JAX is a powerful tool for high-performance machine learning research that allows for automatic differentiation of Python and NumPy code. It supports both reverse-mode and forward-mode differentiation and can handle complex algorithms. One unique feature of JAX is its ability to compile and run NumPy code on accelerators like GPUs and TPUs using XLA. This provides faster computation compared to regular NumPy functions that need to transfer data to the GPU. JAX also offers program transformations like jit(), for speeding up code, grad(), for taking derivatives, and vmap(), for vectorization or batching. These transformations can be composed together to optimize code further.
https://jax.readthedocs.io/en/latest/notebooks/quickstart.html