JAX
JAX is a high-performance numerical computing library for Python that provides composable function transformations for automatic differentiation, vectorization, and just-in-time compilation. It is designed to accelerate machine learning research and scientific computing by enabling efficient execution on CPUs, GPUs, and TPUs. JAX builds on NumPy's API, making it familiar to Python users while offering advanced features for optimization and parallelization.
Developers should learn JAX when working on machine learning research, scientific simulations, or any project requiring high-performance numerical computations with automatic differentiation, such as training neural networks or solving differential equations. It is particularly useful for prototyping and scaling models on hardware accelerators like GPUs and TPUs, offering a flexible and efficient alternative to frameworks like PyTorch or TensorFlow for research-oriented tasks.