Think Different

 JAX is an open-source library developed by Google designed for high-performance numerical computing and machine learning research. It provides capabilities for: 1. Automatic Differentiation : JAX allows for automatic differentiation of Python and NumPy functions, which is essential for gradient-based optimization techniques commonly used in machine learning. 2. GPU/TPU Acceleration : JAX can seamlessly accelerate computations on GPUs and TPUs, making it suitable for large-scale machine learning tasks and other high-performance applications. 3. Function Transformation : JAX offers a suite of composable function transformations, such as `grad` for gradients, `jit` for Just-In-Time compilation, `vmap` for vectorizing code, and `pmap` for parallelizing across multiple devices. JAX is widely used in both academic research and industry for its efficiency and flexibility in numerical computing and machine learning. Here's a simple example demonstrating the use of JAX for computing the gr...