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added closure toolbox and shogun
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Joseph Misiti
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README.md
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README.md
@ -3,6 +3,196 @@ Other awesome lists can be found in the [awesome-awesomeness](https://github.com
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If you want to contribute to this list (please do), send me a pull request or contact me [@josephmisiti](https://www.twitter.com/josephmisiti)
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## C++
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#### Compute Vision
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* [ccv](https://github.com/liuliu/ccv)
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* [OpenCV](http://opencv.org) - OpenCV has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. It has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS.
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#### General-Purpose Machine Learning
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* [MLPack](http://www.mlpack.org/)
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* [DLib](http://dlib.net/ml.html)
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* [ecogg](https://code.google.com/p/encog-cpp/)
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* [shark](http://image.diku.dk/shark/sphinx_pages/build/html/index.html)
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## Closure
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#### General-Purpose Machine Learning
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* [Closure Toolbox](http://www.clojure-toolbox.com) - A categorised directory of libraries and tools for Clojure
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## Go
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#### Natural Language Processing
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* [go-porterstemmer](https://github.com/reiver/go-porterstemmer) - A native Go clean room implementation of the Porter Stemming algorithm.
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* [paicehusk](https://github.com/Rookii/paicehusk) - Golang implementation of the Paice/Husk Stemming Algorithm
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* [snowball](https://bitbucket.org/tebeka/snowball) - Snowball Stemmer for Go.
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#### General-Purpose Machine Learning
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* [Go Learn](https://github.com/sjwhitworth/golearn) - Machine Learning for Go
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* [go-pr](https://github.com/daviddengcn/go-pr) - Pattern recognition package in Go lang.
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* [bayesian](https://github.com/jbrukh/bayesian) - Naive Bayesian Classification for Golang.
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* [go-galib](https://github.com/thoj/go-galib) - Genetic Algorithms library written in Go / golang
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#### Data Analysis / Data Visualization
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* [go-graph](https://github.com/StepLg/go-graph) - Graph library for Go/golang language.
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* [SVGo](http://www.svgopen.org/2011/papers/34-SVGo_a_Go_Library_for_SVG_generation/) - The Go Language library for SVG generation
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## Java
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#### Natural Language Processing
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* [CoreNLP] (http://nlp.stanford.edu/software/corenlp.shtml) - 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
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* [Stanford Parser] (http://nlp.stanford.edu/software/lex-parser.shtml) - A natural language parser is a program that works out the grammatical structure of sentences
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* [Stanford POS Tagger] (http://nlp.stanford.edu/software/tagger.shtml) - A Part-Of-Speech Tagger (POS Tagger
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* [Stanford Name Entity Recognizer] (http://nlp.stanford.edu/software/CRF-NER.shtml) - Stanford NER is a Java implementation of a Named Entity Recognizer.
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* [Stanford Word Segmenter] (http://nlp.stanford.edu/software/segmenter.shtml) - Tokenization of raw text is a standard pre-processing step for many NLP tasks.
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* [Tregex, Tsurgeon and Semgrex](http://nlp.stanford.edu/software/tregex.shtml) - Tregex is a utility for matching patterns in trees, based on tree relationships and regular expression matches on nodes (the name is short for "tree regular expressions").
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* [Stanford Phrasal: A Phrase-Based Translation System](http://nlp.stanford.edu/software/phrasal/)
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* [Stanford English Tokenizer](http://nlp.stanford.edu/software/tokenizer.shtml) - Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java.
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* [Stanford Tokens Regex](http://nlp.stanford.edu/software/tokensregex.shtml) - A tokenizer divides text into a sequence of tokens, which roughly correspond to "words"
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* [Stanford Temporal Tagger](http://nlp.stanford.edu/software/sutime.shtml) - SUTime is a library for recognizing and normalizing time expressions.
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* [Stanford SPIED](http://nlp.stanford.edu/software/patternslearning.shtml) - Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion
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* [Stanford Topic Modeling Toolbox](http://nlp.stanford.edu/software/tmt/tmt-0.4/) - Topic modeling tools to social scientists and others who wish to perform analysis on datasets
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* [Twitter Text Java](https://github.com/twitter/twitter-text-java) - A Java implementation of Twitter's text processing library
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* [MALLET](http://mallet.cs.umass.edu/) - A Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
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* [OpenNLP](https://opennlp.apache.org/) - a machine learning based toolkit for the processing of natural language text.
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* [LingPipe](http://alias-i.com/lingpipe/index.html) - A tool kit for processing text using computational linguistics.
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#### General-Purpose Machine Learning
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* [MLlib in Apache Spark](http://spark.apache.org/docs/latest/mllib-guide.html) - Distributed machine learning library in Spark
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* [Mahout](https://github.com/apache/mahout) - Distributed machine learning
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* [Stanford Classifier](http://nlp.stanford.edu/software/classifier.shtml) - A classifier is a machine learning tool that will take data items and place them into one of k classes.
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* [Weka](http://www.cs.waikato.ac.nz/ml/weka/) - Weka is a collection of machine learning algorithms for data mining tasks
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* [ORYX](https://github.com/cloudera/oryx) - Simple real-time large-scale machine learning infrastructure.
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#### Data Analysis / Data Visualization
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* [Hadoop](https://github.com/apache/hadoop-mapreduce) - Hadoop/HDFS
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* [Spark](https://github.com/apache/spark) - Spark is a fast and general engine for large-scale data processing.
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* [Impala](https://github.com/cloudera/impala) - Real-time Query for Hadoop
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## Javascript
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#### Natural Language Processing
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* [Twitter-text-js](https://github.com/twitter/twitter-text-js) - A JavaScript implementation of Twitter's text processing library
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* [NLP.js](https://github.com/nicktesla/nlpjs) - NLP utilities in javascript and coffeescript
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#### Data Analysis / Data Visualization
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* [D3.js](http://d3js.org/)
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* [High Charts](http://www.highcharts.com/)
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* [NVD3.js](http://nvd3.org/)
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* [dc.js](http://dc-js.github.io/dc.js/)
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* [chartjs](http://www.chartjs.org/)
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* [dimple](http://dimplejs.org/)
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* [amCharts](http://www.amcharts.com/)
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#### General-Purpose Machine Learning
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* [Convnet.js](http://cs.stanford.edu/people/karpathy/convnetjs/) - ConvNetJS is a Javascript library for training Deep Learning models[DEEP LEARNING]
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* [Clustering.js](https://github.com/tixz/clustering.js) - Clustering algorithms implemented in Javascript for Node.js and the browser
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* [Decision Trees](https://github.com/serendipious/nodejs-decision-tree-id3) - NodeJS Implementation of Decision Tree using ID3 Algorithm
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* [Node-fann](https://github.com/rlidwka/node-fann) - FANN (Fast Artificial Neural Network Library) bindings for Node.js
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* [Kmeans.js](https://github.com/tixz/kmeans.js) - Simple Javascript implementation of the k-means algorithm, for node.js and the browser
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* [LDA.js](https://github.com/primaryobjects/lda) - LDA topic modeling for node.js
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* [Learning.js](https://github.com/yandongliu/learningjs) - Javascript implementation of logistic regression/c4.5 decision tree
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* [Machine Learning](http://joonku.com/project/machine_learning) - Machine learning library for Node.js
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* [Node-SVM](https://github.com/nicolaspanel/node-svm) - Support Vector Machine for nodejs
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* [Brain](https://github.com/harthur/brain) - Neural networks in JavaScript
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## Julia
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#### General-Purpose Machine Learning
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* [PGM](https://github.com/JuliaStats/PGM.jl) - A Julia framework for probabilistic graphical models.
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* [DA](https://github.com/trthatcher/DA.jl) - Julia package for Regularized Discriminant Analysis
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* [Regression](https://github.com/lindahua/Regression.jl) - Algorithms for regression analysis (e.g. linear regression and logistic regression)
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* [Local Regression](https://github.com/dcjones/Loess.jl) - Local regression, so smooooth!
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* [Naive Bayes](https://github.com/nutsiepully/NaiveBayes.jl) - Simple Naive Bayes implementation in Julia
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* [Mixed Models](https://github.com/dmbates/MixedModels.jl) - A Julia package for fitting (statistical) mixed-effects models
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* [Simple MCMC](https://github.com/fredo-dedup/SimpleMCMC.jl) - basic mcmc sampler implemented in Julia
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* [Distance](https://github.com/JuliaStats/Distance.jl) - Julia module for Distance evaluation
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* [Decision Tree](https://github.com/bensadeghi/DecisionTree.jl) - Decision Tree Classifier and Regressor
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* [Neural](https://github.com/compressed/neural.jl) - A neural network in Julia
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* [MCMC](https://github.com/doobwa/MCMC.jl) - MCMC tools for Julia
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* [GLM](https://github.com/JuliaStats/GLM.jl) - Generalized linear models in Julia
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* [Online Learning](https://github.com/lendle/OnlineLearning.jl)
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* [GLMNet](https://github.com/simonster/GLMNet.jl) - Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet
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* [Clustering](https://github.com/JuliaStats/Clustering.jl) - Basic functions for clustering data: k-means, dp-means, etc.
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* [SVM](https://github.com/JuliaStats/SVM.jl) - SVM's for Julia
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* [Kernal Density](https://github.com/JuliaStats/KernelDensity.jl) - Kernel density estimators for julia
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* [Dimensionality Reduction](https://github.com/JuliaStats/DimensionalityReduction.jl) - Methods for dimensionality reduction
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* [NMF](https://github.com/JuliaStats/NMF.jl) - A Julia package for non-negative matrix factorization
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#### Natural Language Processing
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* [Topic Models](https://github.com/slycoder/TopicModels.jl) - TopicModels for Julia
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* [Text Analysis](https://github.com/johnmyleswhite/TextAnalysis.jl) - Julia package for text analysis
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#### Data Analysis / Data Visualization
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* [Graph Layout](https://github.com/IainNZ/GraphLayout.jl) - Graph layout algorithms in pure Julia
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* [Data Frames Meta](https://github.com/JuliaStats/DataFramesMeta.jl) - Metaprogramming tools for DataFrames
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* [Julia Data](https://github.com/nfoti/JuliaData) - library for working with tabular data in Julia
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* [Data Read](https://github.com/WizardMac/DataRead.jl) - Read files from Stata, SAS, and SPSS
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* [Hypothesis Tests](https://github.com/JuliaStats/HypothesisTests.jl) - Hypothesis tests for Julia
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* [Gladfly](https://github.com/dcjones/Gadfly.jl) - Crafty statistical graphics for Julia.
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* [Stats](https://github.com/johnmyleswhite/stats.jl) - Statistical tests for Julia
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* [RDataSets](https://github.com/johnmyleswhite/RDatasets.jl) - Julia package for loading many of the data sets available in R
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* [DataFrames](https://github.com/JuliaStats/DataFrames.jl) - library for working with tabular data in Julia
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* [Distributions](https://github.com/JuliaStats/Distributions.jl) - A Julia package for probability distributions and associated functions.
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* [Data Arrays](https://github.com/JuliaStats/DataArrays.jl) - Data structures that allow missing values
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* [Time Series](https://github.com/JuliaStats/TimeSeries.jl) - Time series toolkit for Julia
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* [Sampling](https://github.com/JuliaStats/Sampling.jl) - Basic sampling algorithms for Julia
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#### Misc Stuff / Presentations
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* [JuliaCon Presentations](https://github.com/JuliaCon/presentations) - Presentations for JuliaCon
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* [SignalProcessing](https://github.com/davidavdav/SignalProcessing) - Signal Processing tools for Julia
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* [Images](https://github.com/timholy/Images.jl) - An image library for Julia
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## Matlab
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#### Computer Vision
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* [Contourlets](http://www.ifp.illinois.edu/~minhdo/software/contourlet_toolbox.tar) - MATLAB source code that implements the contourlet transform and its utility functions.
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* [Shearlets](http://www.shearlab.org/index_software.html) - MATLAB code for shearlet transform
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* [Curvelets](http://www.curvelet.org/software.html) - The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles.
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* [Bandlets](http://www.cmap.polytechnique.fr/~peyre/download/) - MATLAB code for bandlet transform
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#### Natural Language Processing
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* [NLP](https://amplab.cs.berkeley.edu/2012/05/05/an-nlp-library-for-matlab/) - An NLP library for Matlab
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#### General-Purpose Machine Learning
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* [Training a deep autoencoder or a classifier
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on MNIST digits](http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html) - Training a deep autoencoder or a classifier
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on MNIST digits[DEEP LEARNING]
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* [t-Distributed Stochastic Neighbor Embedding](http://homepage.tudelft.nl/19j49/t-SNE.html) - t-Distributed Stochastic Neighbor Embedding (t-SNE) is a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.
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* [Spider](http://people.kyb.tuebingen.mpg.de/spider/) - The spider is intended to be a complete object orientated environment for machine learning in Matlab.
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* [LibSVM](http://www.csie.ntu.edu.tw/~cjlin/libsvm/#matlab) - A Library for Support Vector Machines
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* [LibLinear](http://www.csie.ntu.edu.tw/~cjlin/liblinear/#download) - A Library for Large Linear Classification
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* [Machine Learning Module](https://github.com/josephmisiti/machine-learning-module) - Class on machine w/ PDF,lectures,code
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#### Data Analysis / Data Visualization
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* [matlab_gbl](https://www.cs.purdue.edu/homes/dgleich/packages/matlab_bgl/) - MatlabBGL is a Matlab package for working with graphs.
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* [gamic](http://www.mathworks.com/matlabcentral/fileexchange/24134-gaimc---graph-algorithms-in-matlab-code) - Efficient pure-Matlab implementations of graph algorithms to complement MatlabBGL's mex functions.
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## Python
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@ -36,6 +226,7 @@ If you want to contribute to this list (please do), send me a pull request or co
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* [Bolt](https://github.com/pprett/bolt) - Bolt Online Learning Toolbox
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* [CoverTree](https://github.com/patvarilly/CoverTree) - Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree
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* [nilearn](https://github.com/nilearn/nilearn) - Machine learning for NeuroImaging in Python
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* [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox
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#### Data Analysis / Data Visualization
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@ -100,21 +291,6 @@ If you want to contribute to this list (please do), send me a pull request or co
|
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* [kaggle_acquire-valued-shoppers-challenge](https://github.com/MLWave/kaggle_acquire-valued-shoppers-challenge) - Code for the Kaggle acquire valued shoppers challenge
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* [wine-quality](https://github.com/zygmuntz/wine-quality) - Predicting wine quality
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## C++
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#### Compute Vision
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* [ccv](https://github.com/liuliu/ccv)
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* [OpenCV](http://opencv.org) - OpenCV has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. It has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS.
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#### General-Purpose Machine Learning
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* [MLPack](http://www.mlpack.org/)
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* [DLib](http://dlib.net/ml.html)
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* [ecogg](https://code.google.com/p/encog-cpp/)
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* [shark](http://image.diku.dk/shark/sphinx_pages/build/html/index.html)
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## Ruby
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#### Natural Language Processing
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@ -162,39 +338,6 @@ If you want to contribute to this list (please do), send me a pull request or co
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* [Learning Statistics Using R](http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/)
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## Javascript
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#### Natural Language Processing
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* [Twitter-text-js](https://github.com/twitter/twitter-text-js) - A JavaScript implementation of Twitter's text processing library
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* [NLP.js](https://github.com/nicktesla/nlpjs) - NLP utilities in javascript and coffeescript
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#### Data Analysis / Data Visualization
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* [D3.js](http://d3js.org/)
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* [High Charts](http://www.highcharts.com/)
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* [NVD3.js](http://nvd3.org/)
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* [dc.js](http://dc-js.github.io/dc.js/)
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* [chartjs](http://www.chartjs.org/)
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* [dimple](http://dimplejs.org/)
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* [amCharts](http://www.amcharts.com/)
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|
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#### General-Purpose Machine Learning
|
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|
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* [Convnet.js](http://cs.stanford.edu/people/karpathy/convnetjs/) - ConvNetJS is a Javascript library for training Deep Learning models[DEEP LEARNING]
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* [Clustering.js](https://github.com/tixz/clustering.js) - Clustering algorithms implemented in Javascript for Node.js and the browser
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* [Decision Trees](https://github.com/serendipious/nodejs-decision-tree-id3) - NodeJS Implementation of Decision Tree using ID3 Algorithm
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* [Node-fann](https://github.com/rlidwka/node-fann) - FANN (Fast Artificial Neural Network Library) bindings for Node.js
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* [Kmeans.js](https://github.com/tixz/kmeans.js) - Simple Javascript implementation of the k-means algorithm, for node.js and the browser
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* [LDA.js](https://github.com/primaryobjects/lda) - LDA topic modeling for node.js
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* [Learning.js](https://github.com/yandongliu/learningjs) - Javascript implementation of logistic regression/c4.5 decision tree
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* [Machine Learning](http://joonku.com/project/machine_learning) - Machine learning library for Node.js
|
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* [Node-SVM](https://github.com/nicolaspanel/node-svm) - Support Vector Machine for nodejs
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* [Brain](https://github.com/harthur/brain) - Neural networks in JavaScript
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## Scala
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#### Natural Language Processing
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@ -222,147 +365,6 @@ If you want to contribute to this list (please do), send me a pull request or co
|
||||
* [adam](https://github.com/bigdatagenomics/adam) - A genomics processing engine and specialized file format built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.
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* [bioscala](https://github.com/bioscala/bioscala) - Bioinformatics for the Scala programming language
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## Java
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#### Natural Language Processing
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* [CoreNLP] (http://nlp.stanford.edu/software/corenlp.shtml) - 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] (http://nlp.stanford.edu/software/lex-parser.shtml) - A natural language parser is a program that works out the grammatical structure of sentences
|
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* [Stanford POS Tagger] (http://nlp.stanford.edu/software/tagger.shtml) - A Part-Of-Speech Tagger (POS Tagger
|
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* [Stanford Name Entity Recognizer] (http://nlp.stanford.edu/software/CRF-NER.shtml) - Stanford NER is a Java implementation of a Named Entity Recognizer.
|
||||
* [Stanford Word Segmenter] (http://nlp.stanford.edu/software/segmenter.shtml) - Tokenization of raw text is a standard pre-processing step for many NLP tasks.
|
||||
* [Tregex, Tsurgeon and Semgrex](http://nlp.stanford.edu/software/tregex.shtml) - Tregex is a utility for matching patterns in trees, based on tree relationships and regular expression matches on nodes (the name is short for "tree regular expressions").
|
||||
* [Stanford Phrasal: A Phrase-Based Translation System](http://nlp.stanford.edu/software/phrasal/)
|
||||
* [Stanford English Tokenizer](http://nlp.stanford.edu/software/tokenizer.shtml) - Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java.
|
||||
* [Stanford Tokens Regex](http://nlp.stanford.edu/software/tokensregex.shtml) - A tokenizer divides text into a sequence of tokens, which roughly correspond to "words"
|
||||
* [Stanford Temporal Tagger](http://nlp.stanford.edu/software/sutime.shtml) - SUTime is a library for recognizing and normalizing time expressions.
|
||||
* [Stanford SPIED](http://nlp.stanford.edu/software/patternslearning.shtml) - Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion
|
||||
* [Stanford Topic Modeling Toolbox](http://nlp.stanford.edu/software/tmt/tmt-0.4/) - Topic modeling tools to social scientists and others who wish to perform analysis on datasets
|
||||
* [Twitter Text Java](https://github.com/twitter/twitter-text-java) - A Java implementation of Twitter's text processing library
|
||||
* [MALLET](http://mallet.cs.umass.edu/) - A Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
|
||||
* [OpenNLP](https://opennlp.apache.org/) - a machine learning based toolkit for the processing of natural language text.
|
||||
* [LingPipe](http://alias-i.com/lingpipe/index.html) - A tool kit for processing text using computational linguistics.
|
||||
|
||||
#### General-Purpose Machine Learning
|
||||
|
||||
* [MLlib in Apache Spark](http://spark.apache.org/docs/latest/mllib-guide.html) - Distributed machine learning library in Spark
|
||||
* [Mahout](https://github.com/apache/mahout) - Distributed machine learning
|
||||
* [Stanford Classifier](http://nlp.stanford.edu/software/classifier.shtml) - A classifier is a machine learning tool that will take data items and place them into one of k classes.
|
||||
* [Weka](http://www.cs.waikato.ac.nz/ml/weka/) - Weka is a collection of machine learning algorithms for data mining tasks
|
||||
* [ORYX](https://github.com/cloudera/oryx) - Simple real-time large-scale machine learning infrastructure.
|
||||
|
||||
#### Data Analysis / Data Visualization
|
||||
|
||||
* [Hadoop](https://github.com/apache/hadoop-mapreduce) - Hadoop/HDFS
|
||||
* [Spark](https://github.com/apache/spark) - Spark is a fast and general engine for large-scale data processing.
|
||||
* [Impala](https://github.com/cloudera/impala) - Real-time Query for Hadoop
|
||||
|
||||
## Go
|
||||
|
||||
#### Natural Language Processing
|
||||
|
||||
* [go-porterstemmer](https://github.com/reiver/go-porterstemmer) - A native Go clean room implementation of the Porter Stemming algorithm.
|
||||
* [paicehusk](https://github.com/Rookii/paicehusk) - Golang implementation of the Paice/Husk Stemming Algorithm
|
||||
* [snowball](https://bitbucket.org/tebeka/snowball) - Snowball Stemmer for Go.
|
||||
|
||||
#### General-Purpose Machine Learning
|
||||
|
||||
* [Go Learn](https://github.com/sjwhitworth/golearn) - Machine Learning for Go
|
||||
* [go-pr](https://github.com/daviddengcn/go-pr) - Pattern recognition package in Go lang.
|
||||
* [bayesian](https://github.com/jbrukh/bayesian) - Naive Bayesian Classification for Golang.
|
||||
* [go-galib](https://github.com/thoj/go-galib) - Genetic Algorithms library written in Go / golang
|
||||
|
||||
#### Data Analysis / Data Visualization
|
||||
|
||||
* [go-graph](https://github.com/StepLg/go-graph) - Graph library for Go/golang language.
|
||||
* [SVGo](http://www.svgopen.org/2011/papers/34-SVGo_a_Go_Library_for_SVG_generation/) - The Go Language library for SVG generation
|
||||
|
||||
|
||||
|
||||
## Matlab
|
||||
|
||||
#### Computer Vision
|
||||
|
||||
* [Contourlets](http://www.ifp.illinois.edu/~minhdo/software/contourlet_toolbox.tar) - MATLAB source code that implements the contourlet transform and its utility functions.
|
||||
* [Shearlets](http://www.shearlab.org/index_software.html) - MATLAB code for shearlet transform
|
||||
* [Curvelets](http://www.curvelet.org/software.html) - The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles.
|
||||
* [Bandlets](http://www.cmap.polytechnique.fr/~peyre/download/) - MATLAB code for bandlet transform
|
||||
|
||||
|
||||
#### Natural Language Processing
|
||||
|
||||
* [NLP](https://amplab.cs.berkeley.edu/2012/05/05/an-nlp-library-for-matlab/) - An NLP library for Matlab
|
||||
|
||||
#### General-Purpose Machine Learning
|
||||
|
||||
* [Training a deep autoencoder or a classifier
|
||||
on MNIST digits](http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html) - Training a deep autoencoder or a classifier
|
||||
on MNIST digits[DEEP LEARNING]
|
||||
* [t-Distributed Stochastic Neighbor Embedding](http://homepage.tudelft.nl/19j49/t-SNE.html) - t-Distributed Stochastic Neighbor Embedding (t-SNE) is a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.
|
||||
* [Spider](http://people.kyb.tuebingen.mpg.de/spider/) - The spider is intended to be a complete object orientated environment for machine learning in Matlab.
|
||||
* [LibSVM](http://www.csie.ntu.edu.tw/~cjlin/libsvm/#matlab) - A Library for Support Vector Machines
|
||||
* [LibLinear](http://www.csie.ntu.edu.tw/~cjlin/liblinear/#download) - A Library for Large Linear Classification
|
||||
* [Machine Learning Module](https://github.com/josephmisiti/machine-learning-module) - Class on machine w/ PDF,lectures,code
|
||||
|
||||
#### Data Analysis / Data Visualization
|
||||
|
||||
* [matlab_gbl](https://www.cs.purdue.edu/homes/dgleich/packages/matlab_bgl/) - MatlabBGL is a Matlab package for working with graphs.
|
||||
* [gamic](http://www.mathworks.com/matlabcentral/fileexchange/24134-gaimc---graph-algorithms-in-matlab-code) - Efficient pure-Matlab implementations of graph algorithms to complement MatlabBGL's mex functions.
|
||||
|
||||
## Julia
|
||||
|
||||
#### General-Purpose Machine Learning
|
||||
|
||||
* [PGM](https://github.com/JuliaStats/PGM.jl) - A Julia framework for probabilistic graphical models.
|
||||
* [DA](https://github.com/trthatcher/DA.jl) - Julia package for Regularized Discriminant Analysis
|
||||
* [Regression](https://github.com/lindahua/Regression.jl) - Algorithms for regression analysis (e.g. linear regression and logistic regression)
|
||||
* [Local Regression](https://github.com/dcjones/Loess.jl) - Local regression, so smooooth!
|
||||
* [Naive Bayes](https://github.com/nutsiepully/NaiveBayes.jl) - Simple Naive Bayes implementation in Julia
|
||||
* [Mixed Models](https://github.com/dmbates/MixedModels.jl) - A Julia package for fitting (statistical) mixed-effects models
|
||||
* [Simple MCMC](https://github.com/fredo-dedup/SimpleMCMC.jl) - basic mcmc sampler implemented in Julia
|
||||
* [Distance](https://github.com/JuliaStats/Distance.jl) - Julia module for Distance evaluation
|
||||
* [Decision Tree](https://github.com/bensadeghi/DecisionTree.jl) - Decision Tree Classifier and Regressor
|
||||
* [Neural](https://github.com/compressed/neural.jl) - A neural network in Julia
|
||||
* [MCMC](https://github.com/doobwa/MCMC.jl) - MCMC tools for Julia
|
||||
* [GLM](https://github.com/JuliaStats/GLM.jl) - Generalized linear models in Julia
|
||||
* [Online Learning](https://github.com/lendle/OnlineLearning.jl)
|
||||
* [GLMNet](https://github.com/simonster/GLMNet.jl) - Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet
|
||||
* [Clustering](https://github.com/JuliaStats/Clustering.jl) - Basic functions for clustering data: k-means, dp-means, etc.
|
||||
* [SVM](https://github.com/JuliaStats/SVM.jl) - SVM's for Julia
|
||||
* [Kernal Density](https://github.com/JuliaStats/KernelDensity.jl) - Kernel density estimators for julia
|
||||
* [Dimensionality Reduction](https://github.com/JuliaStats/DimensionalityReduction.jl) - Methods for dimensionality reduction
|
||||
* [NMF](https://github.com/JuliaStats/NMF.jl) - A Julia package for non-negative matrix factorization
|
||||
|
||||
|
||||
#### Natural Language Processing
|
||||
|
||||
* [Topic Models](https://github.com/slycoder/TopicModels.jl) - TopicModels for Julia
|
||||
* [Text Analysis](https://github.com/johnmyleswhite/TextAnalysis.jl) - Julia package for text analysis
|
||||
|
||||
|
||||
#### Data Analysis / Data Visualization
|
||||
|
||||
* [Graph Layout](https://github.com/IainNZ/GraphLayout.jl) - Graph layout algorithms in pure Julia
|
||||
* [Data Frames Meta](https://github.com/JuliaStats/DataFramesMeta.jl) - Metaprogramming tools for DataFrames
|
||||
* [Julia Data](https://github.com/nfoti/JuliaData) - library for working with tabular data in Julia
|
||||
* [Data Read](https://github.com/WizardMac/DataRead.jl) - Read files from Stata, SAS, and SPSS
|
||||
* [Hypothesis Tests](https://github.com/JuliaStats/HypothesisTests.jl) - Hypothesis tests for Julia
|
||||
* [Gladfly](https://github.com/dcjones/Gadfly.jl) - Crafty statistical graphics for Julia.
|
||||
* [Stats](https://github.com/johnmyleswhite/stats.jl) - Statistical tests for Julia
|
||||
|
||||
* [RDataSets](https://github.com/johnmyleswhite/RDatasets.jl) - Julia package for loading many of the data sets available in R
|
||||
* [DataFrames](https://github.com/JuliaStats/DataFrames.jl) - library for working with tabular data in Julia
|
||||
* [Distributions](https://github.com/JuliaStats/Distributions.jl) - A Julia package for probability distributions and associated functions.
|
||||
* [Data Arrays](https://github.com/JuliaStats/DataArrays.jl) - Data structures that allow missing values
|
||||
* [Time Series](https://github.com/JuliaStats/TimeSeries.jl) - Time series toolkit for Julia
|
||||
* [Sampling](https://github.com/JuliaStats/Sampling.jl) - Basic sampling algorithms for Julia
|
||||
|
||||
#### Misc Stuff / Presentations
|
||||
|
||||
* [JuliaCon Presentations](https://github.com/JuliaCon/presentations) - Presentations for JuliaCon
|
||||
* [SignalProcessing](https://github.com/davidavdav/SignalProcessing) - Signal Processing tools for Julia
|
||||
* [Images](https://github.com/timholy/Images.jl) - An image library for Julia
|
||||
|
||||
## Credits
|
||||
|
||||
* Some of the python libraries were cut-and-pasted from [vinta](https://github.com/vinta/awesome-python)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user