[](https://github.com/sindresorhus/awesome#readme) [](https://www.patreon.com/arbox) [[RubyNLP](https://github.com/arbox/nlp-with-ruby) | [RubyDataScience](https://github.com/arbox/data-science-with-ruby) | [RubyInterop](https://github.com/arbox/ruby-interoperability)] # 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](contributing.md) is welcome! Add links through pull requests or create an issue to start a discussion. Follow us on [Twitter](https://twitter.com/NonWebRuby) and please spread the word using the `#RubyML` hash tag! ## Contents - [:sparkles: Tutorials](#sparkles-tutorials) - [Machine Learning Libraries](#machine-learning-libraries) * [Frameworks](#frameworks) * [Neural networks](#neural-networks) * [Deep Learning](#deep-learning) * [Kernel methods](#kernel-methods) * [Evolutionary algorithms](#evolutionary-algorithms) * [Bayesian methods](#bayesian-methods) * [Decision trees](#decision-trees) * [Clustering](#clustering) * [Linear classifiers](#linear-classifiers) * [Statistical models](#statistical-models) * [Gradient boosting](#gradient-boosting) * [Vector search](#vector-search) - [Applications of machine learning](#applications-of-machine-learning) - [Data structures](#data-structures) - [Data visualization](#data-visualization) - [Articles, Posts, Talks, and Presentations](#articles-posts-talks-and-presentations) - [Projects and Code Examples](#projects-and-code-examples) - [Heroku buildpacks](#heroku-buildpacks) - [Books, Blogs, Channels](#books-blogs-channels) - [Community](#community) - [Related Resources](#related-resources) - [License](#license) ## :sparkles: Tutorials Please help us to fill out this section! :smiley: - [Ruby neural networks](https://www.honeybadger.io/blog/ruby-neural-networks/) - [How to implement linear regression in Ruby](https://www.practicalai.io/implementing-linear-regression-using-ruby/) ^{[[code](https://github.com/daugaard/example-linear-regression)]} - [How to implement classification using logistic regression in Ruby](https://www.practicalai.io/implementing-classification-using-logistic-regression-in-ruby/) - [How to implement simple binary classification using a Neural Network in Ruby](https://www.practicalai.io/implementing-simple-classification-using-neural-network-in-ruby/) ^{[[code](https://github.com/daugaard/example-neural-network)]} - [How to implement classification using a SVM in Ruby](https://www.practicalai.io/implementing-classification-using-a-svm-in-ruby/) ^{[[code](https://github.com/daugaard/example-svm)]} - [Unsupervised learning using k-means clustering in Ruby](https://www.practicalai.io/unsupervised-learning-using-k-means-clustering-in-ruby/) ^{[[code](https://github.com/daugaard/example-kmeans-clustering)]} - [Teaching an AI to play a simple game using Q-Learning in Ruby](https://www.practicalai.io/teaching-ai-play-simple-game-using-q-learning/) ^{[[code](https://github.com/daugaard/q-learning-simple-game)]} - [Teaching a Neural Network to play a game using Q-Learning in Ruby](https://www.practicalai.io/teaching-a-neural-network-to-play-a-game-with-q-learning/) ^{[[code](https://github.com/daugaard/q-learning-simple-game/tree/neuralnetwork)]} - [Using the Python scikit-learn machine learning library in Ruby using PyCall](https://www.practicalai.io/using-scikit-learn-machine-learning-library-in-ruby-using-pycall/) ^{[[code](https://github.com/daugaard/scikit-learn-from-ruby)]} - [How to _evolve_ neural networks in Ruby using the Machine Learning Workbench](https://github.com/giuse/machine_learning_workbench/blob/master/examples/neuroevolution.rb) ## Machine Learning Libraries [Machine Learning][ml] algorithms in pure Ruby or written in other programming languages with appropriate bindings for Ruby. ### Frameworks - [LangChain.rb](https://github.com/andreibondarev/langchainrb) - Build ML/AI-supercharged applications with Ruby's LangChain. - [weka](https://github.com/paulgoetze/weka-jruby) - JRuby bindings for Weka, different ML algorithms implemented through Weka. - [ai4r](https://github.com/SergioFierens/ai4r) - Artificial Intelligence for Ruby. - [classifier-reborn](https://github.com/jekyll/classifier-reborn) - General classifier module to allow Bayesian and other types of classifications. ^{[[dep: GLS](#gls)]} - [scoruby](https://github.com/asafschers/scoruby) - Ruby scoring API for [PMML](http://dmg.org/pmml/v4-3/GeneralStructure.html) (Predictive Model Markup Language). - [rblearn](https://github.com/himkt/rblearn) - Feature Extraction and Crossvalidation library. - [data_modeler](https://github.com/giuse/data_modeler) - Model your data with machine learning. Ample test coverage, examples to start fast, complete documentation. Production ready since 1.0.0. - [shogun](https://github.com/shogun-toolbox/shogun) - Polyfunctional and mature machine learning toolbox with [Ruby bindings](https://github.com/shogun-toolbox/shogun/tree/develop/src/interfaces/ruby). - [aws-sdk-machinelearning](https://github.com/aws/aws-sdk-ruby) - Machine Learning API of the Amazon Web Services. - [azure_mgmt_machine_learning](https://github.com/Azure/azure-sdk-for-ruby) - Machine Learning API of the Microsoft Azure. - [machine_learning_workbench](https://github.com/giuse/machine_learning_workbench) - Growing machine learning framework written in pure Ruby, high performance computing using [Numo](https://github.com/ruby-numo/), CUDA bindings through [Cumo](https://github.com/sonots/cumo). Currently implementating neural networks, evolutionary strategies, vector quantization, and plenty of examples and utilities. - [Deep NeuroEvolution](https://github.com/giuse/DNE) - Experimental setup based on the [machine_learning_workbench](https://github.com/giuse/machine_learning_workbench) towards searching for deep neural networks (rather than training) using evolutionary algorithms. Applications to the [OpenAI Gym](https://github.com/openai/gym) using [PyCall](https://github.com/mrkn/pycall.rb). - [rumale](https://github.com/yoshoku/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. - [eps](https://github.com/ankane/eps) - Bayesian Classification and Linear Regression with exports using [PMML](http://dmg.org/pmml/v4-3/GeneralStructure.html) and an alternative backend using [GSL][gsl]. - [ruby-openai](https://github.com/alexrudall/ruby-openai) - OpenAI API wrapper - [Instruct](https://github.com/instruct-rb/instruct) - Inspired by Guidance; weave code, prompts and completions together to instruct LLMs to do what you want. ### Neural networks - [neural-net-ruby](https://github.com/gbuesing/neural-net-ruby) - Neural network written in Ruby. - [ruby-fann](https://github.com/tangledpath/ruby-fann) - Ruby bindings to the [Fast Artificial Neural Network Library (FANN)](http://leenissen.dk/fann/wp/). - [cerebrum](https://github.com/irfansharif/cerebrum) - Experimental implementation for Artificial Neural Networks in Ruby. - [tlearn-rb](https://github.com/josephwilk/tlearn-rb) - Recurrent Neural Network library for Ruby. - [brains](https://github.com/jedld/brains-jruby) - Feed-forward neural networks for JRuby based on [brains](https://github.com/jedld/brains). - [machine_learning_workbench](https://github.com/giuse/machine_learning_workbench/tree/master/lib/machine_learning_workbench/neural_network) - Framework including pure-Ruby implementation of both feed-forward and recurrent neural networks (fully connected). Training available using neuroevolution (Natural Evolution Strategies algorithms). - [rann](https://github.com/mikecmpbll/rann) - Flexible Ruby ANN implementation with backprop (through-time, for recurrent nets), gradient checking, adagrad, and parallel batch execution. ### Deep learning - [tensor_stream](https://github.com/jedld/tensor_stream) - Ground-up and standalone reimplementation of TensorFlow for Ruby. - [red-chainer](https://github.com/red-data-tools/red-chainer) - Deep learning framework for Ruby. - [tensorflow](https://github.com/somaticio/tensorflow.rb) - Ruby bindings for [TensorFlow](https://www.tensorflow.org/). - [ruby-dnn](https://github.com/unagiootoro/ruby-dnn) - Simple deep learning for Ruby. - [torch-rb](https://github.com/ankane/torch-rb) - Ruby bindings for [LibTorch](https://github.com/pytorch/pytorch) using [rice](https://github.com/jasonroelofs/rice). - [mxnet](https://github.com/mrkn/mxnet.rb) - Ruby bindings for [mxnet](https://mxnet.apache.org/). ### Kernel methods - [rb-libsvm](https://github.com/febeling/rb-libsvm) - Support Vector Machines with Ruby and the [LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) library. ^{[[dep: bundled](#bundled)]} ### Evolutionary algorithms - [machine_learning_workbench](https://github.com/giuse/machine_learning_workbench/tree/master/lib/machine_learning_workbench/optimizer/natural_evolution_strategies) - 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](https://github.com/giuse/simple_ga) - Simplest Genetic Algorithms implementation in Ruby. ### Bayesian methods - [linnaeus](https://github.com/djcp/linnaeus) - Redis-backed Bayesian classifier. - [naive_bayes](https://github.com/reddavis/Naive-Bayes) - Simple Naive Bayes classifier. - [nbayes](https://github.com/oasic/nbayes) - Full-featured, Ruby implementation of Naive Bayes. ### Decision trees - [decisiontree](https://github.com/igrigorik/decisiontree) - Decision Tree ID3 Algorithm in pure Ruby. ^{[[dep: GraphViz](#graphviz) | [post](https://www.igvita.com/2007/04/16/decision-tree-learning-in-ruby/)]}. ### Clustering - [kmeans-clusterer](https://github.com/gbuesing/kmeans-clusterer) - k-means clustering in Ruby. - [k_means](https://github.com/reddavis/K-Means) - Attempting to build a fast, memory efficient K-Means program. - [knn](https://github.com/reddavis/knn) - Simple K Nearest Neighbour Algorithm. ### Linear classifiers - [liblinear-ruby-swig](https://github.com/tomz/liblinear-ruby-swig) - Ruby interface to LIBLINEAR (much more efficient than LIBSVM for text classification). - [liblinear-ruby](https://github.com/kei500/liblinear-ruby) - Ruby interface to LIBLINEAR using SWIG. ### Statistical models - [rtimbl](https://github.com/maspwr/rtimbl) - Memory based learners from the Timbl framework. - [lda-ruby](https://github.com/ealdent/lda-ruby) - Ruby implementation of the [LDA](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) (Latent Dirichlet Allocation) for automatic Topic Modelling and Document Clustering. - [maxent_string_classifier](https://github.com/mccraigmccraig/maxent_string_classifier) - JRuby maximum entropy classifier for string data, based on the OpenNLP Maxent framework. - [omnicat](https://github.com/mustafaturan/omnicat) - Generalized rack framework for text classifications. - [omnicat-bayes](https://github.com/mustafaturan/omnicat-bayes) - Naive Bayes text classification implementation as an OmniCat classifier strategy. ^{[[dep: bundled](#bundled)]} ### Gradient boosting - [xgboost](https://github.com/PairOnAir/xgboost-ruby) — Ruby bindings for XGBoost. ^{[[dep: XGBoost](#xgboost)]} - [xgb](https://github.com/ankane/xgb) — Ruby bindings for XGBoost. ^{[[dep: XGBoost](#xgboost)]} - [lightgbm](https://github.com/ankane/lightgbm) — Ruby bindings for LightGBM. ^{[[dep: LightGBM](#lightgbm)]} ### Vector search - [flann](https://github.com/mariusmuja/flann) - Ruby bindings for the [FLANN](https://github.com/flann-lib/flann) (Fast Library for Approximate Nearest Neighbors). ^{[[flann](#flann)]} - [annoy-rb](https://github.com/yoshoku/annoy.rb) - Ruby bindings for the [Annoy](https://github.com/spotify/annoy) (Approximate Nearest Neighbors Oh Yeah). - [hnswlib.rb](https://github.com/yoshoku/hnswlib.rb) - Ruby bindings for the [Hnswlib](https://github.com/nmslib/hnswlib) that implements approximate nearest neighbor search with Hierarchical Navigable Small World graphs. - [ngt-ruby](https://github.com/ankane/ngt-ruby) - Ruby bindings for the [NGT](https://github.com/yahoojapan/NGT) (Neighborhood Graph and Tree for Indexing High-dimensional data). - [milvus](https://github.com/andreibondarev/milvus) — Ruby client for Milvus Vector DB. - [pinecone](https://github.com/ScotterC/pinecone) — Ruby client for Pinecone Vector DB. - [qdrant-ruby](https://github.com/andreibondarev/qdrant-ruby) — Ruby wrapper for the Qdrant vector search database API. - [weaviate-ruby](https://github.com/andreibondarev/weaviate-ruby) — Ruby wrapper for the Weaviate vector search database API. ## Applications of machine learning - [phashion](https://github.com/westonplatter/phashion) - Ruby wrapper around pHash, the perceptual hash library for detecting duplicate multimedia files. ^{[[ImageMagick](#imagemagick) | [libjpeg](#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](https://github.com/arbox/data-science-with-ruby#data-structures) in our [Data Science with Ruby][ds-with-ruby] list. ## Data visualization Please refer to the [Data Visualization](https://github.com/arbox/data-science-with-ruby#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](https://twitter.com/TonsOfFun111) ^{[[video](https://www.youtube.com/watch?v=HPbizNgcyFk)]} - 2019 - _TensorStream: Bringing Machine Learning to Ruby_ by [Joseph Emmanuel Dayo](https://www.linkedin.com/in/jdayo/) ^{[[post](https://medium.com/@joseph.dayo/tensorstream-bringing-machine-learning-to-ruby-114582060e3d)]} - _Easy machine learning with Ruby using SVMKit_ by [@kojix](https://twitter.com/kojix2dayo) ^{[[post](https://dev.to/kojix2/easy-machine-learning-with-ruby-using-svmkit-4n86)]} - 2018 - _Deep Learning Programming on Ruby_ by [Kenta Murata](https://twitter.com/mrkn) & [Yusaku Hatanaka ](https://twitter.com/hatappi) ^{[[slides](https://speakerdeck.com/mrkn/deep-learning-programming-on-ruby) | [page](https://rubykaigi.org/2018/presentations/mrkn.html)]} - _How to use trained Keras and TensorFlow machine learning models within Ruby on Rails_ by [Denis Sellu](https://twitter.com/denis_sellu) ^{[[post](https://www.cookieshq.co.uk/posts/how-to-use-trained-keras-and-tensorflow-machine-learning-models-within-ruby-on-rails)]} - 2017 - _Scientific Computing on JRuby_ by [Prasun Anand](https://twitter.com/prasun_anand) ^{[[slides](https://www.slideshare.net/PrasunAnand2/fosdem2017-scientific-computing-on-jruby) | [video](https://ftp.fau.de/fosdem/2017/K.4.201/ruby_scientific_computing_on_jruby.mp4) | [slides](https://www.slideshare.net/PrasunAnand2/scientific-computing-on-jruby) | [slides](https://www.slideshare.net/PrasunAnand2/scientific-computation-on-jruby)]} - _Is it Food? An Introduction to Machine Learning_ by [Matthew Mongeau](https://twitter.com/halogenandtoast) ^{[[video](https://www.youtube.com/watch?v=8G709hKkthY) | [slides](https://www.slideshare.net/halogenandtoast/is-it-food)]} - _Bayes is BAE_ by [Richard Schneeman](https://twitter.com/schneems) ^{[[video](https://www.youtube.com/watch?v=bQSzZrDDV80) | [slides](https://speakerdeck.com/schneems/bayes-is-bae)]} - _Ruby Roundtable: Machine Learning in Ruby_ by [RubyThursday](https://rubythursday.com/) ^{[[video](https://www.youtube.com/watch?v=ScIFARN0jCo)]} - 2016 - _Practical Machine Learning with Ruby_ by [Jordan Hudgens](https://twitter.com/jordanhudgens) ^{[[tutorial](https://www.crondose.com/2016/12/practical-machine-learning-ruby/)]} - _Deep Learning: An Introduction for Ruby Developers_ by [Geoffrey Litt](https://twitter.com/geoffreylitt) ^{[[slides](https://speakerdeck.com/geoffreylitt/deep-learning-an-introduction-for-ruby-developers)]} - _How I made a pure-Ruby word2vec program more than 3x faster_ by [Kei Sawada](https://twitter.com/remore) ^{[[slides](https://speakerdeck.com/remore/how-i-made-a-pure-ruby-word2vec-program-more-than-3x-faster)]} - _Dōmo arigatō, Mr. Roboto: Machine Learning with Ruby_ by [Eric Weinstein](https://twitter.com/ericqweinstein) ^{[[slides](https://speakerdeck.com/ericqweinstein/domo-arigato-mr-roboto-machine-learning-with-ruby) | [video](https://www.youtube.com/watch?v=T1nFQ49TyeA)]} - _Building a Recommendation Engine with Machine Learning Techniques_ by [Brian Sam-Bodden](https://twitter.com/bsbodden) ^{[[video](https://www.youtube.com/watch?v=SRnM_P_ygqI)]} - :sparkles: _SciRuby Machine Learning: Current Status and Future_ by [Kenta Murata](https://twitter.com/mrkn) ^{[[slides](https://speakerdeck.com/mrkn/sciruby-machine-learning-current-status-and-future) | [video: jp](https://www.youtube.com/watch?v=gfQ8XEy7vO4)]} - _Ruby Roundtable: Intro to Tensorflow_ by [RubyThursday](https://rubythursday.com/) ^{[[video](https://www.youtube.com/watch?v=pYC5mXHUWkc)]} - 2015 - _Machine Learning made simple with Ruby_ by [Lorenzo Masini](https://twitter.com/rugginoso) ^{[[post](https://www.leanpanda.com/blog/2015-08-24-machine-learning-automatic-classification/)]} - _Using Ruby Machine Learning to Find Paris Hilton Quotes_ by [Rick Carlino](https://github.com/RickCarlino) ^{[[tutorial](https://web.archive.org/web/20160414072324/http://datamelon.io/blog/2015/using-ruby-machine-learning-id-paris-hilton-quotes.html)]} - 2014 - _Test Driven Neural Networks_ by [Matthew Kirk](https://twitter.com/mjkirk) ^{[[video](https://www.youtube.com/watch?v=ppf8m-3uXvU&t=36s)]} - _Five machine learning techniques that you can use in your Ruby apps today_ by [Benjamin Curtis](https://twitter.com/stympy) ^{[[video](https://www.youtube.com/watch?v=crziu7dk6Vw) | [slides](https://speakerdeck.com/stympy/machine-learning-techniques)]} - _Machine Learning for Fun and Profit_ by [John Paul Ashenfelter](https://twitter.com/johnashenfelter) ^{[[video](https://www.youtube.com/watch?v=KC5MtKHm1O4)]} - 2013 - _Sentiment Analysis using Support Vector Machines in Ruby_ by [Matthew Kirk](https://twitter.com/mjkirk) ^{[[video](https://www.youtube.com/watch?v=iSug6CgxWxc) | [code](https://github.com/hexgnu/sentiment_analyzer)]} - _Recommender Systems with Ruby_ by [Marcel Caraciolo](https://twitter.com/marcelcaraciolo) ^{[[slides](https://www.slideshare.net/marcelcaraciolo/recommender-systems-with-ruby-adding-machine-learning-statistics-etc)]} - _Detecting Faces with Ruby: FFI in a Nutshell_ by [Marc Berszick]() ^{[[post](https://www.sitepoint.com/detecting-faces-with-ruby-ffi-in-a-nutshell/)]} - 2012 - _Machine Learning with Ruby, Part One_ by [Vasily Vasinov](https://twitter.com/vasinov) ^{[[tutorial](https://www.vasinov.com/blog/machine-learning-with-ruby-part-one/)]} - _Recurrent Neural Networks in Ruby_ by [Joseph Wilk](https://twitter.com/josephwilk) ^{[[post](http://blog.josephwilk.net/ruby/recurrent-neural-networks-in-ruby.html)]} - _Recommendation Engines using Machine Learning, and JRuby_ by [Matthew Kirk](https://twitter.com/mjkirk) ^{[[video](https://www.youtube.com/watch?v=hsZcrlbBg_0)]} - _Practical Machine Learning and Rails_ by [Andrew Cantino](https://twitter.com/tectonic) and [Ryan Stout](https://twitter.com/ryanstout) ^{[[video](https://www.youtube.com/watch?v=vy_zQ1-F0JI)]} - 2011 - _Clustering in Ruby_ by [Colin Drake](https://twitter.com/colinfdrake) ^{[[post](https://colindrake.me/post/k-means-clustering-in-ruby/)]} - _Text Classification using Support Vector Machines in Ruby_ by [Rimas Silkaitis](https://twitter.com/neovintage) ^{[[post](http://neovintage.org/2011/11/14/text-classification-using-support/)]} - 2010 - _bayes_motel – Bayesian classification for Ruby_ by [Mike Perham](https://twitter.com/mperham) ^{[[post](http://www.mikeperham.com/2010/04/28/bayes_motel-bayesian-classification-for-ruby/)]} - _Intelligent Ruby: Getting Started with Machine Learning_ by [Ilya Grigorik](https://twitter.com/igrigorik) ^{[[video](https://vimeo.com/22513786)]} - 2009 - 2008 - _Support Vector Machines (SVM) in Ruby_ by [Ilya Grigorik](https://twitter.com/igrigorik) ^{[[post](https://www.igvita.com/2008/01/07/support-vector-machines-svm-in-ruby/)]} - 2007 - _Decision Tree Learning in Ruby_ by [Ilya Grigorik](https://twitter.com/igrigorik) ^{[[post](https://www.igvita.com/2007/04/16/decision-tree-learning-in-ruby/)]} ## Projects and Code Examples - [Wine Clustering](https://github.com/hexgnu/wine_clustering) - Wine quality estimations clustered with different algorithms. - [simple_ga](https://github.com/giuse/simple_ga) - Basic (working) demo of Genetic Algorithms in Ruby. - [Handwritten Digits Recognition](https://github.com/jdrzj/handwritten-digits-recognition) - Handwritten digits recognition using Neural Networks and Ruby. ## Heroku buildpacks - [GSL and Ruby buildpack](https://github.com/tomwolfe/heroku-buildpack-gsl-ruby) - [OpenCV and Ruby buildpack](https://github.com/lilibethdlc/heroku-buildpack-ruby-opencv) - [ImageMagick buildpack](https://github.com/mcollina/heroku-buildpack-imagemagick) ## Books, Blogs, Channels - [Kirk, Matthew](https://twitter.com/mjkirk). _Thoughtful Machine Learning: A Test-Driven Approach_. O'Reilly, 2014. ^{[[Amazon](https://www.amazon.com/Thoughtful-Machine-Learning-Test-Driven-Approach/dp/1449374069) | [code](https://github.com/thoughtfulml/examples)]} - [Practical Artificial Intelligence](https://www.practicalai.io/) - Blog about Artificial Intelligence and Machine Learning with tutorials and code samples in Ruby. ## Community - [SciRuby Mailing List](https://groups.google.com/forum/#!forum/sciruby-dev) - [SciRuby Slack](https://sciruby.slack.com/) - [Red Data Gitter](https://gitter.im/red-data-tools/) - [Reddit](https://www.reddit.com/r/MachineLearning/search?q=Ruby&restrict_sr=on) - [Stack Overflow](https://stackoverflow.com/search?q=machine+learning+ruby) - [Twitter](https://twitter.com/search?q=Machine%20Learning%20Ruby&src=typd) - [NonWebRuby](https://twitter.com/NonWebRuby) - [Ruby AI Builders Discord](https://discord.gg/zDyFJFBTGB) - [X Ruby AI group](https://twitter.com/i/communities/1709211359039078677) - [Mastodon Ruby AI and Data group](https://ruby.social/@Ruby_AI_and_Data@chirp.social) ## Related Resources - [LightGBM](https://github.com/microsoft/LightGBM) - [XGBoost](https://github.com/dmlc/xgboost) - [GSL (GNU Scientific Library)][gls] - [OpenCV](https://opencv.org/) - [Graphviz](http://www.graphviz.org/) - [Gnuplot](http://www.gnuplot.info/) - [X11/XQuartz](https://www.xquartz.org/) - [ImageMagick](https://www.imagemagick.org/script/index.php) - [R](http://www.r-project.org/) - [Octave](https://www.gnu.org/software/octave/) - [scikit-learn algorithm cheatsheet](https://scikit-learn.org/stable/tutorial/machine_learning_map/) - [Awesome Ruby](https://github.com/markets/awesome-ruby#natural-language-processing) - Among other awesome items a short list of NLP related projects. - [Ruby NLP](https://github.com/diasks2/ruby-nlp) - State-of-Art collection of Ruby libraries for NLP. - [Speech and Natural Language Processing](https://github.com/edobashira/speech-language-processing) - General List of NLP related resources (mostly not for Ruby programmers). - [Scientific Ruby](http://sciruby.com/) - Linear Algebra, Visualization and Scientific Computing for Ruby. - [iRuby](https://github.com/SciRuby/iruby) - IRuby kernel for Jupyter (formerly IPython). - [Kiba](https://github.com/thbar/kiba) - Lightweight [ETL](https://en.wikipedia.org/wiki/Extract,_transform,_load) (Extract, Transform, Load) pipeline. - [Awesome OCR](https://github.com/kba/awesome-ocr) - Multitude of OCR (Optical Character Recognition) resources. - [Awesome TensorFlow](https://github.com/jtoy/awesome-tensorflow) - Machine Learning with TensorFlow libraries. - [rb-gsl](https://github.com/SciRuby/rb-gsl) - Ruby interface to the [GNU Scientific Library](https://www.gnu.org/software/gsl/). - [The Definitive Guide to Ruby's C API](https://silverhammermba.github.io/emberb/) - Modern Reference and Tutorial on Embedding and Extending Ruby using C programming language. ## License [](https://creativecommons.org/publicdomain/zero/1.0/) `Awesome ML with Ruby` by [Andrei Beliankou](https://github.com/arbox) and [Contributors][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 . [ruby]: https://www.ruby-lang.org/en/ [awesome]: https://github.com/sindresorhus/awesome/blob/master/awesome.md [change-pr]: https://github.com/RichardLitt/knowledge/blob/master/github/amending-a-commit-guide.md [ml]: https://en.wikipedia.org/wiki/Machine_learning [ds-with-ruby]: https://github.com/arbox/data-science-with-ruby [contributors]: https://github.com/arbox/machine-learning-with-ruby/graphs/contributors [sciruby]: https://github.com/sciruby [ai]: https://en.wikipedia.org/wiki/Artificial_intelligence [cs]: https://en.wikipedia.org/wiki/Computational_science [fe]: https://en.wikipedia.org/wiki/Feature_engineering [ts]: https://en.wikipedia.org/wiki/Test_set [gsl]: https://www.gnu.org/software/gsl/ [scikit]: https://scikit-learn.org/stable/index.html