Machine Learning > ML with Ruby

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).

Build ML/AI-supercharged applications with Ruby's LangChain.

JRuby bindings for Weka, different ML algorithms implemented through Weka.

Artificial Intelligence for Ruby.

General classifier module to allow Bayesian and other types of classifications.

Ruby scoring API for PMML (Predictive Model Markup Language).

Feature Extraction and Crossvalidation library.

Model your data with machine learning. Ample test coverage, examples to start fast, complete documentation. Production ready since 1.0.0.

Polyfunctional and mature machine learning toolbox with Ruby bindings.

Machine Learning API of the Amazon Web Services.

Machine Learning API of the Microsoft Azure.

Experimental setup based on the machinelearningworkbench towards searching for deep neural networks (rather than training) using evolutionary algorithms. Applications to the OpenAI Gym using PyCall.

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.

Bayesian Classification and Linear Regression with exports using PMML and an alternative backend using [GSL][gsl].

OpenAI API wrapper

Inspired by Guidance; weave code, prompts and completions together to instruct LLMs to do what you want.

Neural network written in Ruby.

Ruby bindings to the Fast Artificial Neural Network Library (FANN).

Experimental implementation for Artificial Neural Networks in Ruby.

Recurrent Neural Network library for Ruby.

Feed-forward neural networks for JRuby based on brains.

Flexible Ruby ANN implementation with backprop (through-time, for recurrent nets), gradient checking, adagrad, and parallel batch execution.

Ground-up and standalone reimplementation of TensorFlow for Ruby.

Deep learning framework for Ruby.

Ruby bindings for TensorFlow.

Simple deep learning for Ruby.

Ruby bindings for LibTorch using rice.

Ruby bindings for mxnet.

Support Vector Machines with Ruby and the LIBSVM library.

Redis-backed Bayesian classifier.

Simple Naive Bayes classifier.

Full-featured, Ruby implementation of Naive Bayes.

Decision Tree ID3 Algorithm in pure Ruby.

k-means clustering in Ruby.

Attempting to build a fast, memory efficient K-Means program.

Simple K Nearest Neighbour Algorithm.

Ruby interface to LIBLINEAR (much more efficient than LIBSVM for text classification).

Ruby interface to LIBLINEAR using SWIG.

Memory based learners from the Timbl framework.

Ruby implementation of the LDA (Latent Dirichlet Allocation) for automatic Topic Modelling and Document Clustering.

JRuby maximum entropy classifier for string data, based on the OpenNLP Maxent framework.

Generalized rack framework for text classifications.

Naive Bayes text classification implementation as an OmniCat classifier strategy.

Ruby bindings for XGBoost.

Ruby bindings for XGBoost.

Ruby bindings for LightGBM.

Ruby bindings for the FLANN (Fast Library for Approximate Nearest Neighbors).

Ruby bindings for the Annoy (Approximate Nearest Neighbors Oh Yeah).

Ruby bindings for the Hnswlib that implements approximate nearest neighbor search with Hierarchical Navigable Small World graphs.

Ruby bindings for the NGT (Neighborhood Graph and Tree for Indexing High-dimensional data).

Ruby client for Milvus Vector DB.

Ruby client for Pinecone Vector DB.

Ruby wrapper for the Qdrant vector search database API.

Ruby wrapper for the Weaviate vector search database API.

Sentiment Analysis using Support Vector Machines in Ruby