This repository is a curated list of resources for machine learning in Ruby, targeting Ruby developers interested in AI and data science. It provides a comprehensive overview of libraries, frameworks, tutorials, and applications, aiming to foster ML development within the Ruby ecosystem.

How It Works

The list categorizes ML resources by algorithm type (e.g., neural networks, decision trees, clustering), application area (e.g., vector search, NLP), and learning materials (tutorials, talks, books). It highlights both pure Ruby implementations and bindings to established libraries in other languages, facilitating integration and leveraging existing ML tooling.

Quick Start & Requirements

This is a curated list, not a runnable project. Specific library installation and usage vary. Links to tutorials and documentation are provided for individual components.

Highlighted Details

• Extensive coverage of Ruby ML libraries, including pure Ruby implementations and bindings for popular frameworks like TensorFlow, XGBoost, and LibSVM.
• Numerous tutorials and presentations from 2007 to 2022, offering a historical and practical perspective on ML in Ruby.
• Includes resources for deep learning, evolutionary algorithms, Bayesian methods, and vector search, demonstrating the breadth of ML capabilities available.
• Features bindings for high-performance computing libraries like Numo and Cumo, enabling GPU acceleration.

Maintenance & Community

The project is community-driven with an open invitation for contributions via pull requests or issues. It encourages engagement through Twitter (#RubyML) and lists community channels like SciRuby Slack and Gitter.

Licensing & Compatibility

The repository itself is licensed under CC0 (Creative Commons Zero), dedicating it to the public domain. Individual libraries listed may have their own licenses, which users must verify for compatibility, especially for commercial use.

Limitations & Caveats

As a curated list, it does not provide a unified ML framework or guarantee the maintenance status of all listed projects. Users must evaluate individual libraries for their specific needs, performance, and ongoing support.