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awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.
https://github.com/eric-erki/awesome-machine-learning

Last synced: 2 days ago
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  • APL

    • naive-apl - Naive Bayesian Classifier implementation in APL.

  • C

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

  • Javascript

    • Speech Recognition

  • Java

    • Speech Recognition

      • Weka - Weka is a collection of machine learning algorithms for data mining tasks.
      • 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).
      • Stanford Topic Modeling Toolbox - Topic modeling tools to social scientists and others who wish to perform analysis on datasets.
      • OpenNLP - a machine learning based toolkit for the processing of natural language text.
      • ClearTK - ClearTK provides a framework for developing statistical natural language processing (NLP) components in Java and is built on top of Apache UIMA.
      • 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.

  • Perl 6

  • C++

    • Speech Recognition

      • 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.
      • encog-cpp
      • libfm - A generic approach that allows to mimic most factorization models by feature engineering.
      • PyCUDA - Python interface to CUDA
      • shark - A fast, modular, feature-rich open-source C++ machine learning library.
      • sofia-ml - Suite of fast incremental algorithms.
      • CRFsuite - CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data.

  • Clojure

    • Speech Recognition

      • Incanter - Incanter is a Clojure-based, R-like platform for statistical computing and graphics.

  • Erlang

    • Speech Recognition

      • Yanni - ANN neural networks using Erlangs leightweight processes.

  • Go

    • Speech Recognition

      • SVGo - The Go Language library for SVG generation.
      • SVGo - The Go Language library for SVG generation.

  • Haskell

    • Speech Recognition

      • hnn - Haskell Neural Network library.

  • Lua

  • Matlab

    • 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.
      • Convolutional-Recursive Deep Learning for 3D Object Classification - Convolutional-Recursive Deep Learning for 3D Object Classification[DEEP LEARNING].
      • 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.
      • matlab_gbl - MatlabBGL is a Matlab package for working with graphs.
      • Shearlets - MATLAB code for shearlet transform.

  • .NET

    • Speech Recognition

      • 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 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.
      • 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.
      • 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.
      • 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.

  • R

    • General-Purpose Machine Learning

      • RXshrink - RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression.
      • bigrf - bigrf: Big Random Forests: Classification and Regression Forests for Large Data Sets.
      • ahaz - ahaz: Regularization for semiparametric additive hazards regression.
      • arules - arules: Mining Association Rules and Frequent Itemsets
      • biglasso - biglasso: Extending Lasso Model Fitting to Big Data in R.
      • 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.
      • caret - Classification and Regression Training: Unified interface to ~150 ML algorithms in R.
      • caretEnsemble - caretEnsemble: Framework for fitting multiple caret models as well as creating ensembles of such 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.
      • 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.
      • fpc - fpc: Flexible procedures for clustering.
      • 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.
      • Introduction to Statistical Learning
      • 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.
      • mlr - mlr: Machine Learning in R.
      • 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.
      • 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.
      • ggplot2 - A data visualization package based on the grammar of graphics.
      • Optunity - A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search. Optunity is written in Python but interfaces seamlessly to R.

  • Perl

  • Python

    • General-Purpose Machine Learning

      • Pattern - A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
      • 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.
      • astroML - Machine Learning and Data Mining for Astronomy.
      • mrjob - A library to let Python program run on Hadoop.
      • Edward - A library for probabilistic modeling, inference, and criticism. Built on top of TensorFlow.
      • Numba - Python JIT (just in time) complier to LLVM aimed at scientific Python by the developers of Cython and NumPy.
      • Pandas - A library providing high-performance, easy-to-use data structures and data analysis tools.
      • PyDy - Short for Python Dynamics, used to assist with workflow in the modeling of dynamic motion based around NumPy, SciPy, IPython, and matplotlib.
      • cerebro2 - based visualization and debugging platform for NuPIC.
      • Seaborn - A python visualization library based on matplotlib.
      • A gallery of interesting IPython notebooks
      • 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.

  • PHP

    • 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.

  • Ruby

  • SAS

    • General-Purpose Machine Learning

      • Enterprise Miner - Data mining and machine learning that creates deployable models using a GUI or code.
      • High Performance Text Mining - Text mining using a GUI or code in an MPP environment, including Hadoop.
      • ML_Tables - Concise cheat sheets containing machine learning best practices.
      • Factory Miner - Automatically creates deployable machine learning models across numerous market or customer segments using a GUI.

  • Scala

    • 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.
      • Smile - Statistical Machine Intelligence and Learning Engine.
      • FlinkML in Apache Flink - Distributed machine learning library in Flink.

  • Swift

    • General-Purpose Machine Learning

      • Bender - Fast Neural Networks framework built on top of Metal. Supports TensorFlow models.
      • DeepLearningKit
      • 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.
      • 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...
      • Awesome Core ML Models - A curated list of machine learning models in CoreML format.

  • TensorFlow

    • General-Purpose Machine Learning

  • Credits

Programming Languages
C 4 Swift 3 Python 3 Ruby 2 Lua 1 Java 1 HTML 1 APL 1
Categories
R 82 Javascript 15 Python 13 Java 10 C++ 9 Lua 8 Matlab 8 Ruby 6 .NET 6 Swift 5 C 5 SAS 4 Perl 4 Scala 4 Go 2 Credits 2 Haskell 1 Erlang 1 Perl 6 1 Clojure 1 APL 1 TensorFlow 1 PHP 1
Sub Categories
General-Purpose Machine Learning 122 Speech Recognition 61 Data Analysis / Data Visualization 1
Keywords
machine-learning 8 awesome 4 ruby 3 awesome-list 3 nlp 2 apple 2 rubyml 2 rubynlp 2 swift 2 ml 2 list 2 regression 2 python 2 deep-learning 2 c 2 bayesian 2 ios 2 computational-linguistics 1 natural-language-processing 1 pos-tag 1 sentiment-analysis 1 ruby-gem 1 classification 1 clustering 1 computer-algebra-system 1 data-science 1 unicode 1 twitter-text 1 twitter 1 tweet 1 objective-c 1 nodejs 1 java 1 emoji 1 sparse 1 rvm 1 relevance-vector-machine 1 bindings 1 recommendation-engine 1 collaborative-filtering 1 bayes-classifier 1 swift-brain 1 caffe 1 caffemodel 1 core-ml 1 coreml 1 coreml-framework 1 coreml-model 1 coreml-models 1 coremltools 1