From fad4c14998d59646a0f45a27c35178235ef5cebf Mon Sep 17 00:00:00 2001 From: Aaron Meese Date: Tue, 31 Oct 2017 20:40:56 -0500 Subject: [PATCH] Update README.md Started to carry period style throughout (it kept switching and it looked unprofessional) --- README.md | 276 +++++++++++++++++++++++++++--------------------------- 1 file changed, 138 insertions(+), 138 deletions(-) diff --git a/README.md b/README.md index 366de27..949f770 100644 --- a/README.md +++ b/README.md @@ -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. @@ -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. ## Java @@ -351,10 +351,10 @@ Further resources: #### 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. #### Neural Networks @@ -1026,23 +1026,23 @@ be #### 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. #### 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. #### 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. #### 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 #### 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. ## 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. @@ -1317,7 +1317,7 @@ be #### 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. ## Credits