Added the first version.

Author: arbox

README.md

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-# machine-learning-with-ruby
+# Machine Learning with Ruby [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)
+
+[<img src="ruby.jpg" align="right" width="100px" height="100px" />][ruby]
+
+> Useful resources for machine learning in [Ruby][ruby]
+
+This curated list comprises [_awesome_](https://github.com/sindresorhus/awesome/blob/master/awesome.md)
+resources, libraries, information sources about Machine Learning with the [Ruby programming language](ruby).
+
+This list comes from our day to day work on ML Applications.
+
+Our main goal is to promote Ruby as a tool for NLP related tasks. Your help,
+suggestions and contributions are welcome! We kindly ask you to study
+the [Contribution](#contributing) section. Follow us on [Twitter](https://twitter.com/RubyNLP)
+and please spread the word using the `#RubyML` hash tag!
+
+<!-- nodoc -->
+## Contents
+
+<!-- toc -->
+
+- [Machine Learning Libraries](#machine-learning-libraries)
+- [Articles, Posts, Talks, and Presentations](#articles-posts-talks-and-presentations)
+- [Projects and Code Examples](#projects-and-code-examples)
+- [Books](#books)
+- [Community](#community)
+- [Needs your Help!](#needs-your-help)
+- [Related Resources](#related-resources)
+- [Contributing](#contributing)
+- [License](#license)
+
+<!-- tocstop -->
+
+<!-- doc -->
+
+## Machine Learning Libraries
+
+[Machine Learning](https://en.wikipedia.org/wiki/Machine_learning) Algorithms
+in pure Ruby or written in other programming languages with appropriate bindings
+for Ruby.
+
+- [rb-libsvm](https://github.com/febeling/rb-libsvm) -
+  Support Vector Machines with Ruby.
+- [weka-jruby](https://github.com/paulgoetze/weka-jruby) -
+  JRuby bindings for Weka, different ML algorithms implemented through Weka.
+- [decisiontree](https://github.com/igrigorik/decisiontree) -
+  Decision Tree ID3 Algorithm in pure Ruby
+  <sup>[[post](https://www.igvita.com/2007/04/16/decision-tree-learning-in-ruby/)]</sup>.
+- [rtimbl](https://github.com/maspwr/rtimbl) -
+  Memory based learners from the Timbl framework.
+- [classifier-reborn](https://github.com/jekyll/classifier-reborn) -
+  General classifier module to allow Bayesian and other types of classifications.
+- [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.
+- [liblinear-ruby-swig](https://github.com/tomz/liblinear-ruby-swig) -
+  Ruby interface to LIBLINEAR (much more efficient than LIBSVM for text classification).
+- [linnaeus](https://github.com/djcp/linnaeus) -
+  Redis-backed Bayesian classifier.
+- [maxent_string_classifier](https://github.com/mccraigmccraig/maxent_string_classifier) -
+  JRuby maximum entropy classifier for string data, based on the OpenNLP Maxent framework.
+- [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.
+- [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.
+- [ruby-fann](https://github.com/tangledpath/ruby-fann) -
+  Ruby bindings to the [Fast Artificial Neural Network Library (FANN)](http://leenissen.dk/fann/wp/).
+
+## Articles, Posts, Talks, and Presentations
+
+- 2017
+  - _Scientific Computing on JRuby_ by [Prasun Anand](https://twitter.com/prasun_anand)
+    <sup>[[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)]</sup>
+  - _Unicode Normalization in Ruby_ by [Starr Horne](https://twitter.com/starrhorne)
+    <sup>[[post](http://blog.honeybadger.io/ruby_unicode_normalization/)]</sup>
+- 2016
+  - _Quickly Create a Telegram Bot in Ruby_ by [Ardian Haxha](https://twitter.com/ArdianHaxha)
+    <sup>[[tutorial](https://www.sitepoint.com/quickly-create-a-telegram-bot-in-ruby/)]</sup>
+  - _Deep Learning: An Introduction for Ruby Developers_ by [Geoffrey Litt](https://twitter.com/geoffreylitt)
+    <sup>[[slides](https://speakerdeck.com/geoffreylitt/deep-learning-an-introduction-for-ruby-developers)]</sup>
+  - _How I made a pure-Ruby word2vec program more than 3x faster_ by [Kei Sawada](https://twitter.com/remore)
+    <sup>[[slides](https://speakerdeck.com/remore/how-i-made-a-pure-ruby-word2vec-program-more-than-3x-faster)]</sup>
+  - _Dōmo arigatō, Mr. Roboto: Machine Learning with Ruby_ by [Eric Weinstein](https://twitter.com/ericqweinstein)
+    <sup>[[slides](https://speakerdeck.com/ericqweinstein/domo-arigato-mr-roboto-machine-learning-with-ruby) | [video](https://www.youtube.com/watch?v=T1nFQ49TyeA)]</sup>
+- 2015
+  - _N-gram Analysis for Fun and Profit_ by [Jesus Castello](https://github.com/matugm)
+    <sup>[[tutorial](http://www.blackbytes.info/2015/09/ngram-analysis-ruby/)]</sup>
+  - _Machine Learning made simple with Ruby_ by [Lorenzo Masini](https://github.com/rugginoso)
+    <sup>[[tutorial](https://www.leanpanda.com/blog/2015/08/24/machine-learning-automatic-classification/)]</sup>
+  - _Using Ruby Machine Learning to Find Paris Hilton Quotes_ by [Rick Carlino](https://github.com/RickCarlino)
+    <sup>[[tutorial](https://web-beta.archive.org/web/20160515115739/http://datamelon.io/blog/2015/using-ruby-machine-learning-id-paris-hilton-quotes.html)]</sup>
+  - _Exploring Natural Language Processing in Ruby_ by [Kevin Dias](https://github.com/diasks2)
+    <sup>[[slides](https://www.slideshare.net/diasks2/exploring-natural-language-processing-in-ruby)]</sup>
+  - _Machine Learning made simple with Ruby_ by [Lorenzo Masini](https://twitter.com/rugginoso)
+    <sup>[[post](https://www.leanpanda.com/blog/2015/08/24/machine-learning-automatic-classification/)]</sup>
+  - _Practical Data Science in Ruby_ by Bobby Grayson
+    <sup>[[slides](http://slides.com/bobbygrayson/p#/)]</sup>
+- 2014
+  - _Natural Language Parsing with Ruby_ by [Glauco Custódio](https://github.com/glaucocustodio)
+    <sup>[[tutorial](http://glaucocustodio.github.io/2014/11/10/natural-language-parsing-with-ruby/)]</sup>
+  - _Demystifying Data Science: Analyzing Conference Talks with Rails and Ngrams_ by
+    [Todd Schneider](https://github.com/toddwschneider)
+    <sup>[[video](https://www.youtube.com/watch?v=2ZDCxwB29Bg) | [code](https://github.com/Genius/abstractogram)]</sup>
+  - _Natural Language Processing with Ruby_ by [Konstantin Tennhard](https://github.com/t6d)
+    <sup>[[video](https://www.youtube.com/watch?v=5u86qVh8r0M) | [video](https://www.youtube.com/watch?v=oFmy_QBQ5DU) |
+    [video](https://www.youtube.com/watch?v=sPkeeWnsMn0) |
+    [slides](http://euruko2013.org/speakers/presentations/natural_language_processing_with_ruby_and_opennlp-tennhard.pdf)]</sup>
+- 2013
+  - _How to parse 'go' - Natural Language Processing in Ruby_ by
+    [Tom Cartwright](https://twitter.com/tomcartwrightuk)
+    <sup>[[slides](https://www.slideshare.net/TomCartwright/natual-language-processing-in-ruby) |
+    [video](https://skillsmatter.com/skillscasts/4883-how-to-parse-go)]</sup>
+  - _Natural Language Processing in Ruby_ by [Brandon Black](https://github.com/brandonblack)
+    <sup>[[slides](https://speakerdeck.com/brandonblack/natural-language-processing-in-ruby) |
+    [video](http://confreaks.tv/videos/railsconf2013-natural-language-processing-with-ruby)]</sup>
+  - _Natural Language Processing with Ruby: n-grams_ by [Nathan Kleyn](https://github.com/nathankleyn)
+    <sup>[[tutorial](https://www.sitepoint.com/natural-language-processing-ruby-n-grams/) |
+    [code](https://github.com/nathankleyn/ruby_nlp)]</sup>
+  - _Seeking Lovecraft, Part 1: An introduction to NLP and the Treat Gem_ by
+    [Robert Qualls](https://github.com/rlqualls)
+    <sup>[[tutorial](https://www.sitepoint.com/seeking-lovecraft-part-1-an-introduction-to-nlp-and-the-treat-gem/)]</sup>
+- 2012
+  - _Machine Learning with Ruby, Part One_ by [Vasily Vasinov](https://twitter.com/vasinov)
+    <sup>[[tutorial](http://www.vasinov.com/blog/machine-learning-with-ruby-part-one/)]</sup>
+- 2011
+  - _Ruby one-liners_ by [Benoit Hamelin](https://twitter.com/benoithamelin)
+    <sup>[[post](http://benoithamelin.tumblr.com/ruby1line)]</sup>
+  - _Clustering in Ruby_ by [Colin Drake](https://twitter.com/colinfdrake)
+    <sup>[[post](https://colindrake.me/2011/05/28/clustering-in-ruby/)]</sup>
+- 2010
+  - _bayes_motel – Bayesian classification for Ruby_ by [Mike Perham](https://twitter.com/mperham)
+    <sup>[[post](http://www.mikeperham.com/2010/04/28/bayes_motel-bayesian-classification-for-ruby/)]</sup>
+- 2009
+  - _Porting the UEA-Lite Stemmer to Ruby_ by [Jason Adams](https://twitter.com/ealdent)
+    <sup>[[post](https://ealdent.wordpress.com/2009/07/16/porting-the-uea-lite-stemmer-to-ruby/)]</sup>
+  - _NLP Resources for Ruby_ by [Jason Adams](https://twitter.com/ealdent)
+    <sup>[[post](https://ealdent.wordpress.com/2009/09/13/nlp-resources-for-ruby/)]</sup>
+- 2008
+  - _Support Vector Machines (SVM) in Ruby_ by [Ilya Grigorik](https://twitter.com/igrigorik)
+    <sup>[[post](https://www.igvita.com/2008/01/07/support-vector-machines-svm-in-ruby/)]</sup>
+  - _Practical text classification with Ruby_ by [Gleicon Moraes](https://twitter.com/gleicon)
+    <sup>[[post](https://zenmachine.wordpress.com/practical-text-classification-with-ruby/) |
+    [code](https://github.com/gleicon/zenmachine)]</sup>
+- 2007
+  - _Decision Tree Learning in Ruby_ by [Ilya Grigorik](https://twitter.com/igrigorik)
+    <sup>[[post](https://www.igvita.com/2007/04/16/decision-tree-learning-in-ruby/)]</sup>
+
+## Projects and Code Examples
+
+- [Going the Distance](https://github.com/schneems/going_the_distance) -
+  Implementations of various distance algorithms with example calculations.
+- [Named entity recognition with Stanford NER and Ruby](https://github.com/mblongii/ruby-ner) -
+  NER Examples in Ruby and Java with some [explanations](https://web.archive.org/web/20120722225402/http://mblongii.com/2012/04/15/named-entity-recognition-with-stanford-ner-and-ruby/).
+- [Words Counted](http://rubywordcount.com/) -
+  examples of customizable word statistics powered by
+  [words_counted](https://github.com/abitdodgy/words_counted).
+
+## Books
+
+## Community
+
+- [Reddit](https://www.reddit.com/r/LanguageTechnology/search?q=ruby&restrict_sr=on)
+- [Stack Overflow](http://stackoverflow.com/search?q=%5Bnlp%5D+and+%5Bruby%5D)
+- [Twitter](https://twitter.com/search?q=Ruby%20NLP%20%23ruby%20OR%20%23nlproc%20OR%20%23rubynlp%20OR%20%23nlp&src=typd&lang=en)
+
+## 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.
+
+## Related Resources
+
+- [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 (formelly 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.
+
+## Contributing
+
+We are very glad to see you in this section and highly appreciate any help!
+
+But we also take care about the quality of this list. If you want to contribute
+please:
+
+- agree that your work will be published under the terms of the `CC0` license;
+- carefully read the [Contribution Guidelines][contributing].
+
+Some of the open tasks for contributors are listed in the [todo file][todo].
+You may want to start there.
+
+## License
+
+[![Creative Commons Zero 1.0](http://mirrors.creativecommons.org/presskit/buttons/80x15/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/) `Awesome ML with Ruby` by [Andrei Beliankou](https://github.com/arbox) and
+[Contributors](https://github.com/arbox/machine-learning-with-ruby/graphs/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/>.
+
+<!--- Links --->
+[ruby]: https://www.ruby-lang.org/en/
+[motivation]: https://github.com/arbox/nlp-with-ruby/blob/master/motivation.md
+[contributing]: https://github.com/arbox/nlp-with-ruby/blob/master/CONTRIBUTING.md
+[todo]: https://github.com/arbox/nlp-with-ruby/blob/master/todo.md
+[faq]: https://github.com/arbox/nlp-with-ruby/blob/master/FAQ.md