[RubyNLP | RubyDataScience | RubyInterop]

Awesome Machine Learning with Ruby

A Curated List of Ruby Machine Learning Links and Resources

This curated list comprises awesome libraries, data sources, tutorials and presentations about Machine Learning utilizing the Ruby programming language.

A lot of useful resources on this list come from the development by The Ruby Science Foundation, our contributors and our own day to day work on various ML applications. Read why this list is awesome.

✨ Every contribution is welcome! Add links through pull requests or create an issue to start a discussion.

Follow us on Twitter and please spread the word using the #RubyML hash tag!

Contents

• ✨ Tutorials
• Machine Learning Libraries
  • Frameworks
  • Neural networks
  • Kernel methods
  • Bayesian methods
  • Decision trees
  • Clustering
  • Linear classifiers
  • Statistical models
• Applications of machine learning
• Data structures
• Data visualization
• Articles, Posts, Talks, and Presentations
• Projects and Code Examples
• Heroku buildpacks
• Books
• Community
• Needs your Help!
• Related Resources
• Contributing
• Wait but why?
• License

✨ Tutorials

Please help us to fill out this section! 😃

Machine Learning Libraries

Machine Learning Algorithms in pure Ruby or written in other programming languages with appropriate bindings for Ruby.

Frameworks

• weka-jruby - JRuby bindings for Weka, different ML algorithms implemented through Weka.
• ai4r - Artificial Intelligence for Ruby.
• classifier-reborn - General classifier module to allow Bayesian and other types of classifications. ^{[dep: GLS]}

Neural networks

• neural-net-ruby - Neural network written in Ruby.
• ruby-fann - Ruby bindings to the Fast Artificial Neural Network Library (FANN).
• cerebrum - Experimental implementation for Artificial Neural Networks in Ruby.
• tlearn-rb - Recurrent Neural Network library for Ruby.
• brains - Feed-forward neural networks for JRuby based on brains.
• neuroevo - Pure Ruby evolutional implementation of both feed-forward and recurrent, fully-connected neural networks (via xNES and SNES).

Kernel methods

• rb-libsvm - Support Vector Machines with Ruby and the LIBSVM library. ^{[dep: bundled]}

Bayesian methods

• linnaeus - Redis-backed Bayesian classifier.
• naive_bayes - Simple Naive Bayes classifier.
• nbayes - Full-featured, Ruby implementation of Naive Bayes.

Decision trees

• decisiontree - Decision Tree ID3 Algorithm in pure Ruby. ^{[dep: GraphViz | post]}.

Clustering

• flann - Fast Library for Approximate Nearest Neighbors. ^[flann]
• kmeans-clusterer - k-means clustering in Ruby.
• k_means - Attempting to build a fast, memory efficient K-Means program.
• knn - Simple K Nearest Neighbour Algorithm.

Linear classifiers

• liblinear-ruby-swig - Ruby interface to LIBLINEAR (much more efficient than LIBSVM for text classification).
• liblinear-ruby - Ruby interface to LIBLINEAR using SWIG.

Statistical models

• rtimbl - Memory based learners from the Timbl framework.
• lda-ruby - Ruby implementation of the LDA (Latent Dirichlet Allocation) for automatic Topic Modelling and Document Clustering.
• maxent_string_classifier - JRuby maximum entropy classifier for string data, based on the OpenNLP Maxent framework.
• omnicat - Generalized rack framework for text classifications.
• omnicat-bayes - Naive Bayes text classification implementation as an OmniCat classifier strategy. ^{[dep: bundled]}

Applications of machine learning

• scoruby - Ruby scoring API for PMML (Predictive Model Markup Language).
• phashion - Ruby wrapper around pHash, the perceptual hash library for detecting duplicate multimedia files. ^{[ImageMagick | 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 in our Data Science with Ruby list.

Data visualization

Please refer to the Data Visualization section on the Data Science with Ruby list.

Articles, Posts, Talks, and Presentations

• 2017

  • Scientific Computing on JRuby by Prasun Anand ^{[slides | video | slides | slides]}
  • Is it Food? An Introduction to Machine Learning by Matthew Mongeau ^[video]
  • Bayes is BAE by Richard Schneeman ^{[video | slides]}

• 2016

  • Practical Machine Learning with Ruby by Jordan Hudgens ^[tutorial]
  • Deep Learning: An Introduction for Ruby Developers by Geoffrey Litt ^[slides]
  • How I made a pure-Ruby word2vec program more than 3x faster by Kei Sawada ^[slides]
  • Dōmo arigatō, Mr. Roboto: Machine Learning with Ruby by Eric Weinstein ^{[slides | video]}
  • Building a Recommendation Engine with Machine Learning Techniques by Brian Sam-Bodden ^[video]
  • ✨ SciRuby Machine Learning: Current Status and Future by Kenta Murata ^{[slides | video: jp]}

• 2015

  • Machine Learning made simple with Ruby by Lorenzo Masini ^[post]
  • Using Ruby Machine Learning to Find Paris Hilton Quotes by Rick Carlino ^[tutorial]

• 2014

  • Test Driven Neural Networks by Matthew Kirk ^[video]
  • Five machine learning techniques that you can use in your Ruby apps today by Benjamin Curtis ^{[video | slides]}
  • Machine Learning for Fun and Profit by John Paul Ashenfelter ^[video]

• 2013

  • Sentiment Analysis using Support Vector Machines in Ruby by Matthew Kirk ^{[video | code]}
  • Recommender Systems with Ruby by Marcel Caraciolo ^[slides]

• 2012

  • Machine Learning with Ruby, Part One by Vasily Vasinov ^[tutorial]
  • Recurrent Neural Networks in Ruby by Joseph Wilk ^[post]
  • Recommendation Engines using Machine Learning, and JRuby by Matthew Kirk ^[video]

• 2011

  • Clustering in Ruby by Colin Drake ^[post]

• 2010

  • bayes_motel – Bayesian classification for Ruby by Mike Perham ^[post]

• 2009

• 2008

  • Support Vector Machines (SVM) in Ruby by Ilya Grigorik ^[post]

• 2007

  • Decision Tree Learning in Ruby by Ilya Grigorik ^[post]

Projects and Code Examples

• Wine Clustering - Wine quality estimations clustered with different algorithms.
• simple_ga - Basic (working) demo program on Genetic Algorithms in Ruby.

Heroku buildpacks

• GSL and Ruby buildpack
• OpenCV and Ruby buildpack
• ImageMagick buildpack

Books

• Kirk, Matthew. Thoughtful Machine Learning: A Test-Driven Approach. O'Reilly, 2014. ^{[Amazon | code]}

Community

• Reddit
• Stack Overflow
• Twitter

Needs your Help!

All projects in this section are really important for the community but need more attention. Please if you have spare time and dedication spend some hours on the code here.

Related Resources

• GSL (GNU Scientific Library) brew install gsl
• OpenCV brew tap homebrew/science && brew install opencv
• Graphviz brew install graphviz
• Gnuplot brew install gnuplot --with-x11
• X11/XQuartz
• ImageMagick && libjpeg brew install imagemagick && brew install libjpeg
• R brew tap homebrew/science && brew install r
• Octave brew tap homebrew/science && brew install octave --without-docs
• scikit-learn algorithm cheatsheet
• Awesome Ruby - Among other awesome items a short list of NLP related projects.
• Ruby NLP - State-of-Art collection of Ruby libraries for NLP.
• Speech and Natural Language Processing - General List of NLP related resources (mostly not for Ruby programmers).
• Scientific Ruby - Linear Algebra, Visualization and Scientific Computing for Ruby.
• iRuby - IRuby kernel for Jupyter (formelly IPython).
• Kiba - Lightweight ETL (Extract, Transform, Load) pipeline.
• Awesome OCR - Multitude of OCR (Optical Character Recognition) resources.
• Awesome TensorFlow - Machine Learning with TensorFlow libraries.
• rb-gsl - Ruby interface to the GNU Scientific Library.
• The Definitive Guide to Ruby's C API - Modern Reference and Tutorial on Embedding and Extending Ruby using C programming language.

Contributing

✨ We are very glad to see you in this section! ✨

Before you go ahead you should know that your contributions will be published under the terms of the CC0 license.

Have you just found a spelling error? Need to fix indentation? Send us a pull request! ✨

You have discovered something cool, but don't know exactly if it suits the awesome definition? 👍 Submit a pull request to the inbox! We'll consider your addition and format it for you! 😃

💥 If you know something pretty cool, it suits the awesome definition, works for you and seems to be useful for the community please add it to the main list. In this case please ensure your pull request adheres to the following guidelines:

• Use one commit per addition (several commits per PR are OK).
• Add everything to the bottom of the relevant category.
• If required introduce new categories or improve the existing categorization.
• Use the following format: - [title](link) - Distinguishing Description. <sup>[additional links]</sup>
• Name the libraries exactly how they are required from the Ruby program, not after the Readme's title, e.g. treat, not Treat, since you do require "treat".
• Break down long lines, check your spelling, capitalization, and punctuation.
• Provide a useful titles and comments for your Pull Request (not Changed readme.md), mention the originator in the commit message if possible, e.g. Added the neuroevo lib by @giuse.
• Add the topic rubyml to your repository or open an issue and kindly ask the originator of the project to do so (if applicable for your submission).

Sometimes we will ask you to edit your Pull Request before it is included. This is normally due to spelling errors or because your PR didn't match these guidelines.

Here is a write up on how to change a Pull Request and the different ways you can do that.

👍 Thank you for your suggestions!

Wait but why?

There are a lot of software lists with ML related tools. There are a couple of lists with Ruby related projects. There are no lists of only working and tested software with documented scope. We'll try to make one!

What is awesome? Awesome are documented, maintained and focused tools.

Can something turn not awesome at a point? Yes! Abandoned projects with broken dependencies aren't awesome any more! They leave this list.

License

Awesome ML with Ruby by Andrei Beliankou and 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 https://creativecommons.org/publicdomain/zero/1.0/.