Open-source high-performance numerical computing and automatic differentiation library for machine learning, scientific computing and optimisation.
JAX is an open-source Python numerical computing library supporting automatic differentiation, JIT compilation, GPU/TPU acceleration and a NumPy-style interface. It suits machine learning research, scientific computing, optimisation problems, numerical simulation and high-performance computing experiments.
Use cases: scientific computing, automatic differentiation, machine learning, optimisation, high-performance computing
Note: JAX has a steeper learning curve than plain NumPy and suits users with Python and numerical computing experience.
