[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-8013":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":16,"stars30d":17,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":18,"rankGlobal":10,"rankLanguage":10,"license":19,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":20,"hasPages":20,"topics":22,"createdAt":10,"pushedAt":10,"updatedAt":32,"readmeContent":33,"aiSummary":34,"trendingCount":16,"starSnapshotCount":16,"syncStatus":17,"lastSyncTime":35,"discoverSource":36},8013,"machine-learning-with-ruby","arbox\u002Fmachine-learning-with-ruby","arbox","Curated list: Resources for machine learning in Ruby","",null,"Ruby",2214,183,115,4,0,2,28.79,"Creative Commons Zero v1.0 Universal",false,"master",[23,24,25,26,27,28,29,30,31],"awesome","awesome-list","list","machine-learning","ml","ruby","ruby-gem","rubyml","rubynlp","2026-06-12 02:01:47","\u003Cimg title=\"Awesome Machine Learning with Ruby\" alt=\"Awesome Machine Learning with Ruby\" src=\"header.png\" align=\"center\">\n\n[![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge-flat.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome#readme) [![Support Me](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%92%97-Support%20Me-blue.svg?style=flat-square)](https:\u002F\u002Fwww.patreon.com\u002Farbox)\n\n[[RubyNLP](https:\u002F\u002Fgithub.com\u002Farbox\u002Fnlp-with-ruby) |\n [RubyDataScience](https:\u002F\u002Fgithub.com\u002Farbox\u002Fdata-science-with-ruby) |\n [RubyInterop](https:\u002F\u002Fgithub.com\u002Farbox\u002Fruby-interoperability)]\n\n# Awesome Machine Learning with Ruby [\u003Cimg src=\"ruby.jpg\" align=\"left\" width=\"30px\" height=\"30px\" \u002F>][ruby]\n\n> Curated List of Ruby Machine Learning Links and Resources\n\n[Machine Learning][ml] is a field of [Computational Science][cs] -\noften nested under [AI][ai] research - with many practical\napplications due to the ability of resulting algorithms to\nsystematically implement a specific solution without explicit\nprogrammer's instructions. Obviously many algorithms need a definition\nof [features][fe] to look at or a biggish [training set][ts] of data to derive the\nsolution from.\n\nThis curated list comprises [_awesome_][awesome] libraries,\ndata sources, tutorials and presentations about [Machine Learning][ml]\nutilizing the [Ruby][ruby] programming language.\n\nA lot of useful resources on this list come from the development by\n[The Ruby Science Foundation][sciruby], our [contributors][contributors] and\nour own day to day work on various ML applications.\n\n:sparkles: Every [contribution](contributing.md) is welcome! Add links through pull\nrequests or create an issue to start a discussion.\n\nFollow us on [Twitter](https:\u002F\u002Ftwitter.com\u002FNonWebRuby) and please spread\nthe word using the `#RubyML` hash tag!\n\n\u003C!-- nodoc -->\n## Contents\n\n\u003C!-- toc -->\n\n- [:sparkles: Tutorials](#sparkles-tutorials)\n- [Machine Learning Libraries](#machine-learning-libraries)\n  * [Frameworks](#frameworks)\n  * [Neural networks](#neural-networks)\n  * [Deep Learning](#deep-learning)\n  * [Kernel methods](#kernel-methods)\n  * [Evolutionary algorithms](#evolutionary-algorithms)\n  * [Bayesian methods](#bayesian-methods)\n  * [Decision trees](#decision-trees)\n  * [Clustering](#clustering)\n  * [Linear classifiers](#linear-classifiers)\n  * [Statistical models](#statistical-models)\n  * [Gradient boosting](#gradient-boosting)\n  * [Vector search](#vector-search)\n- [Applications of machine learning](#applications-of-machine-learning)\n- [Data structures](#data-structures)\n- [Data visualization](#data-visualization)\n- [Articles, Posts, Talks, and Presentations](#articles-posts-talks-and-presentations)\n- [Projects and Code Examples](#projects-and-code-examples)\n- [Heroku buildpacks](#heroku-buildpacks)\n- [Books, Blogs, Channels](#books-blogs-channels)\n- [Community](#community)\n- [Related Resources](#related-resources)\n- [License](#license)\n\n\u003C!-- tocstop -->\n\n\u003C!-- doc -->\n\n## :sparkles: Tutorials\n\nPlease help us to fill out this section! :smiley:\n- [Ruby neural networks](https:\u002F\u002Fwww.honeybadger.io\u002Fblog\u002Fruby-neural-networks\u002F)\n- [How to implement linear regression in Ruby](https:\u002F\u002Fwww.practicalai.io\u002Fimplementing-linear-regression-using-ruby\u002F)\n  \u003Csup>[[code](https:\u002F\u002Fgithub.com\u002Fdaugaard\u002Fexample-linear-regression)]\u003C\u002Fsup>\n- [How to implement classification using logistic regression in Ruby](https:\u002F\u002Fwww.practicalai.io\u002Fimplementing-classification-using-logistic-regression-in-ruby\u002F)\n- [How to implement simple binary classification using a Neural Network in Ruby](https:\u002F\u002Fwww.practicalai.io\u002Fimplementing-simple-classification-using-neural-network-in-ruby\u002F)\n  \u003Csup>[[code](https:\u002F\u002Fgithub.com\u002Fdaugaard\u002Fexample-neural-network)]\u003C\u002Fsup>\n- [How to implement classification using a SVM in Ruby](https:\u002F\u002Fwww.practicalai.io\u002Fimplementing-classification-using-a-svm-in-ruby\u002F)\n  \u003Csup>[[code](https:\u002F\u002Fgithub.com\u002Fdaugaard\u002Fexample-svm)]\u003C\u002Fsup>\n- [Unsupervised learning using k-means clustering in Ruby](https:\u002F\u002Fwww.practicalai.io\u002Funsupervised-learning-using-k-means-clustering-in-ruby\u002F)\n  \u003Csup>[[code](https:\u002F\u002Fgithub.com\u002Fdaugaard\u002Fexample-kmeans-clustering)]\u003C\u002Fsup>\n- [Teaching an AI to play a simple game using Q-Learning in Ruby](https:\u002F\u002Fwww.practicalai.io\u002Fteaching-ai-play-simple-game-using-q-learning\u002F)\n  \u003Csup>[[code](https:\u002F\u002Fgithub.com\u002Fdaugaard\u002Fq-learning-simple-game)]\u003C\u002Fsup>\n- [Teaching a Neural Network to play a game using Q-Learning in Ruby](https:\u002F\u002Fwww.practicalai.io\u002Fteaching-a-neural-network-to-play-a-game-with-q-learning\u002F)\n  \u003Csup>[[code](https:\u002F\u002Fgithub.com\u002Fdaugaard\u002Fq-learning-simple-game\u002Ftree\u002Fneuralnetwork)]\u003C\u002Fsup>\n- [Using the Python scikit-learn machine learning library in Ruby using PyCall](https:\u002F\u002Fwww.practicalai.io\u002Fusing-scikit-learn-machine-learning-library-in-ruby-using-pycall\u002F)\n  \u003Csup>[[code](https:\u002F\u002Fgithub.com\u002Fdaugaard\u002Fscikit-learn-from-ruby)]\u003C\u002Fsup>\n- [How to _evolve_ neural networks in Ruby using the Machine Learning Workbench](https:\u002F\u002Fgithub.com\u002Fgiuse\u002Fmachine_learning_workbench\u002Fblob\u002Fmaster\u002Fexamples\u002Fneuroevolution.rb)\n\n## Machine Learning Libraries\n\n[Machine Learning][ml] algorithms in pure Ruby or written in other\nprogramming languages with appropriate bindings for Ruby.\n\n### Frameworks\n\n- [LangChain.rb](https:\u002F\u002Fgithub.com\u002Fandreibondarev\u002Flangchainrb) -\n  Build ML\u002FAI-supercharged applications with Ruby's LangChain.\n- [weka](https:\u002F\u002Fgithub.com\u002Fpaulgoetze\u002Fweka-jruby) -\n  JRuby bindings for Weka, different ML algorithms implemented through Weka.\n- [ai4r](https:\u002F\u002Fgithub.com\u002FSergioFierens\u002Fai4r) -\n  Artificial Intelligence for Ruby.\n- [classifier-reborn](https:\u002F\u002Fgithub.com\u002Fjekyll\u002Fclassifier-reborn) -\n  General classifier module to allow Bayesian and other types of classifications.\n  \u003Csup>[[dep: GLS](#gls)]\u003C\u002Fsup>\n- [scoruby](https:\u002F\u002Fgithub.com\u002Fasafschers\u002Fscoruby) -\n  Ruby scoring API for [PMML](http:\u002F\u002Fdmg.org\u002Fpmml\u002Fv4-3\u002FGeneralStructure.html) (Predictive Model Markup Language).\n- [rblearn](https:\u002F\u002Fgithub.com\u002Fhimkt\u002Frblearn) - Feature Extraction and Crossvalidation library.\n- [data_modeler](https:\u002F\u002Fgithub.com\u002Fgiuse\u002Fdata_modeler) -\n  Model your data with machine learning. Ample test coverage, examples to start fast, complete documentation. Production ready since 1.0.0.\n- [shogun](https:\u002F\u002Fgithub.com\u002Fshogun-toolbox\u002Fshogun) - Polyfunctional and mature\n  machine learning toolbox with [Ruby bindings](https:\u002F\u002Fgithub.com\u002Fshogun-toolbox\u002Fshogun\u002Ftree\u002Fdevelop\u002Fsrc\u002Finterfaces\u002Fruby).\n- [aws-sdk-machinelearning](https:\u002F\u002Fgithub.com\u002Faws\u002Faws-sdk-ruby) -\n  Machine Learning API of the Amazon Web Services.\n- [azure_mgmt_machine_learning](https:\u002F\u002Fgithub.com\u002FAzure\u002Fazure-sdk-for-ruby) -\n  Machine Learning API of the Microsoft Azure.\n- [machine_learning_workbench](https:\u002F\u002Fgithub.com\u002Fgiuse\u002Fmachine_learning_workbench) -\n  Growing machine learning framework written in pure Ruby, high performance computing using\n  [Numo](https:\u002F\u002Fgithub.com\u002Fruby-numo\u002F), CUDA bindings through [Cumo](https:\u002F\u002Fgithub.com\u002Fsonots\u002Fcumo).\n  Currently implementating neural networks, evolutionary strategies, vector quantization, and plenty of\n  examples and utilities.\n- [Deep NeuroEvolution](https:\u002F\u002Fgithub.com\u002Fgiuse\u002FDNE) -\n  Experimental setup based on the [machine_learning_workbench](https:\u002F\u002Fgithub.com\u002Fgiuse\u002Fmachine_learning_workbench)\n  towards searching for deep neural networks (rather than training) using evolutionary algorithms. Applications to the\n  [OpenAI Gym](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fgym) using [PyCall](https:\u002F\u002Fgithub.com\u002Fmrkn\u002Fpycall.rb).\n- [rumale](https:\u002F\u002Fgithub.com\u002Fyoshoku\u002Frumale) -\n  Machine Learninig toolkit in Ruby with wide range of implemented algorithms\n  (SVM, Logistic Regression, Linear Regression, Random Forest etc.) and\n  interfaces similar to [Scikit-Learn][scikit] in Python.\n- [eps](https:\u002F\u002Fgithub.com\u002Fankane\u002Feps) - Bayesian Classification and Linear Regression with exports\n  using [PMML](http:\u002F\u002Fdmg.org\u002Fpmml\u002Fv4-3\u002FGeneralStructure.html) and an alternative backend using [GSL][gsl].\n- [ruby-openai](https:\u002F\u002Fgithub.com\u002Falexrudall\u002Fruby-openai) - OpenAI API wrapper\n- [Instruct](https:\u002F\u002Fgithub.com\u002Finstruct-rb\u002Finstruct) - Inspired by Guidance; weave code, prompts and completions together to instruct LLMs to do what you want.\n  \n### Neural networks\n\n- [neural-net-ruby](https:\u002F\u002Fgithub.com\u002Fgbuesing\u002Fneural-net-ruby) -\n  Neural network written in Ruby.\n- [ruby-fann](https:\u002F\u002Fgithub.com\u002Ftangledpath\u002Fruby-fann) -\n  Ruby bindings to the [Fast Artificial Neural Network Library (FANN)](http:\u002F\u002Fleenissen.dk\u002Ffann\u002Fwp\u002F).\n- [cerebrum](https:\u002F\u002Fgithub.com\u002Firfansharif\u002Fcerebrum) -\n  Experimental implementation for Artificial Neural Networks in Ruby.\n- [tlearn-rb](https:\u002F\u002Fgithub.com\u002Fjosephwilk\u002Ftlearn-rb) -\n  Recurrent Neural Network library for Ruby.\n- [brains](https:\u002F\u002Fgithub.com\u002Fjedld\u002Fbrains-jruby) -\n  Feed-forward neural networks for JRuby based on\n  [brains](https:\u002F\u002Fgithub.com\u002Fjedld\u002Fbrains).\n- [machine_learning_workbench](https:\u002F\u002Fgithub.com\u002Fgiuse\u002Fmachine_learning_workbench\u002Ftree\u002Fmaster\u002Flib\u002Fmachine_learning_workbench\u002Fneural_network) -\n  Framework including pure-Ruby implementation of both feed-forward and recurrent neural networks\n  (fully connected). Training available using neuroevolution (Natural Evolution Strategies algorithms).\n- [rann](https:\u002F\u002Fgithub.com\u002Fmikecmpbll\u002Frann) -\n  Flexible Ruby ANN implementation with backprop (through-time, for recurrent\n  nets), gradient checking, adagrad, and parallel batch execution.\n\n### Deep learning\n\n- [tensor_stream](https:\u002F\u002Fgithub.com\u002Fjedld\u002Ftensor_stream) -\n  Ground-up and standalone reimplementation of TensorFlow for Ruby.\n- [red-chainer](https:\u002F\u002Fgithub.com\u002Fred-data-tools\u002Fred-chainer) - Deep learning framework for Ruby.\n- [tensorflow](https:\u002F\u002Fgithub.com\u002Fsomaticio\u002Ftensorflow.rb) - Ruby bindings for [TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002F).\n- [ruby-dnn](https:\u002F\u002Fgithub.com\u002Funagiootoro\u002Fruby-dnn) - Simple deep learning for Ruby.\n- [torch-rb](https:\u002F\u002Fgithub.com\u002Fankane\u002Ftorch-rb) - Ruby bindings for [LibTorch](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fpytorch)\n  using [rice](https:\u002F\u002Fgithub.com\u002Fjasonroelofs\u002Frice).\n- [mxnet](https:\u002F\u002Fgithub.com\u002Fmrkn\u002Fmxnet.rb) - Ruby bindings for [mxnet](https:\u002F\u002Fmxnet.apache.org\u002F).\n\n### Kernel methods\n\n- [rb-libsvm](https:\u002F\u002Fgithub.com\u002Ffebeling\u002Frb-libsvm) -\n  Support Vector Machines with Ruby and the [LIBSVM](https:\u002F\u002Fwww.csie.ntu.edu.tw\u002F~cjlin\u002Flibsvm\u002F) library.\n  \u003Csup>[[dep: bundled](#bundled)]\u003C\u002Fsup>\n\n### Evolutionary algorithms\n\n- [machine_learning_workbench](https:\u002F\u002Fgithub.com\u002Fgiuse\u002Fmachine_learning_workbench\u002Ftree\u002Fmaster\u002Flib\u002Fmachine_learning_workbench\u002Foptimizer\u002Fnatural_evolution_strategies) -\n  Framework including pure-Ruby implementations of Natural Evolution Strategy algorithms\n  (black-box optimization), specifically Exponential NES (XNES),\n  Separable NES (sNES), Block-Diagonal NES (BDNES) and more.\n  Applications include neural network search\u002Ftraining (neuroevolution).\n- [simple_ga](https:\u002F\u002Fgithub.com\u002Fgiuse\u002Fsimple_ga) -\n  Simplest Genetic Algorithms implementation in Ruby.\n\n### Bayesian methods\n\n- [linnaeus](https:\u002F\u002Fgithub.com\u002Fdjcp\u002Flinnaeus) -\n  Redis-backed Bayesian classifier.\n- [naive_bayes](https:\u002F\u002Fgithub.com\u002Freddavis\u002FNaive-Bayes) -\n  Simple Naive Bayes classifier.\n- [nbayes](https:\u002F\u002Fgithub.com\u002Foasic\u002Fnbayes) -\n  Full-featured, Ruby implementation of Naive Bayes.\n\n### Decision trees\n\n- [decisiontree](https:\u002F\u002Fgithub.com\u002Figrigorik\u002Fdecisiontree) -\n  Decision Tree ID3 Algorithm in pure Ruby.\n  \u003Csup>[[dep: GraphViz](#graphviz) |\n        [post](https:\u002F\u002Fwww.igvita.com\u002F2007\u002F04\u002F16\u002Fdecision-tree-learning-in-ruby\u002F)]\u003C\u002Fsup>.\n\n### Clustering\n\n- [kmeans-clusterer](https:\u002F\u002Fgithub.com\u002Fgbuesing\u002Fkmeans-clusterer) -\n  k-means clustering in Ruby.\n- [k_means](https:\u002F\u002Fgithub.com\u002Freddavis\u002FK-Means) -\n  Attempting to build a fast, memory efficient K-Means program.\n- [knn](https:\u002F\u002Fgithub.com\u002Freddavis\u002Fknn) -\n  Simple K Nearest Neighbour Algorithm.\n\n### Linear classifiers\n\n- [liblinear-ruby-swig](https:\u002F\u002Fgithub.com\u002Ftomz\u002Fliblinear-ruby-swig) -\n  Ruby interface to LIBLINEAR (much more efficient than LIBSVM for text classification).\n- [liblinear-ruby](https:\u002F\u002Fgithub.com\u002Fkei500\u002Fliblinear-ruby) -\n  Ruby interface to LIBLINEAR using SWIG.\n\n### Statistical models\n\n- [rtimbl](https:\u002F\u002Fgithub.com\u002Fmaspwr\u002Frtimbl) -\n  Memory based learners from the Timbl framework.\n- [lda-ruby](https:\u002F\u002Fgithub.com\u002Fealdent\u002Flda-ruby) -\n  Ruby implementation of the [LDA](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLatent_Dirichlet_allocation)\n  (Latent Dirichlet Allocation) for automatic Topic Modelling and Document Clustering.\n- [maxent_string_classifier](https:\u002F\u002Fgithub.com\u002Fmccraigmccraig\u002Fmaxent_string_classifier) -\n  JRuby maximum entropy classifier for string data, based on the OpenNLP Maxent framework.\n- [omnicat](https:\u002F\u002Fgithub.com\u002Fmustafaturan\u002Fomnicat) -\n  Generalized rack framework for text classifications.\n- [omnicat-bayes](https:\u002F\u002Fgithub.com\u002Fmustafaturan\u002Fomnicat-bayes) -\n  Naive Bayes text classification implementation as an OmniCat classifier strategy.\n  \u003Csup>[[dep: bundled](#bundled)]\u003C\u002Fsup>\n\n### Gradient boosting\n\n- [xgboost](https:\u002F\u002Fgithub.com\u002FPairOnAir\u002Fxgboost-ruby) —\n  Ruby bindings for XGBoost.\n  \u003Csup>[[dep: XGBoost](#xgboost)]\u003C\u002Fsup>\n- [xgb](https:\u002F\u002Fgithub.com\u002Fankane\u002Fxgb) —\n  Ruby bindings for XGBoost.\n  \u003Csup>[[dep: XGBoost](#xgboost)]\u003C\u002Fsup>\n- [lightgbm](https:\u002F\u002Fgithub.com\u002Fankane\u002Flightgbm) —\n  Ruby bindings for LightGBM.\n  \u003Csup>[[dep: LightGBM](#lightgbm)]\u003C\u002Fsup>\n\n### Vector search\n\n- [flann](https:\u002F\u002Fgithub.com\u002Fmariusmuja\u002Fflann) -\n  Ruby bindings for the [FLANN](https:\u002F\u002Fgithub.com\u002Fflann-lib\u002Fflann) (Fast Library for Approximate Nearest Neighbors).\n  \u003Csup>[[flann](#flann)]\u003C\u002Fsup>\n- [annoy-rb](https:\u002F\u002Fgithub.com\u002Fyoshoku\u002Fannoy.rb) -\n  Ruby bindings for the [Annoy](https:\u002F\u002Fgithub.com\u002Fspotify\u002Fannoy) (Approximate Nearest Neighbors Oh Yeah).\n- [hnswlib.rb](https:\u002F\u002Fgithub.com\u002Fyoshoku\u002Fhnswlib.rb) -\n  Ruby bindings for the [Hnswlib](https:\u002F\u002Fgithub.com\u002Fnmslib\u002Fhnswlib) that implements approximate nearest neighbor search with Hierarchical Navigable Small World graphs.\n- [ngt-ruby](https:\u002F\u002Fgithub.com\u002Fankane\u002Fngt-ruby) -\n  Ruby bindings for the [NGT](https:\u002F\u002Fgithub.com\u002Fyahoojapan\u002FNGT) (Neighborhood Graph and Tree for Indexing High-dimensional data).\n- [milvus](https:\u002F\u002Fgithub.com\u002Fandreibondarev\u002Fmilvus) —\n  Ruby client for Milvus Vector DB.\n- [pinecone](https:\u002F\u002Fgithub.com\u002FScotterC\u002Fpinecone) —\n  Ruby client for Pinecone Vector DB.\n- [qdrant-ruby](https:\u002F\u002Fgithub.com\u002Fandreibondarev\u002Fqdrant-ruby) —\n  Ruby wrapper for the Qdrant vector search database API.\n- [weaviate-ruby](https:\u002F\u002Fgithub.com\u002Fandreibondarev\u002Fweaviate-ruby) —\n  Ruby wrapper for the Weaviate vector search database API.\n\n## Applications of machine learning\n\n- [phashion](https:\u002F\u002Fgithub.com\u002Fwestonplatter\u002Fphashion) -\n  Ruby wrapper around pHash, the perceptual hash library for detecting duplicate multimedia files.\n  \u003Csup>[[ImageMagick](#imagemagick) | [libjpeg](#libjpeg)]\u003C\u002Fsup>\n\n## Data structures\n\nIf you're going to implement your own ML algorithms you're probably interested\nin storing your feature sets efficiently. Look for appropriate\n[data structures](https:\u002F\u002Fgithub.com\u002Farbox\u002Fdata-science-with-ruby#data-structures)\nin our [Data Science with Ruby][ds-with-ruby] list.\n\n## Data visualization\n\nPlease refer to the [Data Visualization](https:\u002F\u002Fgithub.com\u002Farbox\u002Fdata-science-with-ruby#visualization)\nsection on the [Data Science with Ruby][ds-with-ruby] list.\n\n## Articles, Posts, Talks, and Presentations\n\n- 2022\n  - _Discover Machine Learning in Ruby_ by [Justin Bowen](https:\u002F\u002Ftwitter.com\u002FTonsOfFun111)\n   \u003Csup>[[video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=HPbizNgcyFk)]\u003C\u002Fsup>\n- 2019\n  - _TensorStream: Bringing Machine Learning to Ruby_ by [Joseph Emmanuel Dayo](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fjdayo\u002F)\n    \u003Csup>[[post](https:\u002F\u002Fmedium.com\u002F@joseph.dayo\u002Ftensorstream-bringing-machine-learning-to-ruby-114582060e3d)]\u003C\u002Fsup>\n  - _Easy machine learning with Ruby using SVMKit_ by [@kojix](https:\u002F\u002Ftwitter.com\u002Fkojix2dayo)\n    \u003Csup>[[post](https:\u002F\u002Fdev.to\u002Fkojix2\u002Feasy-machine-learning-with-ruby-using-svmkit-4n86)]\u003C\u002Fsup>\n- 2018\n  - _Deep Learning Programming on Ruby_ by [Kenta Murata](https:\u002F\u002Ftwitter.com\u002Fmrkn)\n    & [Yusaku Hatanaka ](https:\u002F\u002Ftwitter.com\u002Fhatappi)\n    \u003Csup>[[slides](https:\u002F\u002Fspeakerdeck.com\u002Fmrkn\u002Fdeep-learning-programming-on-ruby) |\n          [page](https:\u002F\u002Frubykaigi.org\u002F2018\u002Fpresentations\u002Fmrkn.html)]\u003C\u002Fsup>\n  - _How to use trained Keras and TensorFlow machine learning models within Ruby on Rails_ by [Denis Sellu](https:\u002F\u002Ftwitter.com\u002Fdenis_sellu)\n    \u003Csup>[[post](https:\u002F\u002Fwww.cookieshq.co.uk\u002Fposts\u002Fhow-to-use-trained-keras-and-tensorflow-machine-learning-models-within-ruby-on-rails)]\u003C\u002Fsup>\n- 2017\n  - _Scientific Computing on JRuby_ by [Prasun Anand](https:\u002F\u002Ftwitter.com\u002Fprasun_anand)\n    \u003Csup>[[slides](https:\u002F\u002Fwww.slideshare.net\u002FPrasunAnand2\u002Ffosdem2017-scientific-computing-on-jruby) |\n    [video](https:\u002F\u002Fftp.fau.de\u002Ffosdem\u002F2017\u002FK.4.201\u002Fruby_scientific_computing_on_jruby.mp4) |\n    [slides](https:\u002F\u002Fwww.slideshare.net\u002FPrasunAnand2\u002Fscientific-computing-on-jruby) |\n    [slides](https:\u002F\u002Fwww.slideshare.net\u002FPrasunAnand2\u002Fscientific-computation-on-jruby)]\u003C\u002Fsup>\n  - _Is it Food? An Introduction to Machine Learning_ by [Matthew Mongeau](https:\u002F\u002Ftwitter.com\u002Fhalogenandtoast)\n    \u003Csup>[[video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=8G709hKkthY) |\n          [slides](https:\u002F\u002Fwww.slideshare.net\u002Fhalogenandtoast\u002Fis-it-food)]\u003C\u002Fsup>\n  - _Bayes is BAE_ by [Richard Schneeman](https:\u002F\u002Ftwitter.com\u002Fschneems)\n    \u003Csup>[[video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=bQSzZrDDV80) |\n          [slides](https:\u002F\u002Fspeakerdeck.com\u002Fschneems\u002Fbayes-is-bae)]\u003C\u002Fsup>\n  - _Ruby Roundtable: Machine Learning in Ruby_ by [RubyThursday](https:\u002F\u002Frubythursday.com\u002F)\n    \u003Csup>[[video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ScIFARN0jCo)]\u003C\u002Fsup>\n- 2016\n  - _Practical Machine Learning with Ruby_ by [Jordan Hudgens](https:\u002F\u002Ftwitter.com\u002Fjordanhudgens)\n    \u003Csup>[[tutorial](https:\u002F\u002Fwww.crondose.com\u002F2016\u002F12\u002Fpractical-machine-learning-ruby\u002F)]\u003C\u002Fsup>\n  - _Deep Learning: An Introduction for Ruby Developers_ by [Geoffrey Litt](https:\u002F\u002Ftwitter.com\u002Fgeoffreylitt)\n    \u003Csup>[[slides](https:\u002F\u002Fspeakerdeck.com\u002Fgeoffreylitt\u002Fdeep-learning-an-introduction-for-ruby-developers)]\u003C\u002Fsup>\n  - _How I made a pure-Ruby word2vec program more than 3x faster_ by [Kei Sawada](https:\u002F\u002Ftwitter.com\u002Fremore)\n    \u003Csup>[[slides](https:\u002F\u002Fspeakerdeck.com\u002Fremore\u002Fhow-i-made-a-pure-ruby-word2vec-program-more-than-3x-faster)]\u003C\u002Fsup>\n  - _Dōmo arigatō, Mr. Roboto: Machine Learning with Ruby_ by [Eric Weinstein](https:\u002F\u002Ftwitter.com\u002Fericqweinstein)\n    \u003Csup>[[slides](https:\u002F\u002Fspeakerdeck.com\u002Fericqweinstein\u002Fdomo-arigato-mr-roboto-machine-learning-with-ruby) |\n          [video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=T1nFQ49TyeA)]\u003C\u002Fsup>\n  - _Building a Recommendation Engine with Machine Learning Techniques_ by [Brian Sam-Bodden](https:\u002F\u002Ftwitter.com\u002Fbsbodden)\n    \u003Csup>[[video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=SRnM_P_ygqI)]\u003C\u002Fsup>\n  - :sparkles: _SciRuby Machine Learning: Current Status and Future_ by [Kenta Murata](https:\u002F\u002Ftwitter.com\u002Fmrkn)\n    \u003Csup>[[slides](https:\u002F\u002Fspeakerdeck.com\u002Fmrkn\u002Fsciruby-machine-learning-current-status-and-future) |\n          [video: jp](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=gfQ8XEy7vO4)]\u003C\u002Fsup>\n  - _Ruby Roundtable: Intro to Tensorflow_ by [RubyThursday](https:\u002F\u002Frubythursday.com\u002F)\n    \u003Csup>[[video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=pYC5mXHUWkc)]\u003C\u002Fsup>\n- 2015\n  - _Machine Learning made simple with Ruby_ by [Lorenzo Masini](https:\u002F\u002Ftwitter.com\u002Frugginoso)\n    \u003Csup>[[post](https:\u002F\u002Fwww.leanpanda.com\u002Fblog\u002F2015-08-24-machine-learning-automatic-classification\u002F)]\u003C\u002Fsup>\n  - _Using Ruby Machine Learning to Find Paris Hilton Quotes_ by [Rick Carlino](https:\u002F\u002Fgithub.com\u002FRickCarlino)\n    \u003Csup>[[tutorial](https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20160414072324\u002Fhttp:\u002F\u002Fdatamelon.io\u002Fblog\u002F2015\u002Fusing-ruby-machine-learning-id-paris-hilton-quotes.html)]\u003C\u002Fsup>\n- 2014\n  - _Test Driven Neural Networks_ by [Matthew Kirk](https:\u002F\u002Ftwitter.com\u002Fmjkirk)\n    \u003Csup>[[video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ppf8m-3uXvU&t=36s)]\u003C\u002Fsup>\n  - _Five machine learning techniques that you can use in your Ruby apps today_ by [Benjamin Curtis](https:\u002F\u002Ftwitter.com\u002Fstympy)\n    \u003Csup>[[video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=crziu7dk6Vw) |\n          [slides](https:\u002F\u002Fspeakerdeck.com\u002Fstympy\u002Fmachine-learning-techniques)]\u003C\u002Fsup>\n  - _Machine Learning for Fun and Profit_ by [John Paul Ashenfelter](https:\u002F\u002Ftwitter.com\u002Fjohnashenfelter)\n    \u003Csup>[[video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=KC5MtKHm1O4)]\u003C\u002Fsup>\n- 2013\n  - _Sentiment Analysis using Support Vector Machines in Ruby_ by [Matthew Kirk](https:\u002F\u002Ftwitter.com\u002Fmjkirk)\n    \u003Csup>[[video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=iSug6CgxWxc) |\n          [code](https:\u002F\u002Fgithub.com\u002Fhexgnu\u002Fsentiment_analyzer)]\u003C\u002Fsup>\n  - _Recommender Systems with Ruby_ by [Marcel Caraciolo](https:\u002F\u002Ftwitter.com\u002Fmarcelcaraciolo)\n    \u003Csup>[[slides](https:\u002F\u002Fwww.slideshare.net\u002Fmarcelcaraciolo\u002Frecommender-systems-with-ruby-adding-machine-learning-statistics-etc)]\u003C\u002Fsup>\n  - _Detecting Faces with Ruby: FFI in a Nutshell_ by [Marc Berszick]()\n    \u003Csup>[[post](https:\u002F\u002Fwww.sitepoint.com\u002Fdetecting-faces-with-ruby-ffi-in-a-nutshell\u002F)]\u003C\u002Fsup>\n- 2012\n  - _Machine Learning with Ruby, Part One_ by [Vasily Vasinov](https:\u002F\u002Ftwitter.com\u002Fvasinov)\n    \u003Csup>[[tutorial](https:\u002F\u002Fwww.vasinov.com\u002Fblog\u002Fmachine-learning-with-ruby-part-one\u002F)]\u003C\u002Fsup>\n  - _Recurrent Neural Networks in Ruby_ by [Joseph Wilk](https:\u002F\u002Ftwitter.com\u002Fjosephwilk)\n    \u003Csup>[[post](http:\u002F\u002Fblog.josephwilk.net\u002Fruby\u002Frecurrent-neural-networks-in-ruby.html)]\u003C\u002Fsup>\n  - _Recommendation Engines using Machine Learning, and JRuby_ by [Matthew Kirk](https:\u002F\u002Ftwitter.com\u002Fmjkirk)\n    \u003Csup>[[video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=hsZcrlbBg_0)]\u003C\u002Fsup>\n  - _Practical Machine Learning and Rails_ by [Andrew Cantino](https:\u002F\u002Ftwitter.com\u002Ftectonic)\n    and [Ryan Stout](https:\u002F\u002Ftwitter.com\u002Fryanstout)\n    \u003Csup>[[video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vy_zQ1-F0JI)]\u003C\u002Fsup>\n\n- 2011\n  - _Clustering in Ruby_ by [Colin Drake](https:\u002F\u002Ftwitter.com\u002Fcolinfdrake)\n    \u003Csup>[[post](https:\u002F\u002Fcolindrake.me\u002Fpost\u002Fk-means-clustering-in-ruby\u002F)]\u003C\u002Fsup>\n  - _Text Classification using Support Vector Machines in Ruby_ by [Rimas Silkaitis](https:\u002F\u002Ftwitter.com\u002Fneovintage)\n    \u003Csup>[[post](http:\u002F\u002Fneovintage.org\u002F2011\u002F11\u002F14\u002Ftext-classification-using-support\u002F)]\u003C\u002Fsup>\n- 2010\n  - _bayes_motel – Bayesian classification for Ruby_ by [Mike Perham](https:\u002F\u002Ftwitter.com\u002Fmperham)\n    \u003Csup>[[post](http:\u002F\u002Fwww.mikeperham.com\u002F2010\u002F04\u002F28\u002Fbayes_motel-bayesian-classification-for-ruby\u002F)]\u003C\u002Fsup>\n  - _Intelligent Ruby: Getting Started with Machine Learning_ by [Ilya Grigorik](https:\u002F\u002Ftwitter.com\u002Figrigorik)\n    \u003Csup>[[video](https:\u002F\u002Fvimeo.com\u002F22513786)]\u003C\u002Fsup>\n- 2009\n\n- 2008\n  - _Support Vector Machines (SVM) in Ruby_ by [Ilya Grigorik](https:\u002F\u002Ftwitter.com\u002Figrigorik)\n    \u003Csup>[[post](https:\u002F\u002Fwww.igvita.com\u002F2008\u002F01\u002F07\u002Fsupport-vector-machines-svm-in-ruby\u002F)]\u003C\u002Fsup>\n- 2007\n  - _Decision Tree Learning in Ruby_ by [Ilya Grigorik](https:\u002F\u002Ftwitter.com\u002Figrigorik)\n    \u003Csup>[[post](https:\u002F\u002Fwww.igvita.com\u002F2007\u002F04\u002F16\u002Fdecision-tree-learning-in-ruby\u002F)]\u003C\u002Fsup>\n\n## Projects and Code Examples\n\n- [Wine Clustering](https:\u002F\u002Fgithub.com\u002Fhexgnu\u002Fwine_clustering) -\n  Wine quality estimations clustered with different algorithms.\n- [simple_ga](https:\u002F\u002Fgithub.com\u002Fgiuse\u002Fsimple_ga) -\n  Basic (working) demo of Genetic Algorithms in Ruby.\n- [Handwritten Digits Recognition](https:\u002F\u002Fgithub.com\u002Fjdrzj\u002Fhandwritten-digits-recognition) -\n  Handwritten digits recognition using Neural Networks and Ruby.\n\n## Heroku buildpacks\n\n- [GSL and Ruby buildpack](https:\u002F\u002Fgithub.com\u002Ftomwolfe\u002Fheroku-buildpack-gsl-ruby)\n- [OpenCV and Ruby buildpack](https:\u002F\u002Fgithub.com\u002Flilibethdlc\u002Fheroku-buildpack-ruby-opencv)\n- [ImageMagick buildpack](https:\u002F\u002Fgithub.com\u002Fmcollina\u002Fheroku-buildpack-imagemagick)\n\n## Books, Blogs, Channels\n\n-  [Kirk, Matthew](https:\u002F\u002Ftwitter.com\u002Fmjkirk).\n   _Thoughtful Machine Learning: A Test-Driven Approach_. O'Reilly, 2014.\n   \u003Csup>[[Amazon](https:\u002F\u002Fwww.amazon.com\u002FThoughtful-Machine-Learning-Test-Driven-Approach\u002Fdp\u002F1449374069) |\n         [code](https:\u002F\u002Fgithub.com\u002Fthoughtfulml\u002Fexamples)]\u003C\u002Fsup>\n- [Practical Artificial Intelligence](https:\u002F\u002Fwww.practicalai.io\u002F) -\n  Blog about Artificial Intelligence and Machine Learning with tutorials and code samples in Ruby.\n\n## Community\n\n- [SciRuby Mailing List](https:\u002F\u002Fgroups.google.com\u002Fforum\u002F#!forum\u002Fsciruby-dev)\n- [SciRuby Slack](https:\u002F\u002Fsciruby.slack.com\u002F)\n- [Red Data Gitter](https:\u002F\u002Fgitter.im\u002Fred-data-tools\u002F)\n- [Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FMachineLearning\u002Fsearch?q=Ruby&restrict_sr=on)\n- [Stack Overflow](https:\u002F\u002Fstackoverflow.com\u002Fsearch?q=machine+learning+ruby)\n- [Twitter](https:\u002F\u002Ftwitter.com\u002Fsearch?q=Machine%20Learning%20Ruby&src=typd)\n- [NonWebRuby](https:\u002F\u002Ftwitter.com\u002FNonWebRuby)\n- [Ruby AI Builders Discord](https:\u002F\u002Fdiscord.gg\u002FzDyFJFBTGB)\n- [X Ruby AI group](https:\u002F\u002Ftwitter.com\u002Fi\u002Fcommunities\u002F1709211359039078677)\n- [Mastodon Ruby AI and Data group](https:\u002F\u002Fruby.social\u002F@Ruby_AI_and_Data@chirp.social)\n\n## Related Resources\n\n- \u003Ca name=\"lightgbm\">\u003C\u002Fa>\n  [LightGBM](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLightGBM)\n- \u003Ca name=\"xgboost\">\u003C\u002Fa>\n  [XGBoost](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fxgboost)\n- \u003Ca name=\"gls\">\u003C\u002Fa>\n  [GSL (GNU Scientific Library)][gls]\n- \u003Ca name=\"opencv\">\u003C\u002Fa>\n  [OpenCV](https:\u002F\u002Fopencv.org\u002F)\n- \u003Ca name=\"empty-lines-around-access-modifier\">\u003C\u002Fa>\n  [Graphviz](http:\u002F\u002Fwww.graphviz.org\u002F)\n- \u003Ca name=\"gnuplot\">\u003C\u002Fa>\n  [Gnuplot](http:\u002F\u002Fwww.gnuplot.info\u002F)\n- \u003Ca name=\"xquartz\">\u003C\u002Fa>\n  [X11\u002FXQuartz](https:\u002F\u002Fwww.xquartz.org\u002F)\n- \u003Ca name=\"imagemagic\">\u003C\u002Fa>\n  [ImageMagick](https:\u002F\u002Fwww.imagemagick.org\u002Fscript\u002Findex.php)\n- \u003Ca name=\"r\">\u003C\u002Fa>\n  [R](http:\u002F\u002Fwww.r-project.org\u002F)\n- \u003Ca name=\"octave\">\u003C\u002Fa>\n  [Octave](https:\u002F\u002Fwww.gnu.org\u002Fsoftware\u002Foctave\u002F)\n- [scikit-learn algorithm cheatsheet](https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Ftutorial\u002Fmachine_learning_map\u002F)\n- [Awesome Ruby](https:\u002F\u002Fgithub.com\u002Fmarkets\u002Fawesome-ruby#natural-language-processing) -\n  Among other awesome items a short list of NLP related projects.\n- [Ruby NLP](https:\u002F\u002Fgithub.com\u002Fdiasks2\u002Fruby-nlp) -\n  State-of-Art collection of Ruby libraries for NLP.\n- [Speech and Natural Language Processing](https:\u002F\u002Fgithub.com\u002Fedobashira\u002Fspeech-language-processing) -\n  General List of NLP related resources (mostly not for Ruby programmers).\n- [Scientific Ruby](http:\u002F\u002Fsciruby.com\u002F) -\n  Linear Algebra, Visualization and Scientific Computing for Ruby.\n- [iRuby](https:\u002F\u002Fgithub.com\u002FSciRuby\u002Firuby) - IRuby kernel for Jupyter (formerly IPython).\n- [Kiba](https:\u002F\u002Fgithub.com\u002Fthbar\u002Fkiba) -\n  Lightweight [ETL](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FExtract,_transform,_load) (Extract, Transform, Load) pipeline.\n- [Awesome OCR](https:\u002F\u002Fgithub.com\u002Fkba\u002Fawesome-ocr) -\n  Multitude of OCR (Optical Character Recognition) resources.\n- [Awesome TensorFlow](https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow) -\n  Machine Learning with TensorFlow libraries.\n- [rb-gsl](https:\u002F\u002Fgithub.com\u002FSciRuby\u002Frb-gsl) -\n  Ruby interface to the [GNU Scientific Library](https:\u002F\u002Fwww.gnu.org\u002Fsoftware\u002Fgsl\u002F).\n- [The Definitive Guide to Ruby's C API](https:\u002F\u002Fsilverhammermba.github.io\u002Femberb\u002F) -\n  Modern Reference and Tutorial on Embedding and Extending Ruby using C programming language.\n\n## License\n\n[![Creative Commons Zero 1.0](http:\u002F\u002Fmirrors.creativecommons.org\u002Fpresskit\u002Fbuttons\u002F80x15\u002Fsvg\u002Fcc-zero.svg)](https:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0\u002F)\n`Awesome ML with Ruby` by [Andrei Beliankou](https:\u002F\u002Fgithub.com\u002Farbox) and\n[Contributors][contributors].\n\nTo the extent possible under law, the person who associated CC0 with\n`Awesome ML with Ruby` has waived all copyright and related or neighboring rights\nto `Awesome ML with Ruby`.\n\nYou should have received a copy of the CC0 legalcode along with this\nwork. If not, see \u003Chttps:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0\u002F>.\n\n\u003C!--- Links --->\n[ruby]: https:\u002F\u002Fwww.ruby-lang.org\u002Fen\u002F\n[awesome]: https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome\u002Fblob\u002Fmaster\u002Fawesome.md\n[change-pr]: https:\u002F\u002Fgithub.com\u002FRichardLitt\u002Fknowledge\u002Fblob\u002Fmaster\u002Fgithub\u002Famending-a-commit-guide.md\n[ml]: https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMachine_learning\n[ds-with-ruby]: https:\u002F\u002Fgithub.com\u002Farbox\u002Fdata-science-with-ruby\n[contributors]: https:\u002F\u002Fgithub.com\u002Farbox\u002Fmachine-learning-with-ruby\u002Fgraphs\u002Fcontributors\n[sciruby]: https:\u002F\u002Fgithub.com\u002Fsciruby\n[ai]: https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FArtificial_intelligence\n[cs]: https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FComputational_science\n[fe]: https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FFeature_engineering\n[ts]: https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTest_set\n[gsl]: https:\u002F\u002Fwww.gnu.org\u002Fsoftware\u002Fgsl\u002F\n[scikit]: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Findex.html\n","该项目是一个精心整理的资源列表，旨在为使用Ruby进行机器学习提供丰富的链接和资料。它汇集了大量优秀的库、数据源、教程及演讲材料，覆盖从基础框架到深度学习、贝叶斯方法等高级主题。特别适合希望在Ruby环境下探索或实现机器学习解决方案的开发者和技术爱好者。此项目由The Ruby Science Foundation及其贡献者共同维护，体现了社区力量对开源技术的支持与推动。","2026-06-11 03:15:38","top_language"]