Open-source alternatives to Tensorflow

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

🎶 Song similarity search API based on lyrics

OpenAI API Ruby! 🤖❤️ GPT-5 & Realtime WebRTC compatible!

The Swiss Army Knife of Offline AI. Chat, Speak, and Generate Images - Privacy First, Zero Internet. Download an LLM and use it on your mobile device. No data ever leaves your phone. Supports text-to-text, vision, text-to-image

Dynamic Tensor Graph library in Clojure (think PyTorch, DynNet, etc.)

Metric learning algorithms in Python

An open source framework for research in Embodied-AI from AI2.

velox-modelserver

Deep learning for Ruby, powered by LibTorch

A machine learning library for Clojure built on top of Weka and friends

Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query e

Curated list: Resources for machine learning in Ruby

Machine learning platform for Web developers

inference and machine learning in clojure

Noe is a framework for an easier scientific computation in object pascal, especially to build neural networks, and hence the name — noe (Korean:뇌) means brain (🧠). It supports the creation of arbitrary rank tensor and its arithmetical operations. Some of the key features: - Automatic gradient…

robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

Go Scoring API for PMML

A lightweight C library for artificial neural networks

This project is a structured educational curriculum designed to guide developers through the fundamentals of machine learning. It functions as a technical skill builder, offering a curated roadmap of progressive coding challenges that cover core algorithms, statistical concepts, and essential data science libraries. The repository distinguishes itself through an iterative sequencing of content, organizing complex technical topics into a daily progression that facilitates incremental mastery. It integrates third-party academic lectures and educational resources to provide necessary theoretical

Sample applications built using AWS' Amazon Machine Learning.

Mmlspark is a distributed framework for executing machine learning models, data transformations, and AI service integrations across Apache Spark clusters. It functions as a distributed machine learning library and pipeline orchestrator, allowing users to integrate pre-trained cognitive services and custom models into large-scale batch and streaming workflows. The project is distinguished by its ability to incorporate external AI services and web APIs directly into big data pipelines for text and vision analysis. It provides a scalable model training framework that coordinates gradient boostin

Integrate with the latest language models, image generation, speech, and deep learning frameworks like ChatGPT, DALL·E, and Cohere using few java lines.

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

This project is a community-driven knowledge repository and technical learning resource focused on the field of generative artificial intelligence. It serves as a centralized hub for developers and practitioners to access curated research, tutorials, and foundational concepts necessary for building and deploying modern artificial intelligence applications. The platform distinguishes itself through a collaborative, distributed contribution model that aggregates diverse learning materials into a structured, searchable knowledge base. It covers a wide range of specialized topics, including retri