🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Deep Learning for humans

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

Convert Machine Learning Code Between Frameworks

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

Trax — Deep Learning with Clear Code and Speed

Python code for "Probabilistic Machine learning" book by Kevin Murphy

Flax is a neural network library for JAX that is designed for flexibility.

It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.

JAX implementation of OpenAI's Whisper model for up to 70x speed-up on TPU.

TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...

Scenic: A Jax Library for Computer Vision Research and Beyond

Training and serving large-scale neural networks with auto parallelization.

JAX-based neural network library

A retargetable MLIR-based machine learning compiler and runtime toolkit.

🔮 A refreshing functional take on deep learning, compatible with your favorite libraries