Unleashing the Gradient: How JAX Makes Computerized Differentiation Really feel Like Magic
Welcome to the world of JAX, the place differentiation occurs routinely, quicker than a caffeine-fueled coder at 3 a.m.! On this put up, we’re going to delve into the idea of Computerized Differentiation (AD), a function on the coronary heart of JAX, and we’ll discover why it’s such a recreation changer for machine studying, scientific computing, and some other context the place derivatives matter. The recognition of JAX has been growing currently, because of the rising area of scientific machine studying powered by differentiable programming.
However maintain on — earlier than we get too deep, let’s ask the essential questions.
- What’s JAX?
- Why do we’d like computerized differentiation within the first place?
- And most significantly, how is JAX making it cooler (and simpler)?
Don’t fear; you’ll stroll away with a smile in your face and, hopefully, a brand new instrument in your toolkit for working with derivatives like a professional. Prepared? Let’s dive in.
JAX is a library developed by Google designed for high-performance numerical computing and machine studying analysis. At its core, JAX makes it extremely straightforward to put in writing code that’s differentiable, parallelizable, and compiled to run on {hardware} accelerators like GPUs and TPUs. The OG workforce…