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Awesome Machine Learning with Ruby Awesome

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 solution from.

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.

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

Contents

:sparkles: Tutorials

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.

Frameworks

Neural networks

Kernel methods

Evolutionary algorithms

Bayesian methods

Decision trees

Clustering

Linear classifiers

Statistical models

Applications of machine learning

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

Projects and Code Examples

Heroku buildpacks

Books, Blogs, Channels

Community

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.

License

Creative Commons Zero 1.0 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/.