ML with Ruby

GitHub repository ↗ synced 2026/8/9

[RubyNLP |
RubyDataScience |
RubyInterop]

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

:sparkles: Tutorials

Ruby neural networks

How to implement linear regression in Ruby

How to implement classification using logistic regression in Ruby

How to implement simple binary classification using a Neural Network in Ruby

How to implement classification using a SVM in Ruby

Unsupervised learning using k-means clustering in Ruby

Teaching an AI to play a simple game using Q-Learning in Ruby

Teaching a Neural Network to play a game using Q-Learning in Ruby

Using the Python scikit-learn machine learning library in Ruby using PyCall

How to _evolve_ neural networks in Ruby using the Machine Learning Workbench

Please help us to fill out this section! :smiley:
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Machine Learning Libraries

[Machine Learning][ml] algorithms in pure Ruby or written in other
programming languages with appropriate bindings for Ruby.

Frameworks

rblearn

Feature Extraction and Crossvalidation library.

shogun

Polyfunctional and mature

eps

Bayesian Classification and Linear Regression with exports

ruby-openai

OpenAI API wrapper

Instruct

Inspired by Guidance; weave code, prompts and completions together to instruct LLMs to do what you want.

LangChain.rb -
Build ML/AI-supercharged applications with Ruby's LangChain.
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).
data_modeler -
Model your data with machine learning. Ample test coverage, examples to start fast, complete documentation. Production ready since 1.0.0.
machine 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 using
Numo, CUDA bindings through Cumo.
Currently implementating neural networks, evolutionary strategies, vector quantization, and plenty of
examples and utilities.
Deep NeuroEvolution -
Experimental setup based on the machine_learning_workbench
towards searching for deep neural networks (rather than training) using evolutionary algorithms. Applications to the
OpenAI Gym using PyCall.
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.
using PMML and an alternative backend using [GSL][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 on
brains.
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 recurrent
nets), gradient checking, adagrad, and parallel batch execution.

Deep learning

red-chainer

Deep learning framework for Ruby.

tensorflow

Ruby bindings for [TensorFlow](https://www.tensorflow.org/).

ruby-dnn

Simple deep learning for Ruby.

torch-rb

Ruby bindings for [LibTorch](https://github.com/pytorch/pytorch)

mxnet

Ruby bindings for [mxnet](https://mxnet.apache.org/).

tensor_stream -
Ground-up and standalone reimplementation of TensorFlow for Ruby.
using rice.

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

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]

Gradient boosting

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

Vector search

flann -
Ruby bindings for the FLANN (Fast Library for Approximate Nearest Neighbors).
[flann]
annoy-rb -
Ruby bindings for the Annoy (Approximate Nearest Neighbors Oh Yeah).
hnswlib.rb -
Ruby bindings for the Hnswlib that implements approximate nearest neighbor search with Hierarchical Navigable Small World graphs.
ngt-ruby -
Ruby bindings for the NGT (Neighborhood Graph and Tree for Indexing High-dimensional data).
milvus —
Ruby client for Milvus Vector DB.
pinecone —
Ruby client for Pinecone Vector DB.
qdrant-ruby —
Ruby wrapper for the Qdrant vector search database API.
weaviate-ruby —
Ruby wrapper for the Weaviate vector search database API.

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 interested
in storing your feature sets efficiently. Look for appropriate
data structures
in our [Data Science with Ruby][ds-with-ruby] list.

Data visualization

Please refer to the Data 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
  [video]
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]
- :sparkles: 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 Cantino
  and 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

GSL and Ruby buildpack

OpenCV and Ruby buildpack

ImageMagick buildpack

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.