heart of SciPy.
• randomkit - Numpy's randomkit, wrapped for Torch.
• autograd - Autograd automatically differentiates native Torch code. Inspired by the original Python version.

General-Purpose Machine Learning

• ahaz - ahaz: Regularization for semiparametric additive hazards regression.
• arules - arules: Mining Association Rules and Frequent Itemsets
• bigrf - bigrf: Big Random Forests: Classification and Regression Forests for Large Data Sets.
• bigRR - bigRR: Generalized Ridge Regression (with special advantage for p >> n cases).
• bmrm - bmrm: Bundle Methods for Regularized Risk Minimization Package.
• Boruta - Boruta: A wrapper algorithm for all-relevant feature selection.
• bst - bst: Gradient Boosting.
• C50 - C50: C5.0 Decision Trees and Rule-Based Models.
• CORElearn - CORElearn: Classification, regression, feature evaluation and ordinal evaluation.
• CoxBoost - CoxBoost: Cox models by likelihood based boosting for a single survival endpoint or competing risks
• Cubist - Cubist: Rule- and Instance-Based Regression Modeling.
• e1071 - e1071: Misc Functions of the Department of Statistics (e1071), TU Wien
• earth - earth: Multivariate Adaptive Regression Spline Models
• elasticnet - elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA.
• ElemStatLearn - ElemStatLearn: Data sets, functions and examples from the book: "The Elements of Statistical Learning, Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani and Jerome Friedman Prediction" by Trevor Hastie, Robert Tibshirani and Jerome Friedman.
• evtree - evtree: Evolutionary Learning of Globally Optimal Trees.
• frbs - frbs: Fuzzy Rule-based Systems for Classification and Regression Tasks.
• GAMBoost - GAMBoost: Generalized linear and additive models by likelihood based boosting.
• gamboostLSS - gamboostLSS: Boosting Methods for GAMLSS.
• gbm - gbm: Generalized Boosted Regression Models.
• glmnet - glmnet: Lasso and elastic-net regularized generalized linear models.
• glmpath - glmpath: L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model.
• GMMBoost - GMMBoost: Likelihood-based Boosting for Generalized mixed models.
• grplasso - grplasso: Fitting user specified models with Group Lasso penalty.
• grpreg - grpreg: Regularization paths for regression models with grouped covariates.
• h2o - A framework for fast, parallel, and distributed machine learning algorithms at scale -- Deeplearning, Random forests, GBM, KMeans, PCA, GLM.
• hda - hda: Heteroscedastic Discriminant Analysis.
• ipred - ipred: Improved Predictors.
• kernlab - kernlab: Kernel-based Machine Learning Lab.
• klaR - klaR: Classification and visualization.
• lars - lars: Least Angle Regression, Lasso and Forward Stagewise.
• lasso2 - lasso2: L1 constrained estimation aka ‘lasso’.
• LiblineaR - LiblineaR: Linear Predictive Models Based On The Liblinear C/C++ Library.
• LogicReg - LogicReg: Logic Regression.
• maptree - maptree: Mapping, pruning, and graphing tree models.
• mboost - mboost: Model-Based Boosting.
• mvpart - mvpart: Multivariate partitioning.
• ncvreg - ncvreg: Regularization paths for SCAD- and MCP-penalized regression models.
• nnet - nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models.
• oblique.tree - oblique.tree: Oblique Trees for Classification Data.
• pamr - pamr: Pam: prediction analysis for microarrays.
• party - party: A Laboratory for Recursive Partytioning.
• partykit - partykit: A Toolkit for Recursive Partytioning.
• penalized - penalized: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model.
• penalizedLDA - penalizedLDA: Penalized classification using Fisher's linear discriminant.
• penalizedSVM - penalizedSVM: Feature Selection SVM using penalty functions.
• quantregForest - quantregForest: Quantile Regression Forests.
• randomForest - randomForest: Breiman and Cutler's random forests for classification and regression.
• randomForestSRC - randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC).
• rattle - rattle: Graphical user interface for data mining in R.
• rda - rda: Shrunken Centroids Regularized Discriminant Analysis.
• rdetools - rdetools: Relevant Dimension Estimation (RDE) in Feature Spaces.
• REEMtree - REEMtree: Regression Trees with Random Effects for Longitudinal (Panel) Data.
• relaxo - relaxo: Relaxed Lasso.
• rgenoud - rgenoud: R version of GENetic Optimization Using Derivatives
• rgp - rgp: R genetic programming framework.
• Rmalschains - Rmalschains: Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R.
• rminer - rminer: Simpler use of data mining methods (e.g. NN and SVM) in classification and regression.
• ROCR - ROCR: Visualizing the performance of scoring classifiers.
• RoughSets - RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories.
• rpart - rpart: Recursive Partitioning and Regression Trees.
• RPMM - RPMM: Recursively Partitioned Mixture Model.
• RSNNS - RSNNS: Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS).
• RWeka - RWeka: R/Weka interface.
• RXshrink - RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression.
• sda - sda: Shrinkage Discriminant Analysis and CAT Score Variable Selection.
• SDDA - SDDA: Stepwise Diagonal Discriminant Analysis.
• svmpath - svmpath: svmpath: the SVM Path algorithm.
• tgp - tgp: Bayesian treed Gaussian process models.
• tree - tree: Classification and regression trees.
• varSelRF - varSelRF: Variable selection using random forests.
• XGBoost.R - R binding for eXtreme Gradient Boosting (Tree) Library.
• biglasso - biglasso: Extending Lasso Model Fitting to Big Data in R.
• ggplot2 - A data visualization package based on the grammar of graphics.
• caret - Classification and Regression Training: Unified interface to ~150 ML algorithms in R.
• Introduction to Statistical Learning
• caretEnsemble - caretEnsemble: Framework for fitting multiple caret models as well as creating ensembles of such models.
• fpc - fpc: Flexible procedures for clustering.
• mlr - mlr: Machine Learning in R.
• forecast - forecast: Timeseries forecasting using ARIMA, ETS, STLM, TBATS, and neural network models.
• forecastHybrid - forecastHybrid: Automatic ensemble and cross validation of ARIMA, ETS, STLM, TBATS, and neural network models from the "forecast" package.
• Machine Learning For Hackers
• SuperLearner - project.org/web/packages/subsemble/index.html) - Multi-algorithm ensemble learning packages.
• TDSP-Utilities - Two data science utilities in R from Microsoft: 1) Interactive Data Exploration, Analysis, and Reporting (IDEAR) ; 2) Automated Modeling and Reporting (AMR).
• bigrf - bigrf: Big Random Forests: Classification and Regression Forests for Large Data Sets.
• bigRR - bigRR: Generalized Ridge Regression (with special advantage for p >> n cases).
• caret - Classification and Regression Training: Unified interface to ~150 ML algorithms in R.
• mvpart - mvpart: Multivariate partitioning.
• oblique.tree - oblique.tree: Oblique Trees for Classification Data.
• rgp - rgp: R genetic programming framework.
• SDDA - SDDA: Stepwise Diagonal Discriminant Analysis.
• Clever Algorithms For Machine Learning

Speech Recognition

• OpenNLP - a machine learning based toolkit for the processing of natural language text.
• AMIDST Toolbox - A Java Toolbox for Scalable Probabilistic Machine Learning.
• ELKI - Java toolkit for data mining. (unsupervised: clustering, outlier detection etc.)
• RankLib - RankLib is a library of learning to rank algorithms.
• WalnutiQ - object oriented model of the human brain.
• Weka - Weka is a collection of machine learning algorithms for data mining tasks.
• Stanford Topic Modeling Toolbox - Topic modeling tools to social scientists and others who wish to perform analysis on datasets.
• IRIS - [Cortical.io's](http://cortical.io) FREE NLP, Retina API Analysis Tool (written in JavaFX!) - [See the Tutorial Video](https://www.youtube.com/watch?v=CsF4pd7fGF0).
• ClearTK - ClearTK provides a framework for developing statistical natural language processing (NLP) components in Java and is built on top of Apache UIMA.
• ORYX - Lambda Architecture Framework using Apache Spark and Apache Kafka with a specialization for real-time large-scale machine learning.
• SmileMiner - Statistical Machine Intelligence & Learning Engine.
• Spark - Spark is a fast and general engine for large-scale data processing.
• java-deeplearning - Distributed Deep Learning Platform for Java, Clojure, Scala.
• H2O - ML engine that supports distributed learning on Hadoop, Spark or your laptop via APIs in R, Python, Scala, REST/JSON.
• aerosolve - A machine learning library by Airbnb designed from the ground up to be human friendly.
• Onyx - Distributed, masterless, high performance, fault tolerant data processing. Written entirely in Clojure.
• Hydrosphere Mist - a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
• Mahout - Distributed machine learning.
• htm.java - General Machine Learning library using Numenta’s Cortical Learning Algorithm.
• Hadoop - Hadoop/HDFS.
• Datumbox - Machine Learning framework for rapid development of Machine Learning and Statistical applications.
• Impala - Real-time Query for Hadoop.
• LingPipe - A tool kit for processing text using computational linguistics.
• AMIDST Toolbox - A Java Toolbox for Scalable Probabilistic Machine Learning.
• rapaio - statistics, data mining and machine learning toolbox in Java.
• Twitter Text Java - A Java implementation of Twitter's text processing library.
• ClearNLP - 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.
• CogcompNLP - This project collects a number of core libraries for Natural Language Processing (NLP) developed in the University of Illinois' Cognitive Computation Group, for example `illinois-core-utilities` which provides a set of NLP-friendly data structures and a number of NLP-related utilities that support writing NLP applications, running experiments, etc, `illinois-edison` a library for feature extraction from illinois-core-utilities data structures and many other packages.
• CMU Sphinx - Open Source Toolkit For Speech Recognition purely based on Java speech recognition library.
• Weka - Weka is a collection of machine learning algorithms for data mining tasks.
• FlinkML in Apache Flink - Distributed machine learning library in Flink.
• Stanford Classifier - A classifier is a machine learning tool that will take data items and place them into one of k classes.
• Stanford POS Tagger - A Part-Of-Speech Tagger (POS Tagger).
• CoreNLP - 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.
• Twitter Text Java - A Java implementation of Twitter's text processing library.
• Meka - An open source implementation of methods for multi-label classification and evaluation (extension to Weka).
• Neuroph - Neuroph is lightweight Java neural network framework
• SystemML - flexible, scalable machine learning (ML) language.
• Storm - Storm is a distributed realtime computation system.

Speech Recognition

• Incanter - Incanter is a Clojure-based, R-like platform for statistical computing and graphics.
• PigPen - Map-Reduce for Clojure.
• Clojure-openNLP - Natural Language Processing in Clojure (opennlp).
• Infections-clj - Rails-like inflection library for Clojure and ClojureScript.
• Clojush - The Push programming language and the PushGP genetic programming system implemented in Clojure.
• Infer - Inference and machine learning in Clojure.
• lambda-ml - Simple, concise implementations of machine learning techniques and utilities in Clojure.
• Encog - Clojure wrapper for Encog (v3) (Machine-Learning framework that specializes in neural-nets).
• Fungp - A genetic programming library for Clojure.
• comportex - Functionally composable Machine Learning library using Numenta’s Cortical Learning Algorithm.
• Statistiker - Basic Machine Learning algorithms in Clojure.
• clortex - General Machine Learning library using Numenta’s Cortical Learning Algorithm.
• Incanter - Incanter is a Clojure-based, R-like platform for statistical computing and graphics.
• Envision - Clojure Data Visualisation library, based on Statistiker and D3.
• Clj-ML - A machine learning library for Clojure built on top of Weka and friends.
• Touchstone - Clojure A/B testing library.
• DL4CLJ - Clojure wrapper for Deeplearning4j.

Speech Recognition

• Contourlets - MATLAB source code that implements the contourlet transform and its utility functions.
• Curvelets - The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles.
• Bandlets - MATLAB code for bandlet transform.
• matlab_gbl - MatlabBGL is a Matlab package for working with graphs.
• t-Distributed Stochastic Neighbor Embedding - 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 - The spider is intended to be a complete object orientated environment for machine learning in Matlab.
• Shearlets - MATLAB code for shearlet transform.
• Pattern Recognition and Machine Learning - This package contains the matlab implementation of the algorithms described in the book Pattern Recognition and Machine Learning by C. Bishop.
• Machine Learning Module - Class on machine w/ PDF, lectures, code
• Curvelets - The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles.
• Pattern Recognition Toolbox - A complete object-oriented environment for machine learning in Matlab.
• Shearlets - MATLAB code for shearlet transform.
• mexopencv - Collection and a development kit of MATLAB mex functions for OpenCV library.
• gamic - Efficient pure-Matlab implementations of graph algorithms to complement MatlabBGL's mex functions.
• Curvelets - The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles.
• ThunderSVM - An Open-Source SVM Library on GPUs and CPUs
• LibSVM - A Library for Support Vector Machines.
• MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
• LibLinear - A Library for Large Linear Classification.

Data Analysis / Data Visualization

• Perl Data Language

General-Purpose Machine Learning

• Support Vector Machines
• Naïve Bayes
• Naïve Bayes

General-Purpose Machine Learning

• released in CPAN
• Algorithm::SVMLight
• Naïve Bayes

Data Analysis / Data Visualization

• used for machine learning

General-Purpose Machine Learning

• Enterprise Miner - Data mining and machine learning that creates deployable models using a GUI or code.
• ML_Tables - Concise cheat sheets containing machine learning best practices.
• High Performance Text Mining - Text mining using a GUI or code in an MPP environment, including Hadoop.
• Factory Miner - Automatically creates deployable machine learning models across numerous market or customer segments using a GUI.
• enlighten-integration - Example code and materials that illustrate techniques for integrating SAS with other analytics technologies in Java, PMML, Python and R.
• enlighten-apply - Example code and materials that illustrate applications of SAS machine learning techniques.
• enlighten-deep - Example code and materials that illustrate using neural networks with several hidden layers in SAS.
• Factory Miner - Automatically creates deployable machine learning models across numerous market or customer segments using a GUI.
• Text Miner - Text mining using a GUI or code.
• Factory Miner - Automatically creates deployable machine learning models across numerous market or customer segments using a GUI.
• High Performance Text Mining - Text mining using a GUI or code in an MPP environment, including Hadoop.
• Visual Data Mining and Machine Learning - Interactive, automated, and programmatic modeling with the latest machine learning algorithms in and end-to-end analytics environment, from data prep to deployment. Free trial available.
• SAS/STAT - For conducting advanced statistical analysis.
• High Performance Data Mining - Data mining and machine learning that creates deployable models using a GUI or code in an MPP environment, including Hadoop.
• Contextual Analysis - Add structure to unstructured text using a GUI.
• Sentiment Analysis - Extract sentiment from text using a GUI.
• Text Miner - Text mining using a GUI or code.
• University Edition - FREE! Includes all SAS packages necessary for data analysis and visualization, and includes online SAS courses.

General-Purpose Machine Learning

• ScalaNLP - ScalaNLP is a suite of machine learning and numerical computing libraries.
• DeepLearning.scala - Creating statically typed dynamic neural networks from object-oriented & functional programming constructs.
• Scalding - A Scala API for Cascading.
• Summing Bird - Streaming MapReduce with Scalding and Storm.
• Algebird - Abstract Algebra for Scala.
• H2O Sparkling Water - H2O and Spark interoperability.
• BIDMach - CPU and GPU-accelerated Machine Learning Library.
• bioscala - Bioinformatics for the Scala programming language
• adam - A genomics processing engine and specialized file format built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.
• Breeze - Breeze is a numerical processing library for Scala.
• doddle-model - An in-memory machine learning library built on top of Breeze. It provides immutable objects and exposes its functionality through a scikit-learn-like API.
• ganitha - Scalding powered machine learning.
• xerial - Data management utilities for Scala.
• PredictionIO - PredictionIO, a machine learning server for software developers and data engineers.
• ScalaNLP - ScalaNLP is a suite of machine learning and numerical computing libraries.
• Chalk - Chalk is a natural language processing library.
• FACTORIE - FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.
• Montague - Montague is a semantic parsing library for Scala with an easy-to-use DSL.
• BIDMat - CPU and GPU-accelerated matrix library intended to support large-scale exploratory data analysis.
• Spark Notebook - Interactive and Reactive Data Science using Scala and Spark.
• Conjecture - Scalable Machine Learning in Scalding.
• brushfire - Distributed decision tree ensemble learning in Scala.
• Figaro - a Scala library for constructing probabilistic models.
• DynaML - Scala Library/REPL for Machine Learning Research.

Speech Recognition

• SVGo - The Go Language library for SVG generation.
• paicehusk - Golang implementation of the Paice/Husk Stemming Algorithm.
• go-mxnet-predictor - Go binding for MXNet c_predict_api to do inference with pre-trained model.
• bayesian - Naive Bayesian Classification for Golang.
• Cloudforest - Ensembles of decision trees in Go/Golang.
• eaopt - An evolutionary optimization library.
• go-galib - Genetic Algorithms library written in Go / Golang.
• go-pr - Pattern recognition package in Go lang.
• gobrain - Neural Networks written in Go.
• Go Learn - Machine Learning for Go.
• neat - Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT).
• sentences - Golang implementation of Punkt sentence tokenizer.
• GoNN - GoNN is an implementation of Neural Network in Go Language, which includes BPNN, RBF, PCN.
• go-porterstemmer - A native Go clean room implementation of the Porter Stemming algorithm.
• snowball - Snowball Stemmer for Go.
• go-ngram - In-memory n-gram index with compression.
• go-ml - Linear / Logistic regression, Neural Networks, Collaborative Filtering and Gaussian Multivariate Distribution.
• go-graph - Graph library for Go/Golang language.
• RF - Random forests implementation in Go.
• word-embedding - Word Embeddings: the full implementation of word2vec, GloVe in Go.
• SVGo - The Go Language library for SVG generation.
• MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
• Glot - Glot is a plotting library for Golang built on top of gnuplot.

General-Purpose Machine Learning

• PHP-ML - Machine Learning library for PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library.
• PredictionBuilder - A library for machine learning that builds predictions using a linear regression.
• 19 Questions - A machine learning / bayesian inference assigning attributes to objects.

Natural Language Processing

• jieba-php - Chinese Words Segmentation Utilities.

Speech Recognition

• hnn - Haskell Neural Network library.
• HLearn - a suite of libraries for interpreting machine learning models according to their algebraic structure.
• LambdaNet - Configurable Neural Networks in Haskell.
• caffegraph - A DSL for deep neural networks.
• hopfield-networks - Hopfield Networks for unsupervised learning in Haskell.
• haskell-ml - Haskell implementations of various ML algorithms.

• Darknet - 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.
• Recommender - A C library for product recommendations/suggestions using collaborative filtering (CF).
• Hybrid Recommender System - A hybrid recommender system based upon scikit-learn algorithms.
• CCV - C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library.
• neonrvm - neonrvm is an open source machine learning library based on RVM technique. It's written in C programming language and comes with Python programming language bindings.
• VLFeat - VLFeat is an open and portable library of computer vision algorithms, which has Matlab toolbox.

Speech Recognition

• HTK - The Hidden Markov Model Toolkit (HTK) is a portable toolkit for building and manipulating hidden Markov models.

Speech Recognition

• machine - Simple machine learning algorithm.

Speech Recognition

• Clustering - Basic functions for clustering data: k-means, dp-means, etc.
• SVM - SVM's for Julia.
• ScikitLearn - Julia implementation of the scikit-learn API.
• Mocha - Deep Learning framework for Julia inspired by Caffe.
• Gadfly - Crafty statistical graphics for Julia.
• GLM - Generalized linear models in Julia.
• Topic Models - TopicModels for Julia.
• Time Series - Time series toolkit for Julia.
• DSP - Digital Signal Processing (filtering, periodograms, spectrograms, window functions).
• Local Regression - Local regression, so smooooth!.
• Kernel Density - Kernel density estimators for julia.
• Gaussian Processes - Julia package for Gaussian processes.
• Knet - Koç University Deep Learning Framework.
• Distributions - A Julia package for probability distributions and associated functions.
• Merlin - Flexible Deep Learning Framework in Julia.
• MachineLearning - Julia Machine Learning library.
• MLBase - A set of functions to support the development of machine learning algorithms.
• PGM - A Julia framework for probabilistic graphical models.
• DA - Julia package for Regularized Discriminant Analysis.
• Regression - Algorithms for regression analysis (e.g. linear regression and logistic regression).
• Naive Bayes - Simple Naive Bayes implementation in Julia.
• Simple MCMC - basic mcmc sampler implemented in Julia.
• Distance - Julia module for Distance evaluation.
• Decision Tree - Decision Tree Classifier and Regressor.
• Neural - A neural network in Julia.
• MCMC - MCMC tools for Julia.
• Mamba - Markov chain Monte Carlo (MCMC) for Bayesian analysis in Julia.
• Online Learning
• Dimensionality Reduction - Methods for dimensionality reduction.
• NMF - A Julia package for non-negative matrix factorization.
• ANN - Julia artificial neural networks.
• XGBoost - eXtreme Gradient Boosting Package in Julia.
• ManifoldLearning - A Julia package for manifold learning and nonlinear dimensionality reduction.
• ROCAnalysis - Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers.
• GaussianMixtures - Large scale Gaussian Mixture Models.
• Graph Layout - Graph layout algorithms in pure Julia.
• Julia Data - library for working with tabular data in Julia.
• Hypothesis Tests - Hypothesis tests for Julia.
• Data Arrays - Data structures that allow missing values.
• Sampling - Basic sampling algorithms for Julia.
• JuliaCon Presentations - Presentations for JuliaCon.
• SignalProcessing - Signal Processing tools for Julia.
• Images - An image library for Julia.
• Text Analysis - Julia package for text analysis.
• LightGraphs - Graph modeling and analysis.
• Data Frames Meta - Metaprogramming tools for DataFrames.
• Data Read - Read files from Stata, SAS, and SPSS.
• Stats - Statistical tests for Julia.
• RDataSets - Julia package for loading many of the data sets available in R.
• DataFrames - library for working with tabular data in Julia.
• Mixed Models - A Julia package for fitting (statistical) mixed-effects models.
• GLMNet - Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet.

General-Purpose Machine Learning

• rusty-machine - a pure-rust machine learning library.
• RusticSOM - A Rust library for Self Organising Maps (SOM).
• leaf - open source framework for machine intelligence, sharing concepts from TensorFlow and Caffe. Available under the MIT license. [**[Deprecated]**](https://medium.com/@mjhirn/tensorflow-wins-89b78b29aafb#.s0a3uy4cc)
• rustlearn - a machine learning framework featuring logistic regression, support vector machines, decision trees and random forests.
• deeplearn-rs - deeplearn-rs provides simple networks that use matrix multiplication, addition, and ReLU under the MIT license.
• RustNN - RustNN is a feedforward neural network library.

Speech Recognition

• Simple Bayes - A Simple Bayes / Naive Bayes implementation in Elixir.
• Stemmer - An English (Porter2) stemming implementation in Elixir.

General-Purpose Machine Learning

• TensorFlow - OCaml bindings for TensorFlow.
• GPR - Efficient Gaussian Process Regression in OCaml.

Speech Recognition

• cl-online-learning - Online learning algorithms (Perceptron, AROW, SCW, Logistic Regression).
• cl-random-forest - Implementation of Random Forest in Common Lisp.
• mgl - Neural networks (boltzmann machines, feed-forward and recurrent nets), Gaussian Processes.
• mgl-gpr - Evolutionary algorithms.
• cl-libsvm - Wrapper for the libsvm support vector machine library.

General-Purpose Machine Learning

• YCML - A Machine Learning framework for Objective-C and Swift (OS X / iOS).
• MLPNeuralNet - Fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples by trained neural network. It is built on top of the Apple's Accelerate Framework, using vectorized operations and hardware acceleration if available.
• MAChineLearning - An Objective-C multilayer perceptron library, with full support for training through backpropagation. Implemented using vDSP and vecLib, it's 20 times faster than its Java equivalent. Includes sample code for use from Swift.
• BPN-NeuralNetwork - It implemented 3 layers neural network ( Input Layer, Hidden Layer and Output Layer ) and it named Back Propagation Neural Network (BPN). This network can be used in products recommendation, user behavior analysis, data mining and data analysis.
• Multi-Perceptron-NeuralNetwork - it implemented multi-perceptrons neural network (ニューラルネットワーク) based on Back Propagation Neural Network (BPN) and designed unlimited-hidden-layers.
• KRHebbian-Algorithm - It is a non-supervisor and self-learning algorithm (adjust the weights) in neural network of Machine Learning.
• KRKmeans-Algorithm - It implemented K-Means the clustering and classification algorithm. It could be used in data mining and image compression.
• KRFuzzyCMeans-Algorithm - It implemented Fuzzy C-Means (FCM) the fuzzy clustering / classification algorithm on Machine Learning. It could be used in data mining and image compression.

Speech Recognition

• Disco - Map Reduce in Erlang.