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https://github.com/arbox/machine-learning-with-ruby

Curated list: Resources for machine learning in Ruby
https://github.com/arbox/machine-learning-with-ruby

List: machine-learning-with-ruby

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Curated list: Resources for machine learning in Ruby

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README 

        
Awesome Machine Learning with Ruby



[![Awesome](https://awesome.re/badge-flat.svg)](https://github.com/sindresorhus/awesome#readme) [![Support Me](https://img.shields.io/badge/%F0%9F%92%97-Support%20Me-blue.svg?style=flat-square)](https://www.patreon.com/arbox)



[[RubyNLP](https://github.com/arbox/nlp-with-ruby) |

 [RubyDataScience](https://github.com/arbox/data-science-with-ruby) |

 [RubyInterop](https://github.com/arbox/ruby-interoperability)]



# Awesome Machine Learning with Ruby [][ruby]



> Curated List of Ruby Machine Learning Links and Resources



[Machine Learning][ml] is a field of [Computational Science][cs] -

often nested under [AI][ai] research - with many practical

applications due to the ability of resulting algorithms to

systematically implement a specific solution without explicit

programmer's instructions. Obviously many algorithms need a definition

of [features][fe] to look at or a biggish [training set][ts] of data to derive the

solution from.



This curated list comprises [_awesome_][awesome] libraries,

data sources, tutorials and presentations about [Machine Learning][ml]

utilizing the [Ruby][ruby] programming language.



A lot of useful resources on this list come from the development by

[The Ruby Science Foundation][sciruby], our [contributors][contributors] and

our own day to day work on various ML applications.



:sparkles: Every [contribution](contributing.md) is welcome! Add links through pull

requests or create an issue to start a discussion.



Follow us on [Twitter](https://twitter.com/NonWebRuby) and please spread

the word using the `#RubyML` hash tag!



## Contents



- [:sparkles: Tutorials](#sparkles-tutorials)

- [Machine Learning Libraries](#machine-learning-libraries)

  * [Frameworks](#frameworks)

  * [Neural networks](#neural-networks)

  * [Deep Learning](#deep-learning)

  * [Kernel methods](#kernel-methods)

  * [Evolutionary algorithms](#evolutionary-algorithms)

  * [Bayesian methods](#bayesian-methods)

  * [Decision trees](#decision-trees)

  * [Clustering](#clustering)

  * [Linear classifiers](#linear-classifiers)

  * [Statistical models](#statistical-models)

  * [Gradient boosting](#gradient-boosting)

  * [Vector search](#vector-search)

- [Applications of machine learning](#applications-of-machine-learning)

- [Data structures](#data-structures)

- [Data visualization](#data-visualization)

- [Articles, Posts, Talks, and Presentations](#articles-posts-talks-and-presentations)

- [Projects and Code Examples](#projects-and-code-examples)

- [Heroku buildpacks](#heroku-buildpacks)

- [Books, Blogs, Channels](#books-blogs-channels)

- [Community](#community)

- [Related Resources](#related-resources)

- [License](#license)



## :sparkles: Tutorials



Please help us to fill out this section! :smiley:

- [Ruby neural networks](https://www.honeybadger.io/blog/ruby-neural-networks/)

- [How to implement linear regression in Ruby](https://www.practicalai.io/implementing-linear-regression-using-ruby/)

  [[code](https://github.com/daugaard/example-linear-regression)]

- [How to implement classification using logistic regression in Ruby](https://www.practicalai.io/implementing-classification-using-logistic-regression-in-ruby/)

- [How to implement simple binary classification using a Neural Network in Ruby](https://www.practicalai.io/implementing-simple-classification-using-neural-network-in-ruby/)

  [[code](https://github.com/daugaard/example-neural-network)]

- [How to implement classification using a SVM in Ruby](https://www.practicalai.io/implementing-classification-using-a-svm-in-ruby/)

  [[code](https://github.com/daugaard/example-svm)]

- [Unsupervised learning using k-means clustering in Ruby](https://www.practicalai.io/unsupervised-learning-using-k-means-clustering-in-ruby/)

  [[code](https://github.com/daugaard/example-kmeans-clustering)]

- [Teaching an AI to play a simple game using Q-Learning in Ruby](https://www.practicalai.io/teaching-ai-play-simple-game-using-q-learning/)

  [[code](https://github.com/daugaard/q-learning-simple-game)]

- [Teaching a Neural Network to play a game using Q-Learning in Ruby](https://www.practicalai.io/teaching-a-neural-network-to-play-a-game-with-q-learning/)

  [[code](https://github.com/daugaard/q-learning-simple-game/tree/neuralnetwork)]

- [Using the Python scikit-learn machine learning library in Ruby using PyCall](https://www.practicalai.io/using-scikit-learn-machine-learning-library-in-ruby-using-pycall/)

  [[code](https://github.com/daugaard/scikit-learn-from-ruby)]

- [How to _evolve_ neural networks in Ruby using the Machine Learning Workbench](https://github.com/giuse/machine_learning_workbench/blob/master/examples/neuroevolution.rb)



## Machine Learning Libraries



[Machine Learning][ml] algorithms in pure Ruby or written in other

programming languages with appropriate bindings for Ruby.



### Frameworks



- [LangChain.rb](https://github.com/andreibondarev/langchainrb) -

  Build ML/AI-supercharged applications with Ruby's LangChain.

- [weka](https://github.com/paulgoetze/weka-jruby) -

  JRuby bindings for Weka, different ML algorithms implemented through Weka.

- [ai4r](https://github.com/SergioFierens/ai4r) -

  Artificial Intelligence for Ruby.

- [classifier-reborn](https://github.com/jekyll/classifier-reborn) -

  General classifier module to allow Bayesian and other types of classifications.

  [[dep: GLS](#gls)]

- [scoruby](https://github.com/asafschers/scoruby) -

  Ruby scoring API for [PMML](http://dmg.org/pmml/v4-3/GeneralStructure.html) (Predictive Model Markup Language).

- [rblearn](https://github.com/himkt/rblearn) - Feature Extraction and Crossvalidation library.

- [data_modeler](https://github.com/giuse/data_modeler) -

  Model your data with machine learning. Ample test coverage, examples to start fast, complete documentation. Production ready since 1.0.0.

- [shogun](https://github.com/shogun-toolbox/shogun) - Polyfunctional and mature

  machine learning toolbox with [Ruby bindings](https://github.com/shogun-toolbox/shogun/tree/develop/src/interfaces/ruby).

- [aws-sdk-machinelearning](https://github.com/aws/aws-sdk-ruby) -

  Machine Learning API of the Amazon Web Services.

- [azure_mgmt_machine_learning](https://github.com/Azure/azure-sdk-for-ruby) -

  Machine Learning API of the Microsoft Azure.

- [machine_learning_workbench](https://github.com/giuse/machine_learning_workbench) -

  Growing machine learning framework written in pure Ruby, high performance computing using

  [Numo](https://github.com/ruby-numo/), CUDA bindings through [Cumo](https://github.com/sonots/cumo).

  Currently implementating neural networks, evolutionary strategies, vector quantization, and plenty of

  examples and utilities.

- [Deep NeuroEvolution](https://github.com/giuse/DNE) -

  Experimental setup based on the [machine_learning_workbench](https://github.com/giuse/machine_learning_workbench)

  towards searching for deep neural networks (rather than training) using evolutionary algorithms. Applications to the

  [OpenAI Gym](https://github.com/openai/gym) using [PyCall](https://github.com/mrkn/pycall.rb).

- [rumale](https://github.com/yoshoku/rumale) -

  Machine Learninig toolkit in Ruby with wide range of implemented algorithms

  (SVM, Logistic Regression, Linear Regression, Random Forest etc.) and

  interfaces similar to [Scikit-Learn][scikit] in Python.

- [eps](https://github.com/ankane/eps) - Bayesian Classification and Linear Regression with exports

  using [PMML](http://dmg.org/pmml/v4-3/GeneralStructure.html) and an alternative backend using [GSL][gsl].

- [ruby-openai](https://github.com/alexrudall/ruby-openai) - OpenAI API wrapper



### Neural networks



- [neural-net-ruby](https://github.com/gbuesing/neural-net-ruby) -

  Neural network written in Ruby.

- [ruby-fann](https://github.com/tangledpath/ruby-fann) -

  Ruby bindings to the [Fast Artificial Neural Network Library (FANN)](http://leenissen.dk/fann/wp/).

- [cerebrum](https://github.com/irfansharif/cerebrum) -

  Experimental implementation for Artificial Neural Networks in Ruby.

- [tlearn-rb](https://github.com/josephwilk/tlearn-rb) -

  Recurrent Neural Network library for Ruby.

- [brains](https://github.com/jedld/brains-jruby) -

  Feed-forward neural networks for JRuby based on

  [brains](https://github.com/jedld/brains).

- [machine_learning_workbench](https://github.com/giuse/machine_learning_workbench/tree/master/lib/machine_learning_workbench/neural_network) -

  Framework including pure-Ruby implementation of both feed-forward and recurrent neural networks

  (fully connected). Training available using neuroevolution (Natural Evolution Strategies algorithms).

- [rann](https://github.com/mikecmpbll/rann) -

  Flexible Ruby ANN implementation with backprop (through-time, for recurrent

  nets), gradient checking, adagrad, and parallel batch execution.



### Deep learning



- [tensor_stream](https://github.com/jedld/tensor_stream) -

  Ground-up and standalone reimplementation of TensorFlow for Ruby.

- [red-chainer](https://github.com/red-data-tools/red-chainer) - Deep learning framework for Ruby.

- [tensorflow](https://github.com/somaticio/tensorflow.rb) - Ruby bindings for [TensorFlow](https://www.tensorflow.org/).

- [ruby-dnn](https://github.com/unagiootoro/ruby-dnn) - Simple deep learning for Ruby.

- [torch-rb](https://github.com/ankane/torch-rb) - Ruby bindings for [LibTorch](https://github.com/pytorch/pytorch)

  using [rice](https://github.com/jasonroelofs/rice).

- [mxnet](https://github.com/mrkn/mxnet.rb) - Ruby bindings for [mxnet](https://mxnet.apache.org/).



### Kernel methods



- [rb-libsvm](https://github.com/febeling/rb-libsvm) -

  Support Vector Machines with Ruby and the [LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) library.

  [[dep: bundled](#bundled)]



### Evolutionary algorithms



- [machine_learning_workbench](https://github.com/giuse/machine_learning_workbench/tree/master/lib/machine_learning_workbench/optimizer/natural_evolution_strategies) -

  Framework including pure-Ruby implementations of Natural Evolution Strategy algorithms

  (black-box optimization), specifically Exponential NES (XNES),

  Separable NES (sNES), Block-Diagonal NES (BDNES) and more.

  Applications include neural network search/training (neuroevolution).

- [simple_ga](https://github.com/giuse/simple_ga) -

  Simplest Genetic Algorithms implementation in Ruby.



### Bayesian methods



- [linnaeus](https://github.com/djcp/linnaeus) -

  Redis-backed Bayesian classifier.

- [naive_bayes](https://github.com/reddavis/Naive-Bayes) -

  Simple Naive Bayes classifier.

- [nbayes](https://github.com/oasic/nbayes) -

  Full-featured, Ruby implementation of Naive Bayes.



### Decision trees



- [decisiontree](https://github.com/igrigorik/decisiontree) -

  Decision Tree ID3 Algorithm in pure Ruby.

  [[dep: GraphViz](#graphviz) |

        [post](https://www.igvita.com/2007/04/16/decision-tree-learning-in-ruby/)].



### Clustering



- [kmeans-clusterer](https://github.com/gbuesing/kmeans-clusterer) -

  k-means clustering in Ruby.

- [k_means](https://github.com/reddavis/K-Means) -

  Attempting to build a fast, memory efficient K-Means program.

- [knn](https://github.com/reddavis/knn) -

  Simple K Nearest Neighbour Algorithm.



### Linear classifiers



- [liblinear-ruby-swig](https://github.com/tomz/liblinear-ruby-swig) -

  Ruby interface to LIBLINEAR (much more efficient than LIBSVM for text classification).

- [liblinear-ruby](https://github.com/kei500/liblinear-ruby) -

  Ruby interface to LIBLINEAR using SWIG.



### Statistical models



- [rtimbl](https://github.com/maspwr/rtimbl) -

  Memory based learners from the Timbl framework.

- [lda-ruby](https://github.com/ealdent/lda-ruby) -

  Ruby implementation of the [LDA](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation)

  (Latent Dirichlet Allocation) for automatic Topic Modelling and Document Clustering.

- [maxent_string_classifier](https://github.com/mccraigmccraig/maxent_string_classifier) -

  JRuby maximum entropy classifier for string data, based on the OpenNLP Maxent framework.

- [omnicat](https://github.com/mustafaturan/omnicat) -

  Generalized rack framework for text classifications.

- [omnicat-bayes](https://github.com/mustafaturan/omnicat-bayes) -

  Naive Bayes text classification implementation as an OmniCat classifier strategy.

  [[dep: bundled](#bundled)]



### Gradient boosting



- [xgboost](https://github.com/PairOnAir/xgboost-ruby) —

  Ruby bindings for XGBoost.

  [[dep: XGBoost](#xgboost)]

- [xgb](https://github.com/ankane/xgb) —

  Ruby bindings for XGBoost.

  [[dep: XGBoost](#xgboost)]

- [lightgbm](https://github.com/ankane/lightgbm) —

  Ruby bindings for LightGBM.

  [[dep: LightGBM](#lightgbm)]



### Vector search



- [flann](https://github.com/mariusmuja/flann) -

  Ruby bindings for the [FLANN](https://github.com/flann-lib/flann) (Fast Library for Approximate Nearest Neighbors).

  [[flann](#flann)]

- [annoy-rb](https://github.com/yoshoku/annoy.rb) -

  Ruby bindings for the [Annoy](https://github.com/spotify/annoy) (Approximate Nearest Neighbors Oh Yeah).

- [hnswlib.rb](https://github.com/yoshoku/hnswlib.rb) -

  Ruby bindings for the [Hnswlib](https://github.com/nmslib/hnswlib) that implements approximate nearest neighbor search with Hierarchical Navigable Small World graphs.

- [ngt-ruby](https://github.com/ankane/ngt-ruby) -

  Ruby bindings for the [NGT](https://github.com/yahoojapan/NGT) (Neighborhood Graph and Tree for Indexing High-dimensional data).

- [milvus](https://github.com/andreibondarev/milvus) —

  Ruby client for Milvus Vector DB.

- [pinecone](https://github.com/ScotterC/pinecone) —

  Ruby client for Pinecone Vector DB.

- [qdrant-ruby](https://github.com/andreibondarev/qdrant-ruby) —

  Ruby wrapper for the Qdrant vector search database API.

- [weaviate-ruby](https://github.com/andreibondarev/weaviate-ruby) —

  Ruby wrapper for the Weaviate vector search database API.



## Applications of machine learning



- [phashion](https://github.com/westonplatter/phashion) -

  Ruby wrapper around pHash, the perceptual hash library for detecting duplicate multimedia files.

  [[ImageMagick](#imagemagick) | [libjpeg](#libjpeg)]



## Data structures



If you're going to implement your own ML algorithms you're probably interested

in storing your feature sets efficiently. Look for appropriate

[data structures](https://github.com/arbox/data-science-with-ruby#data-structures)

in our [Data Science with Ruby][ds-with-ruby] list.



## Data visualization



Please refer to the [Data Visualization](https://github.com/arbox/data-science-with-ruby#visualization)

section on the [Data Science with Ruby][ds-with-ruby] list.



## Articles, Posts, Talks, and Presentations



- 2022

  - _Discover Machine Learning in Ruby_ by [Justin Bowen](https://twitter.com/TonsOfFun111)

   [[video](https://www.youtube.com/watch?v=HPbizNgcyFk)]

- 2019

  - _TensorStream: Bringing Machine Learning to Ruby_ by [Joseph Emmanuel Dayo](https://www.linkedin.com/in/jdayo/)

    [[post](https://medium.com/@joseph.dayo/tensorstream-bringing-machine-learning-to-ruby-114582060e3d)]

  - _Easy machine learning with Ruby using SVMKit_ by [@kojix](https://twitter.com/kojix2dayo)

    [[post](https://dev.to/kojix2/easy-machine-learning-with-ruby-using-svmkit-4n86)]

- 2018

  - _Deep Learning Programming on Ruby_ by [Kenta Murata](https://twitter.com/mrkn)

    & [Yusaku Hatanaka ](https://twitter.com/hatappi)

    [[slides](https://speakerdeck.com/mrkn/deep-learning-programming-on-ruby) |

          [page](https://rubykaigi.org/2018/presentations/mrkn.html)]

  - _How to use trained Keras and TensorFlow machine learning models within Ruby on Rails_ by [Denis Sellu](https://twitter.com/denis_sellu)

    [[post](https://www.cookieshq.co.uk/posts/how-to-use-trained-keras-and-tensorflow-machine-learning-models-within-ruby-on-rails)]

- 2017

  - _Scientific Computing on JRuby_ by [Prasun Anand](https://twitter.com/prasun_anand)

    [[slides](https://www.slideshare.net/PrasunAnand2/fosdem2017-scientific-computing-on-jruby) |

    [video](https://ftp.fau.de/fosdem/2017/K.4.201/ruby_scientific_computing_on_jruby.mp4) |

    [slides](https://www.slideshare.net/PrasunAnand2/scientific-computing-on-jruby) |

    [slides](https://www.slideshare.net/PrasunAnand2/scientific-computation-on-jruby)]

  - _Is it Food? An Introduction to Machine Learning_ by [Matthew Mongeau](https://twitter.com/halogenandtoast)

    [[video](https://www.youtube.com/watch?v=8G709hKkthY) |

          [slides](https://www.slideshare.net/halogenandtoast/is-it-food)]

  - _Bayes is BAE_ by [Richard Schneeman](https://twitter.com/schneems)

    [[video](https://www.youtube.com/watch?v=bQSzZrDDV80) |

          [slides](https://speakerdeck.com/schneems/bayes-is-bae)]

  - _Ruby Roundtable: Machine Learning in Ruby_ by [RubyThursday](https://rubythursday.com/)

    [[video](https://www.youtube.com/watch?v=ScIFARN0jCo)]

- 2016

  - _Practical Machine Learning with Ruby_ by [Jordan Hudgens](https://twitter.com/jordanhudgens)

    [[tutorial](https://www.crondose.com/2016/12/practical-machine-learning-ruby/)]

  - _Deep Learning: An Introduction for Ruby Developers_ by [Geoffrey Litt](https://twitter.com/geoffreylitt)

    [[slides](https://speakerdeck.com/geoffreylitt/deep-learning-an-introduction-for-ruby-developers)]

  - _How I made a pure-Ruby word2vec program more than 3x faster_ by [Kei Sawada](https://twitter.com/remore)

    [[slides](https://speakerdeck.com/remore/how-i-made-a-pure-ruby-word2vec-program-more-than-3x-faster)]

  - _Dōmo arigatō, Mr. Roboto: Machine Learning with Ruby_ by [Eric Weinstein](https://twitter.com/ericqweinstein)

    [[slides](https://speakerdeck.com/ericqweinstein/domo-arigato-mr-roboto-machine-learning-with-ruby) |

          [video](https://www.youtube.com/watch?v=T1nFQ49TyeA)]

  - _Building a Recommendation Engine with Machine Learning Techniques_ by [Brian Sam-Bodden](https://twitter.com/bsbodden)

    [[video](https://www.youtube.com/watch?v=SRnM_P_ygqI)]

  - :sparkles: _SciRuby Machine Learning: Current Status and Future_ by [Kenta Murata](https://twitter.com/mrkn)

    [[slides](https://speakerdeck.com/mrkn/sciruby-machine-learning-current-status-and-future) |

          [video: jp](https://www.youtube.com/watch?v=gfQ8XEy7vO4)]

  - _Ruby Roundtable: Intro to Tensorflow_ by [RubyThursday](https://rubythursday.com/)

    [[video](https://www.youtube.com/watch?v=pYC5mXHUWkc)]

- 2015

  - _Machine Learning made simple with Ruby_ by [Lorenzo Masini](https://twitter.com/rugginoso)

    [[post](https://www.leanpanda.com/blog/2015-08-24-machine-learning-automatic-classification/)]

  - _Using Ruby Machine Learning to Find Paris Hilton Quotes_ by [Rick Carlino](https://github.com/RickCarlino)

    [[tutorial](https://web.archive.org/web/20160414072324/http://datamelon.io/blog/2015/using-ruby-machine-learning-id-paris-hilton-quotes.html)]

- 2014

  - _Test Driven Neural Networks_ by [Matthew Kirk](https://twitter.com/mjkirk)

    [[video](https://www.youtube.com/watch?v=ppf8m-3uXvU&t=36s)]

  - _Five machine learning techniques that you can use in your Ruby apps today_ by [Benjamin Curtis](https://twitter.com/stympy)

    [[video](https://www.youtube.com/watch?v=crziu7dk6Vw) |

          [slides](https://speakerdeck.com/stympy/machine-learning-techniques)]

  - _Machine Learning for Fun and Profit_ by [John Paul Ashenfelter](https://twitter.com/johnashenfelter)

    [[video](https://www.youtube.com/watch?v=KC5MtKHm1O4)]

- 2013

  - _Sentiment Analysis using Support Vector Machines in Ruby_ by [Matthew Kirk](https://twitter.com/mjkirk)

    [[video](https://www.youtube.com/watch?v=iSug6CgxWxc) |

          [code](https://github.com/hexgnu/sentiment_analyzer)]

  - _Recommender Systems with Ruby_ by [Marcel Caraciolo](https://twitter.com/marcelcaraciolo)

    [[slides](https://www.slideshare.net/marcelcaraciolo/recommender-systems-with-ruby-adding-machine-learning-statistics-etc)]

  - _Detecting Faces with Ruby: FFI in a Nutshell_ by [Marc Berszick]()

    [[post](https://www.sitepoint.com/detecting-faces-with-ruby-ffi-in-a-nutshell/)]

- 2012

  - _Machine Learning with Ruby, Part One_ by [Vasily Vasinov](https://twitter.com/vasinov)

    [[tutorial](https://www.vasinov.com/blog/machine-learning-with-ruby-part-one/)]

  - _Recurrent Neural Networks in Ruby_ by [Joseph Wilk](https://twitter.com/josephwilk)

    [[post](http://blog.josephwilk.net/ruby/recurrent-neural-networks-in-ruby.html)]

  - _Recommendation Engines using Machine Learning, and JRuby_ by [Matthew Kirk](https://twitter.com/mjkirk)

    [[video](https://www.youtube.com/watch?v=hsZcrlbBg_0)]

  - _Practical Machine Learning and Rails_ by [Andrew Cantino](https://twitter.com/tectonic)

    and [Ryan Stout](https://twitter.com/ryanstout)

    [[video](https://www.youtube.com/watch?v=vy_zQ1-F0JI)]



- 2011

  - _Clustering in Ruby_ by [Colin Drake](https://twitter.com/colinfdrake)

    [[post](https://colindrake.me/post/k-means-clustering-in-ruby/)]

  - _Text Classification using Support Vector Machines in Ruby_ by [Rimas Silkaitis](https://twitter.com/neovintage)

    [[post](http://neovintage.org/2011/11/14/text-classification-using-support/)]

- 2010

  - _bayes_motel – Bayesian classification for Ruby_ by [Mike Perham](https://twitter.com/mperham)

    [[post](http://www.mikeperham.com/2010/04/28/bayes_motel-bayesian-classification-for-ruby/)]

  - _Intelligent Ruby: Getting Started with Machine Learning_ by [Ilya Grigorik](https://twitter.com/igrigorik)

    [[video](https://vimeo.com/22513786)]

- 2009



- 2008

  - _Support Vector Machines (SVM) in Ruby_ by [Ilya Grigorik](https://twitter.com/igrigorik)

    [[post](https://www.igvita.com/2008/01/07/support-vector-machines-svm-in-ruby/)]

- 2007

  - _Decision Tree Learning in Ruby_ by [Ilya Grigorik](https://twitter.com/igrigorik)

    [[post](https://www.igvita.com/2007/04/16/decision-tree-learning-in-ruby/)]



## Projects and Code Examples



- [Wine Clustering](https://github.com/hexgnu/wine_clustering) -

  Wine quality estimations clustered with different algorithms.

- [simple_ga](https://github.com/giuse/simple_ga) -

  Basic (working) demo of Genetic Algorithms in Ruby.

- [Handwritten Digits Recognition](https://github.com/jdrzj/handwritten-digits-recognition) -

  Handwritten digits recognition using Neural Networks and Ruby.



## Heroku buildpacks



- [GSL and Ruby buildpack](https://github.com/tomwolfe/heroku-buildpack-gsl-ruby)

- [OpenCV and Ruby buildpack](https://github.com/lilibethdlc/heroku-buildpack-ruby-opencv)

- [ImageMagick buildpack](https://github.com/mcollina/heroku-buildpack-imagemagick)



## Books, Blogs, Channels



-  [Kirk, Matthew](https://twitter.com/mjkirk).

   _Thoughtful Machine Learning: A Test-Driven Approach_. O'Reilly, 2014.

   [[Amazon](https://www.amazon.com/Thoughtful-Machine-Learning-Test-Driven-Approach/dp/1449374069) |

         [code](https://github.com/thoughtfulml/examples)]

- [Practical Artificial Intelligence](https://www.practicalai.io/) -

  Blog about Artificial Intelligence and Machine Learning with tutorials and code samples in Ruby.



## Community



- [SciRuby Mailing List](https://groups.google.com/forum/#!forum/sciruby-dev)

- [SciRuby Slack](https://sciruby.slack.com/)

- [Red Data Gitter](https://gitter.im/red-data-tools/)

- [Reddit](https://www.reddit.com/r/MachineLearning/search?q=Ruby&restrict_sr=on)

- [Stack Overflow](https://stackoverflow.com/search?q=machine+learning+ruby)

- [Twitter](https://twitter.com/search?q=Machine%20Learning%20Ruby&src=typd)

- [NonWebRuby](https://twitter.com/NonWebRuby)

- [Ruby AI Builders Discord](https://discord.gg/zDyFJFBTGB)

- [X Ruby AI group](https://twitter.com/i/communities/1709211359039078677)

- [Mastodon Ruby AI and Data group](https://ruby.social/@[email protected])



## Related Resources



- 

  [LightGBM](https://github.com/microsoft/LightGBM)

- 

  [XGBoost](https://github.com/dmlc/xgboost)

- 

  [GSL (GNU Scientific Library)][gls]

- 

  [OpenCV](https://opencv.org/)

- 

  [Graphviz](http://www.graphviz.org/)

- 

  [Gnuplot](http://www.gnuplot.info/)

- 

  [X11/XQuartz](https://www.xquartz.org/)

- 

  [ImageMagick](https://www.imagemagick.org/script/index.php)

- 

  [R](http://www.r-project.org/)

- 

  [Octave](https://www.gnu.org/software/octave/)

- [scikit-learn algorithm cheatsheet](https://scikit-learn.org/stable/tutorial/machine_learning_map/)

- [Awesome Ruby](https://github.com/markets/awesome-ruby#natural-language-processing) -

  Among other awesome items a short list of NLP related projects.

- [Ruby NLP](https://github.com/diasks2/ruby-nlp) -

  State-of-Art collection of Ruby libraries for NLP.

- [Speech and Natural Language Processing](https://github.com/edobashira/speech-language-processing) -

  General List of NLP related resources (mostly not for Ruby programmers).

- [Scientific Ruby](http://sciruby.com/) -

  Linear Algebra, Visualization and Scientific Computing for Ruby.

- [iRuby](https://github.com/SciRuby/iruby) - IRuby kernel for Jupyter (formerly IPython).

- [Kiba](https://github.com/thbar/kiba) -

  Lightweight [ETL](https://en.wikipedia.org/wiki/Extract,_transform,_load) (Extract, Transform, Load) pipeline.

- [Awesome OCR](https://github.com/kba/awesome-ocr) -

  Multitude of OCR (Optical Character Recognition) resources.

- [Awesome TensorFlow](https://github.com/jtoy/awesome-tensorflow) -

  Machine Learning with TensorFlow libraries.

- [rb-gsl](https://github.com/SciRuby/rb-gsl) -

  Ruby interface to the [GNU Scientific Library](https://www.gnu.org/software/gsl/).

- [The Definitive Guide to Ruby's C API](https://silverhammermba.github.io/emberb/) -

  Modern Reference and Tutorial on Embedding and Extending Ruby using C programming language.



## License



[![Creative Commons Zero 1.0](http://mirrors.creativecommons.org/presskit/buttons/80x15/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/)

`Awesome ML with Ruby` by [Andrei Beliankou](https://github.com/arbox) and

[Contributors][contributors].



To the extent possible under law, the person who associated CC0 with

`Awesome ML with Ruby` has waived all copyright and related or neighboring rights

to `Awesome ML with Ruby`.



You should have received a copy of the CC0 legalcode along with this

work. If not, see .



[ruby]: https://www.ruby-lang.org/en/

[awesome]: https://github.com/sindresorhus/awesome/blob/master/awesome.md

[change-pr]: https://github.com/RichardLitt/knowledge/blob/master/github/amending-a-commit-guide.md

[ml]: https://en.wikipedia.org/wiki/Machine_learning

[ds-with-ruby]: https://github.com/arbox/data-science-with-ruby

[contributors]: https://github.com/arbox/machine-learning-with-ruby/graphs/contributors

[sciruby]: https://github.com/sciruby

[ai]: https://en.wikipedia.org/wiki/Artificial_intelligence

[cs]: https://en.wikipedia.org/wiki/Computational_science

[fe]: https://en.wikipedia.org/wiki/Feature_engineering

[ts]: https://en.wikipedia.org/wiki/Test_set

[gsl]: https://www.gnu.org/software/gsl/

[scikit]: https://scikit-learn.org/stable/index.html