Machine Learning

Media Translation API delivers real-time speech translation to your content and applications directly from your audio data. Leveraging Google’s machine learning technologies, the API offers enhanced accuracy and simplified integration while equipping you with a comprehensive set of features to further refine your translation results. Improve user experience with low-latency streaming translation and scale quickly with straightforward internationalization.

Deploy machine learning models in Ruby (and Rails)

Machine learning library in Ruby

Tensor Processing Units (TPUs) are Google's custom-developed application-specific integrated circuits (ASICs) used to accelerate machine learning workloads. Cloud TPUs allow you to access TPUs from Compute Engine, Google Kubernetes Engine and AI Platform.

A gem with tools for machine learning.

Google Speech-to-Text enables developers to convert audio to text by applying powerful neural network models in an easy-to-use API. The API recognizes more than 120 languages and variants to support your global user base. You can enable voice command-and-control, transcribe audio from call centers, and more. It can process real-time streaming or prerecorded audio, using Google's machine learning technology. Note that google-cloud-speech-v2 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-speech instead. See the readme for more details.

Ariel uses machine learning to assist in extracting information from semi-structured documents including (but not in any way limited to) web pages

Create K-fold splits from data files and assist in training and testing (useful for cross-validation in supervised machine learning)

Data mining and machine learning algorithms for JRuby

Image Recognition and Processing APIs let you use Machine Learning to recognize and process images, including automatic caption generation, face recognition and NSFW classification, and also perform useful image modification operations.

SVMKit is a machine learninig library in Ruby. SVMKit provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. SVMKit supports Linear / Kernel Support Vector Machine, Logistic Regression, Linear Regression, Ridge, Lasso, Factorization Machine, Naive Bayes, Decision Tree, AdaBoost, Random Forest, K-nearest neighbor algorithm, K-Means, DBSCAN, Principal Component Analysis, and Non-negative Matrix Factorization. Note that the SVMKit has been deprecated and has been renamed to Rumale.

Performs k-fold cross-validation on machine learning classifiers.

IMMEDIATE DEPRECATION WARNING: this gem has been deprecated. Please find the same functionality with extended feature, better scoping and integration to more methods in the [`machine_learning_workbench` gem](https://github.com/giuse/machine_learning_workbench): check out the [nes classes](https://github.com/giuse/machine_learning_workbench/tree/master/lib/machine_learning_workbench/optimizer/natural_evolution_strategies), [neural network classes](https://github.com/giuse/machine_learning_workbench/tree/master/lib/machine_learning_workbench/neural_network), and [neuroevo example](https://github.com/giuse/machine_learning_workbench/blob/master/examples/neuroevolution.rb). You should be able to transition in no time. Any problem: just ping me. Apologies for the inconvenience, hope you will enjoy the new gem!

A feedforward neural network library for JRuby. Aims to provide a quick way to get started on machine learning with ruby

While you have to provide the paragraphs, ZombieWriter will arrange the paragraphs into different articles for you to use and edit to your heart's content. You may choose between Machine Learning (Latent Semantic Analysis and k-means clustering) or Randomization.

Using machine learning, create generative models based on your data alone. Applications span from prediction to imputation and compression. This build specifically leverages time series. NOTE: Since version 1.0.0 we're production-ready! :)

This is a gem to help ruby developers to write machine learning algorithms easier and faster / Esta es una gema para ayudar a los desarrolladores de ruby a escribir algoritmos de aprendizaje automático más fácil y rápido.

Supervised learning is the machine learning task of inferring a function from labeled training data. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

Fast online machine learning for Ruby

The Huginn Naive Bayes agent uses some incoming Events as a training set for Naive Bayes Machine Learning. Then it classifies Events from other sources accordingly using tags. Acts as a Huginn Agent front end to the NBayes gem (https://github.com/oasic/nbayes).

Ruby/Rails DSL for Prediction.io, an open source machine learning server.

Sabina is a machine learning library. This gem provides tools for Multi-Layer Perceptrons and Auto-Encoders.

PredictionIO is an open source machine learning server for developers and data scientists to create predictive engines for production environments. This gem provides convenient access to the PredictionIO API for Ruby programmers so that you can focus on application logic.

Experimental machine learning REST API client.

my machine learning lib

lite machine learning tools: classifier, annotator, and more

ID3 decision trees for machine learning in Ruby

Rarff is a Ruby library for dealing with Attribute-Relation File Format (ARFF) files. ARFF files are used to specify data sets for data mining and machine learning.

Mobile machine learning as a service.

This is a library for machine learning. You can use AdaBoost and Naive Bayes easily.

A set of ruby bindings for communicating with the wise.io Machine Learning as a Service platform.

Simplifies development of applications using an IBM Machine Learning service by providing methods for getting deployments and calling them. Operates with both IBM Watson Machine Learning as well as Machine Learning on DSX Local.

A Ruby project that uses machine learning to perform Optical Character Recognition

rtimbl provides Ruby language bindings to the TiMBL machine learning library. See the README for more information.

Machine Learning experiments

A very simple naive Bayesian classifier. I'm just using it as practice as I learn how to package ruby code. The algorithm used here is not original, but an adaptation from Burak Kanber's Machine Learning in Javascript series. http://readable.cc/feed/view/34236/burak-kanber-s-blog

A library for machine learning and classification

A collection of machine learning algorithms implemented in a very simple, concise, and easy-to-use way.

A library for machine learning and classification

Colonize is a simple provisioning tool for Vagrant. Since shell scripts can get difficult and Colonizeing systems like Puppet, Chef and/or Ansible can be too much for simple projects. I decided to write Colonize to cover the middleground. Colonize is written in Ruby and uses a Ruby-typical configuration file simply because Vagrant already is using Ruby and I think it uneccessary for anyone to have to learn another language only to configure a simple virtual machine. Colonize currently is WIP. It's not documented very much, nor are there many services implemented.

Tokkens makes it easy to apply a vector space model to text documents, targeted towards with machine learning. It provides a mapping between numbers and tokens (strings)

Speech APIs enable you to recognize speech and convert it to text using advanced machine learning, and also to convert text to speech.

Turing Analytics is driven by a vision to develop solutions powered by machine learning to help businesses make data driven decisions.

This Ruby gem leverages Machine Learning(ML) techniques to make predictions(forecasts) and classifications in various applications. It provides capabilities such as predicting next month's billing, forecasting upcoming sales orders, identifying patient's potential findings(like Diabetes), determining user approval status, classifying text, generating similarity scores, and making recommendations. It uses Python3 under the hood, powered by popular machine learning techniques including NLP(Natural Language Processing), Decision Tree, K-Nearest Neighbors and Logistic Regression, Random Forest and Linear Regression algorithms.

In machine learning we usually split data into training data and test data. The training set contains a known output and the model learns on this data in order to be generalized to other data later on.

SciRb-learn (pronounced "Sigh Ruby learn") is to become a collection of machine learning and reinforcement algorithms natively implemented in Ruby

Tools for machine learning with ruby

Databot will provide a variety of machine learning and data science tools. Currently in development.

Attempt to create a gem based on machine learning for recognize japanese Kanjis in images.

Small playground to test different machine learning strategies