🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
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Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
☁️ Build multimodal AI applications with cloud-native stack
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
A hyperparameter optimization framework
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Open Machine Learning Compiler Framework
Low-code framework for building custom LLMs, neural networks, and other AI models
A unified framework for machine learning with time series
Largest list of models for Core ML (for iOS 11+)
modular quant framework.
A modular active learning framework for Python
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
An open source library and framework for deep learning on satellite and aerial imagery.
Earth observation processing framework for machine learning in Python
NFStream: a Flexible Network Data Analysis Framework.
An autoML framework & toolkit for machine learning on graphs.
allRank is a framework for training learning-to-rank neural models based on PyTorch.
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
🎯 A comprehensive gradient-free optimization framework written in Python
TensorLayerX: A Unified Deep Learning and Reinforcement Learning Framework for All Hardwares, Backends and OS.
A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python
Maze Applied Reinforcement Learning Framework