Machine Learning

Official AWS Ruby gem for Amazon Machine Learning. This gem is part of the AWS SDK for Ruby.

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.

Ruby algorithm implementations covering several Artificial intelligence fields, including Genetic algorithms, Neural Networks, machine learning, and clustering.

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-v1 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.

Self-contained LIBSVM package for Ruby (that doesn't use SWIG). LIBSVM is a popular implementation of SVM, a machine learning classifier.

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-v1p1beta1 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.

Rumale is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. Rumale supports Support Vector Machine, Logistic Regression, Ridge, Lasso, Multi-layer Perceptron, Naive Bayes, Decision Tree, Gradient Tree Boosting, Random Forest, K-Means, Gaussian Mixture Model, DBSCAN, Spectral Clustering, Mutidimensional Scaling, t-SNE, Fisher Discriminant Analysis, Neighbourhood Component Analysis, Principal Component Analysis, Non-negative Matrix Factorization, and many other algorithms.

AutoML makes the power of machine learning available to you even if you have limited knowledge of machine learning. You can use AutoML to build on Google's machine learning capabilities to create your own custom machine learning models that are tailored to your business needs, and then integrate those models into your applications and web sites. Note that google-cloud-automl-v1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-automl instead. See the readme for more details.

AutoML makes the power of machine learning available to you even if you have limited knowledge of machine learning. You can use AutoML to build on Google's machine learning capabilities to create your own custom machine learning models that are tailored to your business needs, and then integrate those models into your applications and web sites. Note that google-cloud-automl-v1beta1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-automl instead. See the readme for more details.

Vertex AI enables data scientists, developers, and AI newcomers to create custom machine learning models specific to their business needs by leveraging Google's state-of-the-art transfer learning and innovative AI research. Note that google-cloud-ai_platform-v1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-ai_platform instead. See the readme for more details.

A sophisticated parser for academic reference lists and bibliographies based on machine learning algorithms using conditional random fields.

Document AI uses machine learning on a single cloud-based platform to automatically classify, extract, and enrich data within your documents to unlock insights. Note that google-cloud-document_ai-v1beta3 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-document_ai instead. See the readme for more details.

Apache 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 Apache PredictionIO API for Ruby programmers so that you can focus on application logic.

Reckon automagically converts CSV files for use with the command-line accounting tool Ledger. It also helps you to select the correct accounts associated with the CSV data using Bayesian machine learning.

Document AI uses machine learning on a single cloud-based platform to automatically classify, extract, and enrich data within your documents to unlock insights. Note that google-cloud-document_ai-v1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-document_ai instead. See the readme for more details.

Microsoft Azure Machine Learning Management Client Library for Ruby

Machine learning for Ruby. Supports regression (linear regression) and classification (naive Bayes)

AutoML makes the power of machine learning available to you even if you have limited knowledge of machine learning. You can use AutoML to build on Google's machine learning capabilities to create your own custom machine learning models that are tailored to your business needs, and then integrate those models into your applications and web sites.

Jubatus is a distributed processing framework and streaming machine learning library. This is the Jubatus client in Ruby.

Vertex AI enables data scientists, developers, and AI newcomers to create custom machine learning models specific to their business needs by leveraging Google's state-of-the-art transfer learning and innovative AI research.

Ruby bindings to the OpenNLP tools, a Java machine learning toolkit for natural language processing (NLP).

AI Platform Notebooks makes it easy to manage JupyterLab instances through a protected, publicly available notebook instance URL. A JupyterLab instance is a Deep Learning virtual machine instance with the latest machine learning and data science libraries pre-installed. Note that google-cloud-notebooks-v1beta1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-notebooks instead. See the readme for more details.

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. Note that google-cloud-media_translation-v1beta1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-media_translation instead. See the readme for more details.

AI Platform Data Labeling Service lets you work with human labelers to generate highly accurate labels for a collection of data that you can use in machine learning models. Note that google-cloud-data_labeling-v1beta1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-data_labeling instead. See the readme for more details.

AYLIEN Text Analysis API is a package of Natural Language Processing and Machine Learning-powered tools for analyzing and extracting various kinds of information from text and images.

An implementation of a linear regression machine learning algorithm implemented in Ruby. The library supports simple problems with one independent variable used to predict a dependent variable as well as multivariate problems with multiple independent variables to predict a dependent variable. You can train your algorithms using the normal equation or gradient descent. The library is implemented in pure ruby using Ruby's Matrix implementation.

Rumale::Core provides base classes and utility functions for implementing machine learning algorithm with Rumale interface.

Microsoft Azure Machine Learning Services Management Client Library for Ruby

A sophisticated parser for academic reference lists and bibliographies based on machine learning algorithms using conditional random fields.

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. Note that google-cloud-tpu-v1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-tpu instead. See the readme for more details.

Intrastructure test, useful for: (1) Sysadmin teachers to evaluate students remote machines. (2) Sysadmin apprentices to evaluate their learning process as a game. (3) Professional sysadmin to monitor remote machines. Allow us: (a) Write test units for real or virtual machines using simple DSL. (b) Check compliance with requirements on remote machines.

RubyNEAT -- Neural Evolution of Augmenting Topologies for Ruby. By way of an enhanced form of Genetic Algorithms -- the NEAT algorithm, populations of neural nets are evolved to handle pre-defined goals. RubyNEAT is the first implementation of the NEAT algorithm for Ruby, and it leverages Ruby's power to implement the NEAT algorithm in a way that would be difficult to do in other languages. The 'activation function' is largely standalone. Basically, activation is achieved by functional programming. Meaning, once your network is evolved, you can extract it as source code you can then utilize without the RubyNEAT engine. RubyNEAT can be used for nearly any Machine Learning task you can dream of, because it's also extensible and modular. See http://rubyneat.com for the details.

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.

A prototype Virtual Machine written in Ruby. Why would anyone write such a thing? Because it's fun, and it's a great learning tool.

This workbench holds a collection of machine learning methods in Ruby. Rather than specializing on a single task or method, this gem aims at providing an encompassing framework for any machine learning application.

This project is a Ruby gem ('hmm') for machine learning that natively implements a (somewhat) generalized Hidden Markov Model classifier.

The Rosette Text Analytics Platform uses natural language processing, statistical modeling, and machine learning to analyze unstructured and semi-structured text across 364 language-encoding-script combinations, revealing valuable information and actionable data. Rosette provides endpoints for extracting entities and relationships, translating and comparing the similarity of names, categorizing and adding linguistic tags to text and more.

Document AI uses machine learning on a single cloud-based platform to automatically classify, extract, and enrich data within your documents to unlock insights.

JRuby Mahout is a gem that unleashes the power of Apache Mahout in the world of JRuby. Mahout is a superior machine learning library written in Java. It deals with recommendations, clustering and classification machine learning problems at scale. Until now it was difficult to use it in Ruby projects. You'd have to implement Java interfaces in Jruby yourself, which is not quick especially if you just started exploring the world of machine learning.

Cabalist is conceived as a simple way of adding some smarts (machine learning capabilities) to your Ruby on Rails models without having to dig deep into mind-boggling AI algorithms. Using it is meant to be as straightforward as adding a few lines to your existing code and running a Rails generator or two.

AI Platform Notebooks makes it easy to manage JupyterLab instances through a protected, publicly available notebook instance URL. A JupyterLab instance is a Deep Learning virtual machine instance with the latest machine learning and data science libraries pre-installed. Note that google-cloud-notebooks-v1 is a version-specific client library. For most uses, we recommend installing the main client library google-cloud-notebooks instead. See the readme for more details.

A wonderful hound that finds patterns in your data using machine learning.

TensorFlow - the end-to-end machine learning platform - for Ruby

FSelector is a Ruby gem that aims to integrate various feature selection algorithms and related functions into one single package. Welcome to contact me (need47@gmail.com) if you'd like to contribute your own algorithms or report a bug. FSelector allows user to perform feature selection by using either a single algorithm or an ensemble of multiple algorithms, and other common tasks including normalization and discretization on continuous data, as well as replace missing feature values with certain criterion. FSelector acts on a full-feature data set in either CSV, LibSVM or WEKA file format and outputs a reduced data set with only selected subset of features, which can later be used as the input for various machine learning softwares such as LibSVM and WEKA. FSelector, as a collection of filter methods, does not implement any classifier like support vector machines or random forest.

Data mining and machine learning algorithms for JRuby

Ruby implementations of algorithms covering several Artificial intelligence fields, including Genetic algorithms, Neural Networks, machine learning, and clustering.

They are some performance critical pieces of code that will be executed on huge data sets, which we want to make sure will run fast enough. Unfortunately, enforcing this is not easy, often requiring large scale and slow benchmarks. This rspec library (the result of an experiment to learn machine learning) uses linear regression to determine the time complexity (Big O notation, O(x)) of a piece of code and to check that it is at least as good as what we expect. This does not require huge data sets (only a few large ones) and can be written as any unit test (not as fast though).

AI Platform Notebooks makes it easy to manage JupyterLab instances through a protected, publicly available notebook instance URL. A JupyterLab instance is a Deep Learning virtual machine instance with the latest machine learning and data science libraries pre-installed.

This is a ruby gem that lets you implement categorization systems with ease. Associative memory neural networks make it easy to identify probable patterns between sets of named data points. It can be cumbersome to interface with the neural network directly, however, as a typical implementation has a fixed size and training period, which limits how useful they can be to an integrated system. associative_memory simplifies these kind of machine learning models by offering dynamic input and output sets. This allows your code to concentrate on extrapolating meaningful patterns rather than juggling bitmasks and transposition matrices.

AI Platform Data Labeling Service lets you work with human labelers to generate highly accurate labels for a collection of data that you can use in machine learning models.