This is a Python library for numerical computation that combines Just-In-Time (JIT) compilation, automatic differentiation (Autograd), and a linear algebra compiler (XLA), with a usage pattern similar to NumPy. However, JAX is faster, more memory-efficient, and supports automatic differentiation, automatic vectorization, parallel computing, and more.