Deep Learning for humans

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

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

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

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

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, ...

A library for scientific machine learning and physics-informed learning

Scenic: A Jax Library for Computer Vision Research and Beyond

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

JAX-based neural network library

A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit floating point (FP8 and FP4) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.

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

Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023

PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.

Massively parallel rigidbody physics simulation on accelerator hardware.

Functional programming language for signal processing and sound synthesis

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

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

Mastering Diverse Domains through World Models

Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

Monte Carlo tree search in JAX

Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.