Curated list of awesome lists
Awesome Machine Learning with Ruby
A Curated List of Ruby Machine Learning Links and Resources
Machine Learning is a field of Computational Science -
often nested under 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 to look at or a biggish training set of data to derive the
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.
:sparkles: Every contribution is welcome! Add links through pull
requests or create an issue to start a discussion.
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the word using the
#RubyML hash tag!
Please help us to fill out this section! :smiley:
Machine Learning Libraries
Machine Learning algorithms in pure Ruby or written in other
programming languages with appropriate bindings for Ruby.
JRuby bindings for Weka, different ML algorithms implemented through Weka.
Artificial Intelligence for Ruby.
General classifier module to allow Bayesian and other types of classifications.
Ruby scoring API for PMML (Predictive Model Markup Language).
rblearn - Feature Extraction and Crossvalidation library.
Model your data with machine learning. Ample test coverage, examples to start fast, complete documentation. Production ready since 1.0.0.
shogun - Polyfunctional and mature
machine learning toolbox with Ruby bindings
and enormous documentation.
Machine Learning API of the Amazon Web Services.
Machine Learning API of the Microsoft Azure.
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.
Neural network written in Ruby.
Ruby bindings to the Fast Artificial Neural Network Library (FANN).
Experimental implementation for Artificial Neural Networks in Ruby.
Recurrent Neural Network library for Ruby.
Feed-forward neural networks for JRuby based on
Framework including pure-Ruby implementation of both feed-forward and recurrent neural networks
(fully connected). Training available using neuroevolution (Natural Evolution Strategies algorithms).
Flexible Ruby ANN implementation with backprop (through-time, for recurrent
nets), gradient checking, adagrad, and parallel batch execution.
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).
Simplest Genetic Algorithms implementation in Ruby.
Redis-backed Bayesian classifier.
Simple Naive Bayes classifier.
Full-featured, Ruby implementation of Naive Bayes.
Fast Library for Approximate Nearest Neighbors.
k-means clustering in Ruby.
Attempting to build a fast, memory efficient K-Means program.
Simple K Nearest Neighbour Algorithm.
Ruby interface to LIBLINEAR (much more efficient than LIBSVM for text classification).
Ruby interface to LIBLINEAR using SWIG.
Memory based learners from the Timbl framework.
Ruby implementation of the LDA
(Latent Dirichlet Allocation) for automatic Topic Modelling and Document Clustering.
JRuby maximum entropy classifier for string data, based on the OpenNLP Maxent framework.
Generalized rack framework for text classifications.
Naive Bayes text classification implementation as an OmniCat classifier strategy.
Applications of machine learning
Ruby wrapper around pHash, the perceptual hash library for detecting duplicate multimedia files.
[ImageMagick | libjpeg]
If you're going to implement your own ML algorithms you're probably interested
in storing your feature sets efficiently. Look for appropriate
in our Data Science with Ruby list.
Please refer to the Data Visualization
section on the Data Science with Ruby list.
Articles, Posts, Talks, and Presentations
Projects and Code Examples
Wine Clustering -
Wine quality estimations clustered with different algorithms.
Basic (working) demo of Genetic Algorithms in Ruby.
Books, Blogs, Channels
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.
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.
Awesome ML with Ruby by Andrei Beliankou and
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Awesome ML with Ruby has waived all copyright and related or neighboring rights
Awesome ML with Ruby.
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