W.Welcome to the world of JAX, where differentiation happens automatically, faster than a caffeine-fueled coder at 3am! In this post, we'll delve into the concept of automatic differentiation (AD), a core feature of JAX, and explore why it's a game-changer for machine learning, scientific computing, and every other context. where derivatives matter. JAX's popularity has increased lately thanks to the emerging field of scientific machine learning powered by differentiable programming.
But wait, before we get too deep, let's ask the basic questions.
- What is JAX?
- Why do we need automatic differentiation in the first place?
- And most importantly, how does JAX make it cooler (and easier)?
Don't worry; You'll leave with a smile on your face and hopefully a new tool in your toolkit to work with derivatives like a pro. Ready? Let's dive in.
JAX is a library developed by Google designed for high performance numerical computing and machine learning research. In essence, JAX makes it incredibly easy to write code that is Differentiable, parallelizable, and compiled to run on hardware accelerators such as GPU and TPU. The OG team…
