There are 52 repositories under jax topic.
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.
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)
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
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
đź”® A refreshing functional take on deep learning, compatible with your favorite libraries
Functional programming language for signal processing and sound synthesis
PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Built by researchers, for research.
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory utilization in both training and inference.
Monte Carlo tree search in JAX
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.
Fast and Easy Infinite Neural Networks in Python
JAX - A curated list of resources https://github.com/google/jax
A simple, performant and scalable Jax LLM!
A JAX research toolkit for building, editing, and visualizing neural networks.