Unleashing the Gradient: How JAX Makes Automated Differentiation Really feel Like Magic
Welcome to the world of JAX, the place differentiation occurs robotically, quicker than a caffeine-fueled coder at 3 a.m.! On this publish, we’re going to delve into the idea of Automated Differentiation (AD), a characteristic on the coronary heart of JAX, and we’ll discover why it’s such a sport changer for machine studying, scientific computing, and some other context the place derivatives matter. The recognition of JAX has been growing recently, due to the rising subject of scientific machine studying powered by differentiable programming.
However maintain on — earlier than we get too deep, let’s ask the fundamental questions.
- What’s JAX?
- Why do we want automated 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 simple to write down code that’s differentiable, parallelizable, and compiled to run on {hardware} accelerators like GPUs and TPUs. The OG crew…
