Part 2/12:
JAX is described as a Python library designed to mimic NumPy's core functionalities, providing an accessible transition for those familiar with NumPy. Its primary goal is to enable high-performance numerical computing, especially on accelerators like GPUs and TPUs, with minimal code modifications.
Unlike traditional frameworks like TensorFlow or PyTorch, JAX emphasizes functional programming principles such as immutability and pure functions, which simplifies debugging and enhances reproducibility. It achieves this through a set of core features that include automatic differentiation, vectorization, and compilation optimizations.