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@ -329,8 +329,8 @@ Further resources:
#### 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
* [RF](https://github.com/fxsjy/RF.go) - Random forests implementation in Go
* [SVGo](http://www.svgopen.org/2011/papers/34-SVGo_a_Go_Library_for_SVG_generation/) - The Go Language library for SVG generation.
* [RF](https://github.com/fxsjy/RF.go) - Random forests implementation in Go.
* [Glot](https://github.com/arafatk/glot) - Glot is a plotting library for Golang built on top of gnuplot.
<a name="haskell"></a>
@ -342,8 +342,8 @@ Further resources:
* [HLearn](https://github.com/mikeizbicki/HLearn) - a suite of libraries for interpreting machine learning models according to their algebraic structure.
* [hnn](https://wiki.haskell.org/HNN) - Haskell Neural Network library.
* [hopfield-networks](https://github.com/ajtulloch/hopfield-networks) - Hopfield Networks for unsupervised learning in Haskell.
* [caffegraph](https://github.com/ajtulloch/dnngraph) - A DSL for deep neural networks
* [LambdaNet](https://github.com/jbarrow/LambdaNet) - Configurable Neural Networks in Haskell
* [caffegraph](https://github.com/ajtulloch/dnngraph) - A DSL for deep neural networks.
* [LambdaNet](https://github.com/jbarrow/LambdaNet) - Configurable Neural Networks in Haskell.
<a name="java"></a>
## Java
@ -351,10 +351,10 @@ Further resources:
<a name="java-nlp"></a>
#### Natural Language Processing
* [Cortical.io](http://www.cortical.io/) - Retina: an API performing complex NLP operations (disambiguation, classification, streaming text filtering, etc...) as quickly and intuitively as the brain.
* [IRIS](https://github.com/cortical-io/Public/tree/master/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)
* [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
* [Stanford POS Tagger](http://nlp.stanford.edu/software/tagger.shtml) - A Part-Of-Speech Tagger (POS Tagger
* [IRIS](https://github.com/cortical-io/Public/tree/master/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).
* [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.
* [Stanford POS Tagger](http://nlp.stanford.edu/software/tagger.shtml) - A Part-Of-Speech Tagger (POS Tagger).
* [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").
@ -992,19 +992,19 @@ be
* [data-science-ipython-notebooks](https://github.com/donnemartin/data-science-ipython-notebooks) - Continually updated Data Science Python Notebooks: Spark, Hadoop MapReduce, HDFS, AWS, Kaggle, scikit-learn, matplotlib, pandas, NumPy, SciPy, and various command lines.
* [decision-weights](https://github.com/CamDavidsonPilon/decision-weights)
* [Sarah Palin LDA](https://github.com/Wavelets/sarah-palin-lda) - Topic Modeling the Sarah Palin emails.
* [Diffusion Segmentation](https://github.com/Wavelets/diffusion-segmentation) - A collection of image segmentation algorithms based on diffusion methods
* [Scipy Tutorials](https://github.com/Wavelets/scipy-tutorials) - SciPy tutorials. This is outdated, check out scipy-lecture-notes
* [Crab](https://github.com/marcelcaraciolo/crab) - A recommendation engine library for Python
* [BayesPy](https://github.com/maxsklar/BayesPy) - Bayesian Inference Tools in Python
* [scikit-learn tutorials](https://github.com/GaelVaroquaux/scikit-learn-tutorial) - Series of notebooks for learning scikit-learn
* [Diffusion Segmentation](https://github.com/Wavelets/diffusion-segmentation) - A collection of image segmentation algorithms based on diffusion methods.
* [Scipy Tutorials](https://github.com/Wavelets/scipy-tutorials) - SciPy tutorials. This is outdated, check out scipy-lecture-notes.
* [Crab](https://github.com/marcelcaraciolo/crab) - A recommendation engine library for Python.
* [BayesPy](https://github.com/maxsklar/BayesPy) - Bayesian Inference Tools in Python.
* [scikit-learn tutorials](https://github.com/GaelVaroquaux/scikit-learn-tutorial) - Series of notebooks for learning scikit-learn.
* [sentiment-analyzer](https://github.com/madhusudancs/sentiment-analyzer) - Tweets Sentiment Analyzer
* [sentiment_classifier](https://github.com/kevincobain2000/sentiment_classifier) - Sentiment classifier using word sense disambiguation.
* [group-lasso](https://github.com/fabianp/group_lasso) - Some experiments with the coordinate descent algorithm used in the (Sparse) Group Lasso model
* [group-lasso](https://github.com/fabianp/group_lasso) - Some experiments with the coordinate descent algorithm used in the (Sparse) Group Lasso model.
* [jProcessing](https://github.com/kevincobain2000/jProcessing) - Kanji / Hiragana / Katakana to Romaji Converter. Edict Dictionary & parallel sentences Search. Sentence Similarity between two JP Sentences. Sentiment Analysis of Japanese Text. Run Cabocha(ISO--8859-1 configured) in Python.
* [mne-python-notebooks](https://github.com/mne-tools/mne-python-notebooks) - IPython notebooks for EEG/MEG data processing using mne-python
* [Neon Course](https://github.com/NervanaSystems/neon_course) - IPython notebooks for a complete course around understanding Nervana's Neon
* [pandas cookbook](https://github.com/jvns/pandas-cookbook) - Recipes for using Python's pandas library
* [climin](https://github.com/BRML/climin) - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others
* [mne-python-notebooks](https://github.com/mne-tools/mne-python-notebooks) - IPython notebooks for EEG/MEG data processing using mne-python.
* [Neon Course](https://github.com/NervanaSystems/neon_course) - IPython notebooks for a complete course around understanding Nervana's Neon.
* [pandas cookbook](https://github.com/jvns/pandas-cookbook) - Recipes for using Python's pandas library.
* [climin](https://github.com/BRML/climin) - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others.
* [Allen Downey’s Data Science Course](https://github.com/AllenDowney/DataScience) - Code for Data Science at Olin College, Spring 2014.
* [Allen Downey’s Think Bayes Code](https://github.com/AllenDowney/ThinkBayes) - Code repository for Think Bayes.
* [Allen Downey’s Think Complexity Code](https://github.com/AllenDowney/ThinkComplexity) - Code for Allen Downey's book Think Complexity.
@ -1016,7 +1016,7 @@ be
* [TDB](https://github.com/ericjang/tdb) - TensorDebugger (TDB) is a visual debugger for deep learning. It features interactive, node-by-node debugging and visualization for TensorFlow.
* [Suiron](https://github.com/kendricktan/suiron/) - Machine Learning for RC Cars.
* [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos) - IPython notebooks from Data School's video tutorials on scikit-learn.
* [Practical XGBoost in Python](http://education.parrotprediction.teachable.com/p/practical-xgboost-in-python) - comprehensive online course about using XGBoost in Python
* [Practical XGBoost in Python](http://education.parrotprediction.teachable.com/p/practical-xgboost-in-python) - comprehensive online course about using XGBoost in Python.
<a name="python-neural-networks"></a>
#### Neural Networks
@ -1026,23 +1026,23 @@ be
<a name="python-kaggle"></a>
#### Kaggle Competition Source Code
* [wiki challenge](https://github.com/hammer/wikichallenge) - An implementation of Dell Zhang's solution to Wikipedia's Participation Challenge on Kaggle
* [kaggle insults](https://github.com/amueller/kaggle_insults) - Kaggle Submission for "Detecting Insults in Social Commentary"
* [kaggle_acquire-valued-shoppers-challenge](https://github.com/MLWave/kaggle_acquire-valued-shoppers-challenge) - Code for the Kaggle acquire valued shoppers challenge
* [kaggle-cifar](https://github.com/zygmuntz/kaggle-cifar) - Code for the CIFAR-10 competition at Kaggle, uses cuda-convnet
* [kaggle-blackbox](https://github.com/zygmuntz/kaggle-blackbox) - Deep learning made easy
* [kaggle-accelerometer](https://github.com/zygmuntz/kaggle-accelerometer) - Code for Accelerometer Biometric Competition at Kaggle
* [kaggle-advertised-salaries](https://github.com/zygmuntz/kaggle-advertised-salaries) - Predicting job salaries from ads - a Kaggle competition
* [kaggle amazon](https://github.com/zygmuntz/kaggle-amazon) - Amazon access control challenge
* [kaggle-bestbuy_big](https://github.com/zygmuntz/kaggle-bestbuy_big) - Code for the Best Buy competition at Kaggle
* [wiki challenge](https://github.com/hammer/wikichallenge) - An implementation of Dell Zhang's solution to Wikipedia's Participation Challenge on Kaggle.
* [kaggle insults](https://github.com/amueller/kaggle_insults) - Kaggle Submission for "Detecting Insults in Social Commentary".
* [kaggle_acquire-valued-shoppers-challenge](https://github.com/MLWave/kaggle_acquire-valued-shoppers-challenge) - Code for the Kaggle acquire valued shoppers challenge.
* [kaggle-cifar](https://github.com/zygmuntz/kaggle-cifar) - Code for the CIFAR-10 competition at Kaggle, uses cuda-convnet.
* [kaggle-blackbox](https://github.com/zygmuntz/kaggle-blackbox) - Deep learning made easy.
* [kaggle-accelerometer](https://github.com/zygmuntz/kaggle-accelerometer) - Code for Accelerometer Biometric Competition at Kaggle.
* [kaggle-advertised-salaries](https://github.com/zygmuntz/kaggle-advertised-salaries) - Predicting job salaries from ads - a Kaggle competition.
* [kaggle amazon](https://github.com/zygmuntz/kaggle-amazon) - Amazon access control challenge.
* [kaggle-bestbuy_big](https://github.com/zygmuntz/kaggle-bestbuy_big) - Code for the Best Buy competition at Kaggle.
* [kaggle-bestbuy_small](https://github.com/zygmuntz/kaggle-bestbuy_small)
* [Kaggle Dogs vs. Cats](https://github.com/kastnerkyle/kaggle-dogs-vs-cats) - Code for Kaggle Dogs vs. Cats competition
* [Kaggle Galaxy Challenge](https://github.com/benanne/kaggle-galaxies) - Winning solution for the Galaxy Challenge on Kaggle
* [Kaggle Gender](https://github.com/zygmuntz/kaggle-gender) - A Kaggle competition: discriminate gender based on handwriting
* [Kaggle Merck](https://github.com/zygmuntz/kaggle-merck) - Merck challenge at Kaggle
* [Kaggle Stackoverflow](https://github.com/zygmuntz/kaggle-stackoverflow) - Predicting closed questions on Stack Overflow
* [kaggle_acquire-valued-shoppers-challenge](https://github.com/MLWave/kaggle_acquire-valued-shoppers-challenge) - Code for the Kaggle acquire valued shoppers challenge
* [wine-quality](https://github.com/zygmuntz/wine-quality) - Predicting wine quality
* [Kaggle Dogs vs. Cats](https://github.com/kastnerkyle/kaggle-dogs-vs-cats) - Code for Kaggle Dogs vs. Cats competition.
* [Kaggle Galaxy Challenge](https://github.com/benanne/kaggle-galaxies) - Winning solution for the Galaxy Challenge on Kaggle.
* [Kaggle Gender](https://github.com/zygmuntz/kaggle-gender) - A Kaggle competition: discriminate gender based on handwriting.
* [Kaggle Merck](https://github.com/zygmuntz/kaggle-merck) - Merck challenge at Kaggle.
* [Kaggle Stackoverflow](https://github.com/zygmuntz/kaggle-stackoverflow) - Predicting closed questions on Stack Overflow.
* [kaggle_acquire-valued-shoppers-challenge](https://github.com/MLWave/kaggle_acquire-valued-shoppers-challenge) - Code for the Kaggle acquire valued shoppers challenge.
* [wine-quality](https://github.com/zygmuntz/wine-quality) - Predicting wine quality.
<a name="python-reinforcement-learning"></a>
#### Reinforcement Learning
@ -1059,35 +1059,35 @@ be
#### Natural Language Processing
* [Awesome NLP with Ruby](https://github.com/arbox/nlp-with-ruby) - Curated link list for practical natural language processing in Ruby.
* [Treat](https://github.com/louismullie/treat) - Text REtrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby
* [Treat](https://github.com/louismullie/treat) - Text REtrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby.
* [Ruby Linguistics](https://deveiate.org/projects/Linguistics) - Linguistics is a framework for building linguistic utilities for Ruby objects in any language. It includes a generic language-independent front end, a module for mapping language codes into language names, and a module which contains various English-language utilities.
* [Stemmer](https://github.com/aurelian/ruby-stemmer) - Expose libstemmer_c to Ruby
* [Ruby Wordnet](https://deveiate.org/projects/Ruby-WordNet/) - This library is a Ruby interface to WordNet
* [Raspel](https://sourceforge.net/projects/raspell/) - raspell is an interface binding for ruby
* [UEA Stemmer](https://github.com/ealdent/uea-stemmer) - Ruby port of UEALite Stemmer - a conservative stemmer for search and indexing
* [Twitter-text-rb](https://github.com/twitter/twitter-text-rb) - A library that does auto linking and extraction of usernames, lists and hashtags in tweets
* [Stemmer](https://github.com/aurelian/ruby-stemmer) - Expose libstemmer_c to Ruby.
* [Ruby Wordnet](https://deveiate.org/projects/Ruby-WordNet/) - This library is a Ruby interface to WordNet.
* [Raspel](https://sourceforge.net/projects/raspell/) - raspell is an interface binding for ruby.
* [UEA Stemmer](https://github.com/ealdent/uea-stemmer) - Ruby port of UEALite Stemmer - a conservative stemmer for search and indexing.
* [Twitter-text-rb](https://github.com/twitter/twitter-text-rb) - A library that does auto linking and extraction of usernames, lists and hashtags in tweets.
<a name="ruby-general-purpose"></a>
#### General-Purpose Machine Learning
* [Awesome Machine Learning with Ruby](https://github.com/arbox/machine-learning-with-ruby) - Curated list of ML related resources for Ruby
* [Ruby Machine Learning](https://github.com/tsycho/ruby-machine-learning) - Some Machine Learning algorithms, implemented in Ruby
* [Awesome Machine Learning with Ruby](https://github.com/arbox/machine-learning-with-ruby) - Curated list of ML related resources for Ruby.
* [Ruby Machine Learning](https://github.com/tsycho/ruby-machine-learning) - Some Machine Learning algorithms, implemented in Ruby.
* [Machine Learning Ruby](https://github.com/mizoR/machine-learning-ruby)
* [jRuby Mahout](https://github.com/vasinov/jruby_mahout) - JRuby Mahout is a gem that unleashes the power of Apache Mahout in the world of JRuby.
* [CardMagic-Classifier](https://github.com/cardmagic/classifier) - A general classifier module to allow Bayesian and other types of classifications.
* [rb-libsvm](https://github.com/febeling/rb-libsvm) - Ruby language bindings for LIBSVM which is a Library for Support Vector Machines
* [Random Forester](https://github.com/asafschers/random_forester) - Creates Random Forest classifiers from PMML files
* [rb-libsvm](https://github.com/febeling/rb-libsvm) - Ruby language bindings for LIBSVM which is a Library for Support Vector Machines.
* [Random Forester](https://github.com/asafschers/random_forester) - Creates Random Forest classifiers from PMML files.
<a name="ruby-data-analysis"></a>
#### Data Analysis / Data Visualization
* [rsruby](https://github.com/alexgutteridge/rsruby) - Ruby - R bridge
* [data-visualization-ruby](https://github.com/chrislo/data_visualisation_ruby) - Source code and supporting content for my Ruby Manor presentation on Data Visualisation with Ruby
* [ruby-plot](https://www.ruby-toolbox.com/projects/ruby-plot) - gnuplot wrapper for ruby, especially for plotting roc curves into svg files
* [rsruby](https://github.com/alexgutteridge/rsruby) - Ruby - R bridge.
* [data-visualization-ruby](https://github.com/chrislo/data_visualisation_ruby) - Source code and supporting content for my Ruby Manor presentation on Data Visualisation with Ruby.
* [ruby-plot](https://www.ruby-toolbox.com/projects/ruby-plot) - gnuplot wrapper for Ruby, especially for plotting ROC curves into SVG files.
* [plot-rb](https://github.com/zuhao/plotrb) - A plotting library in Ruby built on top of Vega and D3.
* [scruffy](http://www.rubyinside.com/scruffy-a-beautiful-graphing-toolkit-for-ruby-194.html) - A beautiful graphing toolkit for Ruby
* [scruffy](http://www.rubyinside.com/scruffy-a-beautiful-graphing-toolkit-for-ruby-194.html) - A beautiful graphing toolkit for Ruby.
* [SciRuby](http://sciruby.com/)
* [Glean](https://github.com/glean/glean) - A data management tool for humans
* [Glean](https://github.com/glean/glean) - A data management tool for humans.
* [Bioruby](https://github.com/bioruby/bioruby)
* [Arel](https://github.com/nkallen/arel)
@ -1116,92 +1116,92 @@ be
<a name="r-general-purpose"></a>
#### General-Purpose Machine Learning
* [ahaz](http://cran.r-project.org/web/packages/ahaz/index.html) - ahaz: Regularization for semiparametric additive hazards regression
* [ahaz](http://cran.r-project.org/web/packages/ahaz/index.html) - ahaz: Regularization for semiparametric additive hazards regression.
* [arules](http://cran.r-project.org/web/packages/arules/index.html) - arules: Mining Association Rules and Frequent Itemsets
* [biglasso](https://cran.r-project.org/web/packages/biglasso/index.html) - biglasso: Extending Lasso Model Fitting to Big Data in R
* [bigrf](http://cran.r-project.org/web/packages/bigrf/index.html) - bigrf: Big Random Forests: Classification and Regression Forests for Large Data Sets
* [bigRR](http://cran.r-project.org/web/packages/bigRR/index.html) - bigRR: Generalized Ridge Regression (with special advantage for p >> n cases)
* [bmrm](http://cran.r-project.org/web/packages/bmrm/index.html) - bmrm: Bundle Methods for Regularized Risk Minimization Package
* [Boruta](http://cran.r-project.org/web/packages/Boruta/index.html) - Boruta: A wrapper algorithm for all-relevant feature selection
* [bst](http://cran.r-project.org/web/packages/bst/index.html) - bst: Gradient Boosting
* [C50](http://cran.r-project.org/web/packages/C50/index.html) - C50: C5.0 Decision Trees and Rule-Based Models
* [biglasso](https://cran.r-project.org/web/packages/biglasso/index.html) - biglasso: Extending Lasso Model Fitting to Big Data in R.
* [bigrf](http://cran.r-project.org/web/packages/bigrf/index.html) - bigrf: Big Random Forests: Classification and Regression Forests for Large Data Sets.
* [bigRR](http://cran.r-project.org/web/packages/bigRR/index.html) - bigRR: Generalized Ridge Regression (with special advantage for p >> n cases).
* [bmrm](http://cran.r-project.org/web/packages/bmrm/index.html) - bmrm: Bundle Methods for Regularized Risk Minimization Package.
* [Boruta](http://cran.r-project.org/web/packages/Boruta/index.html) - Boruta: A wrapper algorithm for all-relevant feature selection.
* [bst](http://cran.r-project.org/web/packages/bst/index.html) - bst: Gradient Boosting.
* [C50](http://cran.r-project.org/web/packages/C50/index.html) - C50: C5.0 Decision Trees and Rule-Based Models.
* [caret](http://caret.r-forge.r-project.org/) - Classification and Regression Training: Unified interface to ~150 ML algorithms in R.
* [caretEnsemble](http://cran.r-project.org/web/packages/caretEnsemble/index.html) - caretEnsemble: Framework for fitting multiple caret models as well as creating ensembles of such models.
* [Clever Algorithms For Machine Learning](https://github.com/jbrownlee/CleverAlgorithmsMachineLearning)
* [CORElearn](http://cran.r-project.org/web/packages/CORElearn/index.html) - CORElearn: Classification, regression, feature evaluation and ordinal evaluation
* [CORElearn](http://cran.r-project.org/web/packages/CORElearn/index.html) - CORElearn: Classification, regression, feature evaluation and ordinal evaluation.
* [CoxBoost](http://cran.r-project.org/web/packages/CoxBoost/index.html) - CoxBoost: Cox models by likelihood based boosting for a single survival endpoint or competing risks
* [Cubist](http://cran.r-project.org/web/packages/Cubist/index.html) - Cubist: Rule- and Instance-Based Regression Modeling
* [Cubist](http://cran.r-project.org/web/packages/Cubist/index.html) - Cubist: Rule- and Instance-Based Regression Modeling.
* [e1071](http://cran.r-project.org/web/packages/e1071/index.html) - e1071: Misc Functions of the Department of Statistics (e1071), TU Wien
* [earth](http://cran.r-project.org/web/packages/earth/index.html) - earth: Multivariate Adaptive Regression Spline Models
* [elasticnet](http://cran.r-project.org/web/packages/elasticnet/index.html) - elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA
* [ElemStatLearn](http://cran.r-project.org/web/packages/ElemStatLearn/index.html) - 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](http://cran.r-project.org/web/packages/evtree/index.html) - evtree: Evolutionary Learning of Globally Optimal Trees
* [forecast](http://cran.r-project.org/web/packages/forecast/index.html) - forecast: Timeseries forecasting using ARIMA, ETS, STLM, TBATS, and neural network models
* [forecastHybrid](http://cran.r-project.org/web/packages/forecastHybrid/index.html) - forecastHybrid: Automatic ensemble and cross validation of ARIMA, ETS, STLM, TBATS, and neural network models from the "forecast" package
* [fpc](http://cran.r-project.org/web/packages/fpc/index.html) - fpc: Flexible procedures for clustering
* [frbs](http://cran.r-project.org/web/packages/frbs/index.html) - frbs: Fuzzy Rule-based Systems for Classification and Regression Tasks
* [GAMBoost](http://cran.r-project.org/web/packages/GAMBoost/index.html) - GAMBoost: Generalized linear and additive models by likelihood based boosting
* [gamboostLSS](http://cran.r-project.org/web/packages/gamboostLSS/index.html) - gamboostLSS: Boosting Methods for GAMLSS
* [gbm](http://cran.r-project.org/web/packages/gbm/index.html) - gbm: Generalized Boosted Regression Models
* [glmnet](http://cran.r-project.org/web/packages/glmnet/index.html) - glmnet: Lasso and elastic-net regularized generalized linear models
* [glmpath](http://cran.r-project.org/web/packages/glmpath/index.html) - glmpath: L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model
* [GMMBoost](http://cran.r-project.org/web/packages/GMMBoost/index.html) - GMMBoost: Likelihood-based Boosting for Generalized mixed models
* [grplasso](http://cran.r-project.org/web/packages/grplasso/index.html) - grplasso: Fitting user specified models with Group Lasso penalty
* [grpreg](http://cran.r-project.org/web/packages/grpreg/index.html) - grpreg: Regularization paths for regression models with grouped covariates
* [h2o](http://cran.r-project.org/web/packages/h2o/index.html) - A framework for fast, parallel, and distributed machine learning algorithms at scale -- Deeplearning, Random forests, GBM, KMeans, PCA, GLM
* [hda](http://cran.r-project.org/web/packages/hda/index.html) - hda: Heteroscedastic Discriminant Analysis
* [elasticnet](http://cran.r-project.org/web/packages/elasticnet/index.html) - elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA.
* [ElemStatLearn](http://cran.r-project.org/web/packages/ElemStatLearn/index.html) - 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](http://cran.r-project.org/web/packages/evtree/index.html) - evtree: Evolutionary Learning of Globally Optimal Trees.
* [forecast](http://cran.r-project.org/web/packages/forecast/index.html) - forecast: Timeseries forecasting using ARIMA, ETS, STLM, TBATS, and neural network models.
* [forecastHybrid](http://cran.r-project.org/web/packages/forecastHybrid/index.html) - forecastHybrid: Automatic ensemble and cross validation of ARIMA, ETS, STLM, TBATS, and neural network models from the "forecast" package.
* [fpc](http://cran.r-project.org/web/packages/fpc/index.html) - fpc: Flexible procedures for clustering.
* [frbs](http://cran.r-project.org/web/packages/frbs/index.html) - frbs: Fuzzy Rule-based Systems for Classification and Regression Tasks.
* [GAMBoost](http://cran.r-project.org/web/packages/GAMBoost/index.html) - GAMBoost: Generalized linear and additive models by likelihood based boosting.
* [gamboostLSS](http://cran.r-project.org/web/packages/gamboostLSS/index.html) - gamboostLSS: Boosting Methods for GAMLSS.
* [gbm](http://cran.r-project.org/web/packages/gbm/index.html) - gbm: Generalized Boosted Regression Models.
* [glmnet](http://cran.r-project.org/web/packages/glmnet/index.html) - glmnet: Lasso and elastic-net regularized generalized linear models.
* [glmpath](http://cran.r-project.org/web/packages/glmpath/index.html) - glmpath: L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model.
* [GMMBoost](http://cran.r-project.org/web/packages/GMMBoost/index.html) - GMMBoost: Likelihood-based Boosting for Generalized mixed models.
* [grplasso](http://cran.r-project.org/web/packages/grplasso/index.html) - grplasso: Fitting user specified models with Group Lasso penalty.
* [grpreg](http://cran.r-project.org/web/packages/grpreg/index.html) - grpreg: Regularization paths for regression models with grouped covariates.
* [h2o](http://cran.r-project.org/web/packages/h2o/index.html) - A framework for fast, parallel, and distributed machine learning algorithms at scale -- Deeplearning, Random forests, GBM, KMeans, PCA, GLM.
* [hda](http://cran.r-project.org/web/packages/hda/index.html) - hda: Heteroscedastic Discriminant Analysis.
* [Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
* [ipred](http://cran.r-project.org/web/packages/ipred/index.html) - ipred: Improved Predictors
* [kernlab](http://cran.r-project.org/web/packages/kernlab/index.html) - kernlab: Kernel-based Machine Learning Lab
* [klaR](http://cran.r-project.org/web/packages/klaR/index.html) - klaR: Classification and visualization
* [lars](http://cran.r-project.org/web/packages/lars/index.html) - lars: Least Angle Regression, Lasso and Forward Stagewise
* [lasso2](http://cran.r-project.org/web/packages/lasso2/index.html) - lasso2: L1 constrained estimation aka ‘lasso’
* [LiblineaR](http://cran.r-project.org/web/packages/LiblineaR/index.html) - LiblineaR: Linear Predictive Models Based On The Liblinear C/C++ Library
* [LogicReg](http://cran.r-project.org/web/packages/LogicReg/index.html) - LogicReg: Logic Regression
* [ipred](http://cran.r-project.org/web/packages/ipred/index.html) - ipred: Improved Predictors.
* [kernlab](http://cran.r-project.org/web/packages/kernlab/index.html) - kernlab: Kernel-based Machine Learning Lab.
* [klaR](http://cran.r-project.org/web/packages/klaR/index.html) - klaR: Classification and visualization.
* [lars](http://cran.r-project.org/web/packages/lars/index.html) - lars: Least Angle Regression, Lasso and Forward Stagewise.
* [lasso2](http://cran.r-project.org/web/packages/lasso2/index.html) - lasso2: L1 constrained estimation aka ‘lasso’.
* [LiblineaR](http://cran.r-project.org/web/packages/LiblineaR/index.html) - LiblineaR: Linear Predictive Models Based On The Liblinear C/C++ Library.
* [LogicReg](http://cran.r-project.org/web/packages/LogicReg/index.html) - LogicReg: Logic Regression.
* [Machine Learning For Hackers](https://github.com/johnmyleswhite/ML_for_Hackers)
* [maptree](http://cran.r-project.org/web/packages/maptree/index.html) - maptree: Mapping, pruning, and graphing tree models
* [mboost](http://cran.r-project.org/web/packages/mboost/index.html) - mboost: Model-Based Boosting
* [medley](https://www.kaggle.com/forums/f/15/kaggle-forum/t/3661/medley-a-new-r-package-for-blending-regression-models?forumMessageId=21278) - medley: Blending regression models, using a greedy stepwise approach
* [mlr](http://cran.r-project.org/web/packages/mlr/index.html) - mlr: Machine Learning in R
* [mvpart](http://cran.r-project.org/web/packages/mvpart/index.html) - mvpart: Multivariate partitioning
* [ncvreg](http://cran.r-project.org/web/packages/ncvreg/index.html) - ncvreg: Regularization paths for SCAD- and MCP-penalized regression models
* [nnet](http://cran.r-project.org/web/packages/nnet/index.html) - nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models
* [oblique.tree](http://cran.r-project.org/web/packages/oblique.tree/index.html) - oblique.tree: Oblique Trees for Classification Data
* [pamr](http://cran.r-project.org/web/packages/pamr/index.html) - pamr: Pam: prediction analysis for microarrays
* [party](http://cran.r-project.org/web/packages/party/index.html) - party: A Laboratory for Recursive Partytioning
* [partykit](http://cran.r-project.org/web/packages/partykit/index.html) - partykit: A Toolkit for Recursive Partytioning
* [penalized](http://cran.r-project.org/web/packages/penalized/index.html) - penalized: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model
* [penalizedLDA](http://cran.r-project.org/web/packages/penalizedLDA/index.html) - penalizedLDA: Penalized classification using Fisher's linear discriminant
* [penalizedSVM](http://cran.r-project.org/web/packages/penalizedSVM/index.html) - penalizedSVM: Feature Selection SVM using penalty functions
* [quantregForest](http://cran.r-project.org/web/packages/quantregForest/index.html) - quantregForest: Quantile Regression Forests
* [randomForest](http://cran.r-project.org/web/packages/randomForest/index.html) - randomForest: Breiman and Cutler's random forests for classification and regression
* [randomForestSRC](http://cran.r-project.org/web/packages/randomForestSRC/index.html) - randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC)
* [rattle](http://cran.r-project.org/web/packages/rattle/index.html) - rattle: Graphical user interface for data mining in R
* [rda](http://cran.r-project.org/web/packages/rda/index.html) - rda: Shrunken Centroids Regularized Discriminant Analysis
* [rdetools](http://cran.r-project.org/web/packages/rdetools/index.html) - rdetools: Relevant Dimension Estimation (RDE) in Feature Spaces
* [REEMtree](http://cran.r-project.org/web/packages/REEMtree/index.html) - REEMtree: Regression Trees with Random Effects for Longitudinal (Panel) Data
* [relaxo](http://cran.r-project.org/web/packages/relaxo/index.html) - relaxo: Relaxed Lasso
* [maptree](http://cran.r-project.org/web/packages/maptree/index.html) - maptree: Mapping, pruning, and graphing tree models.
* [mboost](http://cran.r-project.org/web/packages/mboost/index.html) - mboost: Model-Based Boosting.
* [medley](https://www.kaggle.com/forums/f/15/kaggle-forum/t/3661/medley-a-new-r-package-for-blending-regression-models?forumMessageId=21278) - medley: Blending regression models, using a greedy stepwise approach.
* [mlr](http://cran.r-project.org/web/packages/mlr/index.html) - mlr: Machine Learning in R.
* [mvpart](http://cran.r-project.org/web/packages/mvpart/index.html) - mvpart: Multivariate partitioning.
* [ncvreg](http://cran.r-project.org/web/packages/ncvreg/index.html) - ncvreg: Regularization paths for SCAD- and MCP-penalized regression models.
* [nnet](http://cran.r-project.org/web/packages/nnet/index.html) - nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models.
* [oblique.tree](http://cran.r-project.org/web/packages/oblique.tree/index.html) - oblique.tree: Oblique Trees for Classification Data.
* [pamr](http://cran.r-project.org/web/packages/pamr/index.html) - pamr: Pam: prediction analysis for microarrays.
* [party](http://cran.r-project.org/web/packages/party/index.html) - party: A Laboratory for Recursive Partytioning.
* [partykit](http://cran.r-project.org/web/packages/partykit/index.html) - partykit: A Toolkit for Recursive Partytioning.
* [penalized](http://cran.r-project.org/web/packages/penalized/index.html) - penalized: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model.
* [penalizedLDA](http://cran.r-project.org/web/packages/penalizedLDA/index.html) - penalizedLDA: Penalized classification using Fisher's linear discriminant.
* [penalizedSVM](http://cran.r-project.org/web/packages/penalizedSVM/index.html) - penalizedSVM: Feature Selection SVM using penalty functions.
* [quantregForest](http://cran.r-project.org/web/packages/quantregForest/index.html) - quantregForest: Quantile Regression Forests.
* [randomForest](http://cran.r-project.org/web/packages/randomForest/index.html) - randomForest: Breiman and Cutler's random forests for classification and regression.
* [randomForestSRC](http://cran.r-project.org/web/packages/randomForestSRC/index.html) - randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC).
* [rattle](http://cran.r-project.org/web/packages/rattle/index.html) - rattle: Graphical user interface for data mining in R.
* [rda](http://cran.r-project.org/web/packages/rda/index.html) - rda: Shrunken Centroids Regularized Discriminant Analysis.
* [rdetools](http://cran.r-project.org/web/packages/rdetools/index.html) - rdetools: Relevant Dimension Estimation (RDE) in Feature Spaces.
* [REEMtree](http://cran.r-project.org/web/packages/REEMtree/index.html) - REEMtree: Regression Trees with Random Effects for Longitudinal (Panel) Data.
* [relaxo](http://cran.r-project.org/web/packages/relaxo/index.html) - relaxo: Relaxed Lasso.
* [rgenoud](http://cran.r-project.org/web/packages/rgenoud/index.html) - rgenoud: R version of GENetic Optimization Using Derivatives
* [rgp](http://cran.r-project.org/web/packages/rgp/index.html) - rgp: R genetic programming framework
* [Rmalschains](http://cran.r-project.org/web/packages/Rmalschains/index.html) - Rmalschains: Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R
* [rminer](http://cran.r-project.org/web/packages/rminer/index.html) - rminer: Simpler use of data mining methods (e.g. NN and SVM) in classification and regression
* [ROCR](http://cran.r-project.org/web/packages/ROCR/index.html) - ROCR: Visualizing the performance of scoring classifiers
* [RoughSets](http://cran.r-project.org/web/packages/RoughSets/index.html) - RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories
* [rpart](http://cran.r-project.org/web/packages/rpart/index.html) - rpart: Recursive Partitioning and Regression Trees
* [RPMM](http://cran.r-project.org/web/packages/RPMM/index.html) - RPMM: Recursively Partitioned Mixture Model
* [RSNNS](http://cran.r-project.org/web/packages/RSNNS/index.html) - RSNNS: Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS)
* [RWeka](http://cran.r-project.org/web/packages/RWeka/index.html) - RWeka: R/Weka interface
* [RXshrink](http://cran.r-project.org/web/packages/RXshrink/index.html) - RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression
* [sda](http://cran.r-project.org/web/packages/sda/index.html) - sda: Shrinkage Discriminant Analysis and CAT Score Variable Selection
* [SDDA](http://cran.r-project.org/web/packages/SDDA/index.html) - SDDA: Stepwise Diagonal Discriminant Analysis
* [rgp](http://cran.r-project.org/web/packages/rgp/index.html) - rgp: R genetic programming framework.
* [Rmalschains](http://cran.r-project.org/web/packages/Rmalschains/index.html) - Rmalschains: Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R.
* [rminer](http://cran.r-project.org/web/packages/rminer/index.html) - rminer: Simpler use of data mining methods (e.g. NN and SVM) in classification and regression.
* [ROCR](http://cran.r-project.org/web/packages/ROCR/index.html) - ROCR: Visualizing the performance of scoring classifiers.
* [RoughSets](http://cran.r-project.org/web/packages/RoughSets/index.html) - RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories.
* [rpart](http://cran.r-project.org/web/packages/rpart/index.html) - rpart: Recursive Partitioning and Regression Trees.
* [RPMM](http://cran.r-project.org/web/packages/RPMM/index.html) - RPMM: Recursively Partitioned Mixture Model.
* [RSNNS](http://cran.r-project.org/web/packages/RSNNS/index.html) - RSNNS: Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS).
* [RWeka](http://cran.r-project.org/web/packages/RWeka/index.html) - RWeka: R/Weka interface.
* [RXshrink](http://cran.r-project.org/web/packages/RXshrink/index.html) - RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression.
* [sda](http://cran.r-project.org/web/packages/sda/index.html) - sda: Shrinkage Discriminant Analysis and CAT Score Variable Selection.
* [SDDA](http://cran.r-project.org/web/packages/SDDA/index.html) - SDDA: Stepwise Diagonal Discriminant Analysis.
* [SuperLearner](https://github.com/ecpolley/SuperLearner) and [subsemble](http://cran.r-project.org/web/packages/subsemble/index.html) - Multi-algorithm ensemble learning packages.
* [svmpath](http://cran.r-project.org/web/packages/svmpath/index.html) - svmpath: svmpath: the SVM Path algorithm
* [tgp](http://cran.r-project.org/web/packages/tgp/index.html) - tgp: Bayesian treed Gaussian process models
* [tree](http://cran.r-project.org/web/packages/tree/index.html) - tree: Classification and regression trees
* [varSelRF](http://cran.r-project.org/web/packages/varSelRF/index.html) - varSelRF: Variable selection using random forests
* [XGBoost.R](https://github.com/tqchen/xgboost/tree/master/R-package) - R binding for eXtreme Gradient Boosting (Tree) Library
* [svmpath](http://cran.r-project.org/web/packages/svmpath/index.html) - svmpath: svmpath: the SVM Path algorithm.
* [tgp](http://cran.r-project.org/web/packages/tgp/index.html) - tgp: Bayesian treed Gaussian process models.
* [tree](http://cran.r-project.org/web/packages/tree/index.html) - tree: Classification and regression trees.
* [varSelRF](http://cran.r-project.org/web/packages/varSelRF/index.html) - varSelRF: Variable selection using random forests.
* [XGBoost.R](https://github.com/tqchen/xgboost/tree/master/R-package) - R binding for eXtreme Gradient Boosting (Tree) Library.
* [Optunity](http://optunity.readthedocs.io/en/latest/) - 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.
* [igraph](http://igraph.org/r/) - binding to igraph library - General purpose graph library
* [igraph](http://igraph.org/r/) - binding to igraph library - General purpose graph library.
* [MXNet](https://github.com/dmlc/mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
* [TDSP-Utilities](https://github.com/Azure/Azure-TDSP-Utilities) - Two data science utilities in R from Microsoft: 1) Interactive Data Exploration, Analysis, and Reporting (IDEAR) ; 2) Automated Modeling and Reporting (AMR).
@ -1265,10 +1265,10 @@ be
* [MLlib in Apache Spark](http://spark.apache.org/docs/latest/mllib-guide.html) - Distributed machine learning library in Spark
* [Hydrosphere Mist](https://github.com/Hydrospheredata/mist) - a service for deployment Apache Spark MLLib machine learning models as realtime, batch or reactive web services.
* [Scalding](https://github.com/twitter/scalding) - A Scala API for Cascading
* [Summing Bird](https://github.com/twitter/summingbird) - Streaming MapReduce with Scalding and Storm
* [Algebird](https://github.com/twitter/algebird) - Abstract Algebra for Scala
* [xerial](https://github.com/xerial/xerial) - Data management utilities for Scala
* [Scalding](https://github.com/twitter/scalding) - A Scala API for Cascading.
* [Summing Bird](https://github.com/twitter/summingbird) - Streaming MapReduce with Scalding and Storm.
* [Algebird](https://github.com/twitter/algebird) - Abstract Algebra for Scala.
* [xerial](https://github.com/xerial/xerial) - Data management utilities for Scala.
* [PredictionIO](https://github.com/apache/incubator-predictionio) - PredictionIO, a machine learning server for software developers and data engineers.
* [BIDMat](https://github.com/BIDData/BIDMat) - CPU and GPU-accelerated matrix library intended to support large-scale exploratory data analysis.
* [Flink](http://flink.apache.org/) - Open source platform for distributed stream and batch data processing.
@ -1278,19 +1278,19 @@ be
#### General-Purpose Machine Learning
* [DeepLearning.scala](http://deeplearning.thoughtworks.school/) - Creating statically typed dynamic neural networks from object-oriented & functional programming constructs.
* [Conjecture](https://github.com/etsy/Conjecture) - Scalable Machine Learning in Scalding
* [brushfire](https://github.com/stripe/brushfire) - Distributed decision tree ensemble learning in Scala
* [ganitha](https://github.com/tresata/ganitha) - scalding powered machine learning
* [Conjecture](https://github.com/etsy/Conjecture) - Scalable Machine Learning in Scalding.
* [brushfire](https://github.com/stripe/brushfire) - Distributed decision tree ensemble learning in Scala.
* [ganitha](https://github.com/tresata/ganitha) - Scalding powered machine learning.
* [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.
* [bioscala](https://github.com/bioscala/bioscala) - Bioinformatics for the Scala programming language
* [BIDMach](https://github.com/BIDData/BIDMach) - CPU and GPU-accelerated Machine Learning Library.
* [Figaro](https://github.com/p2t2/figaro) - a Scala library for constructing probabilistic models.
* [H2O Sparkling Water](https://github.com/h2oai/sparkling-water) - H2O and Spark interoperability.
* [FlinkML in Apache Flink](https://ci.apache.org/projects/flink/flink-docs-master/apis/batch/libs/ml/index.html) - Distributed machine learning library in Flink
* [DynaML](https://github.com/transcendent-ai-labs/DynaML) - Scala Library/REPL for Machine Learning Research
* [FlinkML in Apache Flink](https://ci.apache.org/projects/flink/flink-docs-master/apis/batch/libs/ml/index.html) - Distributed machine learning library in Flink.
* [DynaML](https://github.com/transcendent-ai-labs/DynaML) - Scala Library/REPL for Machine Learning Research.
* [Saul](https://github.com/IllinoisCogComp/saul/) - Flexible Declarative Learning-Based Programming.
* [SwiftLearner](https://github.com/valdanylchuk/swiftlearner/) - Simply written algorithms to help study ML or write your own implementations.
* [Smile](http://haifengl.github.io/smile/) - Statistical Machine Intelligence and Learning Engine
* [Smile](http://haifengl.github.io/smile/) - Statistical Machine Intelligence and Learning Engine.
<a name="swift"></a>
## Swift
@ -1300,16 +1300,16 @@ be
* [Bender](https://github.com/xmartlabs/Bender) - Fast Neural Networks framework built on top of Metal. Supports TensorFlow models.
* [Swift AI](https://github.com/collinhundley/Swift-AI) - Highly optimized artificial intelligence and machine learning library written in Swift.
* [BrainCore](https://github.com/aleph7/BrainCore) - The iOS and OS X neural network framework
* [BrainCore](https://github.com/aleph7/BrainCore) - The iOS and OS X neural network framework.
* [swix](https://github.com/stsievert/swix) - A bare bones library that
includes a general matrix language and wraps some OpenCV for iOS development.
* [DeepLearningKit](http://deeplearningkit.org/) an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS.
It currently allows using deep convolutional neural network models trained in Caffe on Apple operating systems.
* [AIToolbox](https://github.com/KevinCoble/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.
* [MLKit](https://github.com/Somnibyte/MLKit) - A simple Machine Learning Framework written in Swift. Currently features Simple Linear Regression, Polynomial Regression, and Ridge Regression.
* [Swift Brain](https://github.com/vlall/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..
* [Swift Brain](https://github.com/vlall/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...
* [Perfect TensorFlow](https://github.com/PerfectlySoft/Perfect-TensorFlow) - Swift Language Bindings of TensorFlow. Using native TensorFlow models on both macOS / Linux.
* [Awesome CoreML](https://github.com/NilStack/awesome-CoreML-models) - A curated list of pretrained CoreML models
* [Awesome CoreML](https://github.com/NilStack/awesome-CoreML-models) - A curated list of pretrained CoreML models.
* [Awesome Core ML Models](https://github.com/likedan/Awesome-CoreML-Models) - A curated list of machine learning models in CoreML format.
<a name="tensor"></a>
@ -1317,7 +1317,7 @@ be
<a name="tensor-general-purpose"></a>
#### General-Purpose Machine Learning
* [Awesome TensorFlow](https://github.com/jtoy/awesome-tensorflow) - A list of all things related to TensorFlow
* [Awesome TensorFlow](https://github.com/jtoy/awesome-tensorflow) - A list of all things related to TensorFlow.
<a name="credits"></a>
## Credits