Machine Learning is a field of Computational Science -often nested under AI research - with many practicalapplications due to the ability of resulting algorithms tosystematically implement a specific solution without explicitprogrammer's instructions. Obviously many algorithms need a definitionof features to look at or a biggish training set of data to derive thesolution from.

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

A lot of useful resources on this list come from the development byThe Ruby Science Foundation, our contributors andour own day to day work on various ML applications.

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

✨ Tutorials

Please help us to fill out this section! ��

Machine Learning Libraries

Machine Learning algorithms in pure Ruby or written in otherprogramming languages with appropriate bindings for Ruby.

Frameworks

weka -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]}
scoruby -Ruby scoring API for PMML (Predictive Model Markup Language).
rblearn - Feature Extraction and Crossvalidation library.
data_modeler -Model your data with machine learning. Ample test coverage, examples to start fast, complete documentation. Production ready since 1.0.0.
shogun - Polyfunctional and maturemachine learning toolbox with Ruby bindings.
aws-sdk-machinelearning -Machine Learning API of the Amazon Web Services.
azure_mgmt_machine_learning -Machine Learning API of the Microsoft Azure.
machine_learning_workbench -Growing machine learning framework written in pure Ruby, high performance computing usingNumo, CUDA bindings through Cumo.Currently implementating neural networks, evolutionary strategies, vector quantization, and plenty ofexamples and utilities.
Deep NeuroEvolution -Experimental setup based on the machine_learning_workbenchtowards searching for deep neural networks (rather than training) using evolutionary algorithms. Applications to theOpenAI Gym using PyCall.
rumale -Machine Learninig toolkit in Ruby with wide range of implemented algorithms(SVM, Logistic Regression, Linear Regression, Random Forest etc.) andinterfaces similar to Scikit-Learn in Python.
eps - Bayesian Classification and Linear Regression with exportsusing PMML and an alternative backend using GSL.

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 onbrains.
machine_learning_workbench -Framework including pure-Ruby implementation of both feed-forward and recurrent neural networks(fully connected). Training available using neuroevolution (Natural Evolution Strategies algorithms).
rann -Flexible Ruby ANN implementation with backprop (through-time, for recurrentnets), gradient checking, adagrad, and parallel batch execution.

Deep learning

tensor_stream -Ground-up and standalone reimplementation of TensorFlow for Ruby.
red-chainer - Deep learning framework for Ruby.
tensorflow - Ruby bindings for TensorFlow.
ruby-dnn - Simple deep learning for Ruby.
torch-rb - Ruby bindings for LibTorchusing rice.
mxnet - Ruby bindings for mxnet.

Kernel methods

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

Evolutionary algorithms

machine_learning_workbench -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 -Simplest Genetic Algorithms implementation in Ruby.

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.
annoy-rb -bindings for the Annoy (Approximate Nearest Neighbors Oh Yeah).

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]}

Gradient boosting

xgboost —Ruby bindings for XGBoost.^{[dep: XGBoost]}
xgb —Ruby bindings for XGBoost.^{[dep: XGBoost]}
lightgbm —Ruby bindings for LightGBM.^{[dep: LightGBM]}

Applications of machine learning

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 interestedin storing your feature sets efficiently. Look for appropriatedata structuresin our Data Science with Ruby list.

Data visualization

Please refer to the Data Visualizationsection on the Data Science with Ruby list.

Articles, Posts, Talks, and Presentations

2019
- TensorStream: Bringing Machine Learning to Ruby by Joseph Emmanuel Dayo^[post]
- Easy machine learning with Ruby using SVMKit by @kojix^[post]
2018
- Deep Learning Programming on Ruby by Kenta Murata& Yusaku Hatanaka ^{[slides |page]}
- How to use trained Keras and TensorFlow machine learning models within Ruby on Rails by Denis Sellu^[post]
2017
- Scientific Computing on JRuby by Prasun Anand^{[slides |video |slides |slides]}
- Is it Food? An Introduction to Machine Learning by Matthew Mongeau^{[video |slides]}
- Bayes is BAE by Richard Schneeman^{[video |slides]}
- Ruby Roundtable: Machine Learning in Ruby by RubyThursday^[video]
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]}
- Ruby Roundtable: Intro to Tensorflow by RubyThursday^[video]
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]
- Detecting Faces with Ruby: FFI in a Nutshell by Marc Berszick^[post]
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]
- Practical Machine Learning and Rails by Andrew Cantinoand Ryan Stout^[video]
2011
- Clustering in Ruby by Colin Drake^[post]
- Text Classification using Support Vector Machines in Ruby by Rimas Silkaitis^[post]
2010
- bayes_motel – Bayesian classification for Ruby by Mike Perham^[post]
- Intelligent Ruby: Getting Started with Machine Learning by Ilya Grigorik^[video]
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 of Genetic Algorithms in Ruby.
Handwritten Digits Recognition -Handwritten digits recognition using Neural Networks and Ruby.

Heroku buildpacks

Books, Blogs, Channels

Kirk, Matthew.Thoughtful Machine Learning: A Test-Driven Approach. O'Reilly, 2014.^{[Amazon |code]}
Practical Artificial Intelligence -Blog about Artificial Intelligence and Machine Learning with tutorials and code samples in Ruby.

Community

Related Resources

LightGBM
XGBoost
[GSL (GNU Scientific Library)][gls]
OpenCV
Graphviz
Gnuplot
X11/XQuartz
ImageMagick
R
Octave
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 (formerly 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.

License

Awesome ML with Ruby by Andrei Beliankou andContributors.

To the extent possible under law, the person who associated CC0 withAwesome ML with Ruby has waived all copyright and related or neighboring rightsto Awesome ML with Ruby.

You should have received a copy of the CC0 legalcode along with thiswork. If not, see https://creativecommons.org/publicdomain/zero/1.0/.

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Awesome Machine Learning with Ruby

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