awesome-machine-learning 0,13,47,49,0,54,36,0

A curated list of awesome Machine Learning frameworks, libraries and software.

Awesome Machine Learning Awesome

A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php.

If you want to contribute to this list (please do), send me a pull request or contact me @josephmisiti Also, a listed repository should be deprecated if:

  • Repository’s owner explicitly say that “this library is not maintained”.
  • Not committed for long time (2~3 years).

For a list of free machine learning books available for download, go here.

For a list of free-to-attend meetups and local events, go here.

Table of Contents

APL

General-Purpose Machine Learning

  • naive-apl - Naive Bayesian Classifier implementation in APL

C

General-Purpose Machine Learning

  • 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.

Computer Vision

  • CCV - C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library
  • 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.

C++

Computer Vision

  • DLib - DLib has C++ and Python interfaces for face detection and training general object detectors.
  • EBLearn - Eblearn is an object-oriented C++ library that implements various machine learning models
  • OpenCV - OpenCV has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS.
  • VIGRA - VIGRA is a generic cross-platform C++ computer vision and machine learning library for volumes of arbitrary dimensionality with Python bindings.

General-Purpose Machine Learning

  • BanditLib - A simple Multi-armed Bandit library.
  • Caffe - A deep learning framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING]
  • 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.
  • CUDA - This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING]
  • CXXNET - Yet another deep learning framework with less than 1000 lines core code [DEEP LEARNING]
  • 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.
  • 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
  • DSSTNE - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility.
  • 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.
  • encog-cpp
  • Fido - A highly-modular C++ machine learning library for embedded electronics and robotics.
  • igraph - General purpose graph library
  • Intel® DAAL - 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.
  • 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.
  • MLDB - 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.
  • mlpack - A scalable C++ machine learning library
  • ROOT - A modular scientific software framework. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualization and storage.
  • shark - A fast, modular, feature-rich open-source C++ machine learning library.
  • Shogun - The Shogun Machine Learning Toolbox
  • sofia-ml - Suite of fast incremental algorithms.
  • Stan - A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling
  • 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.
  • Vowpal Wabbit (VW) - A fast out-of-core learning system.
  • Warp-CTC - A fast parallel implementation of Connectionist Temporal Classification (CTC), on both CPU and GPU.
  • XGBoost - A parallelized optimized general purpose gradient boosting library.

Natural Language Processing

  • 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.
  • CRF++ - Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks.
  • CRFsuite - CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data.
  • frog - Memory-based NLP suite developed for Dutch: PoS tagger, lemmatiser, dependency parser, NER, shallow parser, morphological analyzer.
  • libfolia - C++ library for the FoLiA format
  • MeTA - MeTA : ModErn Text Analysis is a C++ Data Sciences Toolkit that facilitates mining big text data.
  • MIT Information Extraction Toolkit - C, C++, and Python tools for named entity recognition and relation extraction
  • ucto - Unicode-aware regular-expression based tokenizer for various languages. Tool and C++ library. Supports FoLiA format.

Speech Recognition

  • 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.

Sequence Analysis

  • ToPS - This is an objected-oriented framework that facilitates the integration of probabilistic models for sequences over a user defined alphabet.

Gesture Detection

  • grt - The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition.

Common Lisp

General-Purpose Machine Learning

  • 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

Clojure

Natural Language Processing

  • Clojure-openNLP - Natural Language Processing in Clojure (opennlp)
  • Infections-clj - Rails-like inflection library for Clojure and ClojureScript

General-Purpose Machine Learning

  • Touchstone - Clojure A/B testing library
  • Clojush - The Push programming language and the PushGP genetic programming system implemented in Clojure
  • Infer - Inference and machine learning in Clojure
  • Clj-ML - A machine learning library for Clojure built on top of Weka and friends
  • DL4CLJ - Clojure wrapper for Deeplearning4j
  • Encog - Clojure wrapper for Encog (v3) (Machine-Learning framework that specializes in neural-nets)
  • Fungp - A genetic programming library for Clojure
  • Statistiker - Basic Machine Learning algorithms in Clojure.
  • clortex - General Machine Learning library using Numenta’s Cortical Learning Algorithm
  • comportex - Functionally composable Machine Learning library using Numenta’s Cortical Learning Algorithm
  • cortex - Neural networks, regression and feature learning in Clojure.
  • lambda-ml - Simple, concise implementations of machine learning techniques and utilities in Clojure.

Data Analysis / Data Visualization

  • Incanter - Incanter is a Clojure-based, R-like platform for statistical computing and graphics.
  • PigPen - Map-Reduce for Clojure.
  • Envision - Clojure Data Visualisation library, based on Statistiker and D3

Elixir

General-Purpose Machine Learning

  • Simple Bayes - A Simple Bayes / Naive Bayes implementation in Elixir.

Natural Language Processing

  • Stemmer - An English (Porter2) stemming implementation in Elixir.

Erlang

General-Purpose Machine Learning

  • Disco - Map Reduce in Erlang

Go

Natural Language Processing

  • go-porterstemmer - A native Go clean room implementation of the Porter Stemming algorithm.
  • paicehusk - Golang implementation of the Paice/Husk Stemming Algorithm.
  • snowball - Snowball Stemmer for Go.
  • go-ngram - In-memory n-gram index with compression.
  • word-embedding - Word Embeddings: the full implementation of word2vec, GloVe in Go.

General-Purpose Machine Learning

  • gago - Multi-population, flexible, parallel genetic algorithm.
  • Go Learn - Machine Learning for Go
  • go-pr - Pattern recognition package in Go lang.
  • go-ml - Linear / Logistic regression, Neural Networks, Collaborative Filtering and Gaussian Multivariate Distribution
  • bayesian - Naive Bayesian Classification for Golang.
  • go-galib - Genetic Algorithms library written in Go / Golang
  • Cloudforest - Ensembles of decision trees in go/Golang.
  • gobrain - Neural Networks written in go
  • GoNN - GoNN is an implementation of Neural Network in Go Language, which includes BPNN, RBF, PCN
  • MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
  • go-mxnet-predictor - Go binding for MXNet c_predict_api to do inference with pre-trained model
  • neat - Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT)

Data Analysis / Data Visualization

  • go-graph - Graph library for Go/Golang language.
  • SVGo - The Go Language library for SVG generation
  • RF - Random forests implementation in Go

Haskell

General-Purpose Machine Learning

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

Java

Natural Language Processing

  • Cortical.io - Retina: an API performing complex NLP operations (disambiguation, classification, streaming text filtering, etc…) as quickly and intuitively as the brain.
  • 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
  • Stanford Parser - A natural language parser is a program that works out the grammatical structure of sentences
  • Stanford POS Tagger - A Part-Of-Speech Tagger (POS Tagger
  • Stanford Name Entity Recognizer - Stanford NER is a Java implementation of a Named Entity Recognizer.
  • Stanford Word Segmenter - Tokenization of raw text is a standard pre-processing step for many NLP tasks.
  • Tregex, Tsurgeon and Semgrex - Tregex is a utility for matching patterns in trees, based on tree relationships and regular expression matches on nodes (the name is short for “tree regular expressions”).
  • Stanford Phrasal: A Phrase-Based Translation System
  • Stanford English Tokenizer - Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java.
  • Stanford Tokens Regex - A tokenizer divides text into a sequence of tokens, which roughly correspond to “words”
  • Stanford Temporal Tagger - SUTime is a library for recognizing and normalizing time expressions.
  • Stanford SPIED - Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion
  • Stanford Topic Modeling Toolbox - Topic modeling tools to social scientists and others who wish to perform analysis on datasets
  • Twitter Text Java - A Java implementation of Twitter’s text processing library
  • MALLET - A Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
  • OpenNLP - a machine learning based toolkit for the processing of natural language text.
  • LingPipe - A tool kit for processing text using computational linguistics.
  • ClearTK - ClearTK provides a framework for developing statistical natural language processing (NLP) components in Java and is built on top of Apache UIMA.
  • Apache cTAKES - 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.
  • 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.

General-Purpose Machine Learning

  • aerosolve - A machine learning library by Airbnb designed from the ground up to be human friendly.
  • AMIDST Toolbox - A Java Toolbox for Scalable Probabilistic Machine Learning.
  • Datumbox - Machine Learning framework for rapid development of Machine Learning and Statistical applications
  • ELKI - Java toolkit for data mining. (unsupervised: clustering, outlier detection etc.)
  • Encog - 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.
  • FlinkML in Apache Flink - Distributed machine learning library in Flink
  • H2O - ML engine that supports distributed learning on Hadoop, Spark or your laptop via APIs in R, Python, Scala, REST/JSON.
  • htm.java - General Machine Learning library using Numenta’s Cortical Learning Algorithm
  • java-deeplearning - Distributed Deep Learning Platform for Java, Clojure, Scala
  • Mahout - Distributed machine learning
  • Meka - An open source implementation of methods for multi-label classification and evaluation (extension to Weka).
  • MLlib in Apache Spark - Distributed machine learning library in Spark
  • Hydrosphere Mist - a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
  • Neuroph - Neuroph is lightweight Java neural network framework
  • ORYX - Lambda Architecture Framework using Apache Spark and Apache Kafka with a specialization for real-time large-scale machine learning.
  • Samoa SAMOA is a framework that includes distributed machine learning for data streams with an interface to plug-in different stream processing platforms.
  • RankLib - RankLib is a library of learning to rank algorithms
  • rapaio - statistics, data mining and machine learning toolbox in Java
  • RapidMiner - RapidMiner integration into Java code
  • Stanford Classifier - A classifier is a machine learning tool that will take data items and place them into one of k classes.
  • SmileMiner - Statistical Machine Intelligence & Learning Engine
  • SystemML - flexible, scalable machine learning (ML) language.
  • WalnutiQ - object oriented model of the human brain
  • Weka - Weka is a collection of machine learning algorithms for data mining tasks
  • LBJava - 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.

Speech Recognition

  • CMU Sphinx - Open Source Toolkit For Speech Recognition purely based on Java speech recognition library.

Data Analysis / Data Visualization

  • Flink - Open source platform for distributed stream and batch data processing.
  • Hadoop - Hadoop/HDFS
  • Onyx - Distributed, masterless, high performance, fault tolerant data processing. Written entirely in Clojure.
  • Spark - Spark is a fast and general engine for large-scale data processing.
  • Storm - Storm is a distributed realtime computation system.
  • Impala - Real-time Query for Hadoop
  • DataMelt - Mathematics software for numeric computation, statistics, symbolic calculations, data analysis and data visualization.
  • Dr. Michael Thomas Flanagan’s Java Scientific Library

Deep Learning

  • Deeplearning4j - Scalable deep learning for industry with parallel GPUs

Javascript

Natural Language Processing

  • Twitter-text - A JavaScript implementation of Twitter’s text processing library
  • natural - General natural language facilities for node
  • Knwl.js - A Natural Language Processor in JS
  • Retext - Extensible system for analyzing and manipulating natural language
  • TextProcessing - Sentiment analysis, stemming and lemmatization, part-of-speech tagging and chunking, phrase extraction and named entity recognition.
  • NLP Compromise - Natural Language processing in the browser

Data Analysis / Data Visualization

General-Purpose Machine Learning

  • Convnet.js - ConvNetJS is a Javascript library for training Deep Learning models[DEEP LEARNING]
  • Clusterfck - Agglomerative hierarchical clustering implemented in Javascript for Node.js and the browser
  • Clustering.js - Clustering algorithms implemented in Javascript for Node.js and the browser
  • Decision Trees - NodeJS Implementation of Decision Tree using ID3 Algorithm
  • DN2A - Digital Neural Networks Architecture
  • figue - K-means, fuzzy c-means and agglomerative clustering
  • Gaussian Mixture Model - Unsupervised machine learning with multivariate Gaussian mixture model
  • Node-fann - FANN (Fast Artificial Neural Network Library) bindings for Node.js
  • Kmeans.js - Simple Javascript implementation of the k-means algorithm, for node.js and the browser
  • LDA.js - LDA topic modeling for Node.js
  • Learning.js - Javascript implementation of logistic regression/c4.5 decision tree
  • Machine Learning - Machine learning library for Node.js
  • machineJS - Automated machine learning, data formatting, ensembling, and hyperparameter optimization for competitions and exploration- just give it a .csv file!
  • mil-tokyo - List of several machine learning libraries
  • Node-SVM - Support Vector Machine for Node.js
  • Brain - Neural networks in JavaScript [Deprecated]
  • Bayesian-Bandit - Bayesian bandit implementation for Node and the browser.
  • Synaptic - Architecture-free neural network library for Node.js and the browser
  • kNear - JavaScript implementation of the k nearest neighbors algorithm for supervised learning
  • NeuralN - C++ Neural Network library for Node.js. It has advantage on large dataset and multi-threaded training.
  • kalman - Kalman filter for Javascript.
  • shaman - Node.js library with support for both simple and multiple linear regression.
  • ml.js - Machine learning and numerical analysis tools for Node.js and the Browser!
  • Pavlov.js - Reinforcement learning using Markov Decision Processes
  • MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.

Misc

  • sylvester - Vector and Matrix math for JavaScript.
  • 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.
  • regression-js - A javascript library containing a collection of least squares fitting methods for finding a trend in a set of data.
  • Lyric - Linear Regression library.
  • GreatCircle - Library for calculating great circle distance.

Julia

General-Purpose Machine Learning

  • 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)
  • Local Regression - Local regression, so smooooth!
  • Naive Bayes - Simple Naive Bayes implementation in Julia
  • Mixed Models - A Julia package for fitting (statistical) mixed-effects models
  • 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
  • GLM - Generalized linear models in Julia
  • Gaussian Processes - Julia package for Gaussian processes
  • Online Learning
  • GLMNet - Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet
  • Clustering - Basic functions for clustering data: k-means, dp-means, etc.
  • SVM - SVM’s for Julia
  • Kernel Density - Kernel density estimators for julia
  • Dimensionality Reduction - Methods for dimensionality reduction
  • NMF - A Julia package for non-negative matrix factorization
  • ANN - Julia artificial neural networks
  • Mocha - Deep Learning framework for Julia inspired by Caffe
  • XGBoost - eXtreme Gradient Boosting Package in Julia
  • ManifoldLearning - A Julia package for manifold learning and nonlinear dimensionality reduction
  • MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
  • Merlin - Flexible Deep Learning Framework in Julia
  • ROCAnalysis - Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers
  • GaussianMixtures - Large scale Gaussian Mixture Models
  • ScikitLearn - Julia implementation of the scikit-learn API
  • Knet - Koç University Deep Learning Framework

Natural Language Processing

Data Analysis / Data Visualization

  • Graph Layout - Graph layout algorithms in pure Julia
  • LightGraphs - Graph modeling and analysis
  • Data Frames Meta - Metaprogramming tools for DataFrames
  • Julia Data - library for working with tabular data in Julia
  • Data Read - Read files from Stata, SAS, and SPSS
  • Hypothesis Tests - Hypothesis tests for Julia
  • Gadfly - Crafty statistical graphics for Julia.
  • 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
  • Distributions - A Julia package for probability distributions and associated functions.
  • Data Arrays - Data structures that allow missing values
  • Time Series - Time series toolkit for Julia
  • Sampling - Basic sampling algorithms for Julia

Misc Stuff / Presentations

Lua

General-Purpose Machine Learning

  • Torch7
    • 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.
    • autograd - Autograd automatically differentiates native Torch code. Inspired by the original Python version.
    • graph - Graph package for Torch
    • randomkit - Numpy’s randomkit, wrapped for Torch
    • signal - A signal processing toolbox for Torch-7. FFT, DCT, Hilbert, cepstrums, stft
    • nn - Neural Network package for Torch
    • torchnet - framework for torch which provides a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming
    • nngraph - This package provides graphical computation for nn library in Torch7.
    • nnx - A completely unstable and experimental package that extends Torch’s builtin nn library
    • rnn - A Recurrent Neural Network library that extends Torch’s nn. RNNs, LSTMs, GRUs, BRNNs, BLSTMs, etc.
    • dpnn - Many useful features that aren’t part of the main nn package.
    • 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.
    • optim - An optimization library for Torch. SGD, Adagrad, Conjugate-Gradient, LBFGS, RProp and more.
    • 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
    • cutorch - Torch CUDA Implementation
    • cunn - Torch CUDA Neural Network Implementation
    • 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.
    • OverFeat - A state-of-the-art generic dense feature extractor
  • Numeric Lua
  • Lunatic Python
  • SciLua
  • Lua - Numerical Algorithms
  • Lunum

Demos and Scripts

  • Core torch7 demos repository.
    • linear-regression, logistic-regression
    • face detector (training and detection as separate demos)
    • mst-based-segmenter
    • train-a-digit-classifier
    • train-autoencoder
    • optical flow demo
    • train-on-housenumbers
    • train-on-cifar
    • tracking with deep nets
    • kinect demo
    • filter-bank visualization
    • saliency-networks
  • Training a Convnet for the Galaxy-Zoo Kaggle challenge(CUDA demo)
  • Music Tagging - Music Tagging scripts for torch7
  • torch-datasets - Scripts to load several popular datasets including:
    • BSR 500
    • CIFAR-10
    • COIL
    • Street View House Numbers
    • MNIST
    • NORB
  • Atari2600 - Scripts to generate a dataset with static frames from the Arcade Learning Environment

Matlab

Computer Vision

  • Contourlets - MATLAB source code that implements the contourlet transform and its utility functions.
  • Shearlets - MATLAB code for shearlet transform
  • 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
  • mexopencv - Collection and a development kit of MATLAB mex functions for OpenCV library

Natural Language Processing

  • NLP - An NLP library for Matlab

General-Purpose Machine Learning

Data Analysis / Data Visualization

  • matlab_gbl - MatlabBGL is a Matlab package for working with graphs.
  • gamic - Efficient pure-Matlab implementations of graph algorithms to complement MatlabBGL’s mex functions.

.NET

Computer Vision

  • OpenCVDotNet - A wrapper for the OpenCV project to be used with .NET applications.
  • Emgu CV - Cross platform wrapper of OpenCV which can be compiled in Mono to e run on Windows, Linus, Mac OS X, iOS, and Android.
  • AForge.NET - Open source C# framework for developers and researchers in the fields of Computer Vision and Artificial Intelligence. Development has now shifted to GitHub.
  • Accord.NET - Together with AForge.NET, this library can provide image processing and computer vision algorithms to Windows, Windows RT and Windows Phone. Some components are also available for Java and Android.

Natural Language Processing

  • 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

  • Accord-Framework -The Accord.NET Framework is a complete framework for building machine learning, computer vision, computer audition, signal processing and statistical applications.
  • Accord.MachineLearning - Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications. This package is part of the Accord.NET Framework.
  • 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.
  • Vulpes - Deep belief and deep learning implementation written in F# and leverages CUDA GPU execution with Alea.cuBase.
  • Encog - 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.
  • 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.

Data Analysis / Data Visualization

  • numl - numl is a machine learning library intended to ease the use of using standard modeling techniques for both prediction and clustering.
  • Math.NET Numerics - Numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .Net 4.0, .Net 3.5 and Mono on Windows, Linux and Mac; Silverlight 5, WindowsPhone/SL 8, WindowsPhone 8.1 and Windows 8 with PCL Portable Profiles 47 and 344; Android/iOS with Xamarin.
  • 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.

Objective C

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.

OCaml

General-Purpose Machine Learning

  • Oml - A general statistics and machine learning library.
  • GPR - Efficient Gaussian Process Regression in OCaml.
  • Libra-Tk - Algorithms for learning and inference with discrete probabilistic models.
  • TensorFlow - OCaml bindings for TensorFlow.

Perl

Data Analysis / Data Visualization

General-Purpose Machine Learning

Perl 6

* Support Vector Machines * Naïve Bayes

Data Analysis / Data Visualization

General-Purpose Machine Learning

PHP

Natural Language Processing

  • jieba-php - Chinese Words Segmentation Utilities.

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.

Python

Computer Vision

  • Scikit-Image - A collection of algorithms for image processing in Python.
  • 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.
  • Vigranumpy - Python bindings for the VIGRA C++ computer vision library.
  • OpenFace - Free and open source face recognition with deep neural networks.
  • PCV - Open source Python module for computer vision

Natural Language Processing

  • NLTK - A leading platform for building Python programs to work with human language data.
  • Pattern - A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
  • Quepy - A python framework to transform natural language questions to queries in a database query language
  • 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.
  • YAlign - A sentence aligner, a friendly tool for extracting parallel sentences from comparable corpora.
  • jieba - Chinese Words Segmentation Utilities.
  • SnowNLP - A library for processing Chinese text.
  • spammy - A library for email Spam filtering built on top of nltk
  • loso - Another Chinese segmentation library.
  • genius - A Chinese segment base on Conditional Random Field.
  • KoNLPy - A Python package for Korean natural language processing.
  • nut - Natural language Understanding Toolkit
  • Rosetta - Text processing tools and wrappers (e.g. Vowpal Wabbit)
  • BLLIP Parser - Python bindings for the BLLIP Natural Language Parser (also known as the Charniak-Johnson parser)
  • 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, but also ARPA language models, Moses phrasetables, GIZA++ alignments.
  • python-ucto - Python binding to ucto (a unicode-aware rule-based tokenizer for various languages)
  • python-frog - Python binding to Frog, an NLP suite for Dutch. (pos tagging, lemmatisation, dependency parsing, NER)
  • python-zpar - Python bindings for ZPar, a statistical part-of-speech-tagger, constiuency parser, and dependency parser for English.
  • colibri-core - Python binding to C++ library for extracting and working with with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.
  • spaCy - Industrial strength NLP with Python and Cython.
  • PyStanfordDependencies - Python interface for converting Penn Treebank trees to Stanford Dependencies.
  • Distance - Levenshtein and Hamming distance computation
  • Fuzzy Wuzzy - Fuzzy String Matching in Python
  • [jellyfish](https://github.com/jamesturk/jellyfi

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