Automatic Differentiation in JAX

Automatic differentiation is a fundamental technique in modern machine learning and numerical optimization, enabling efficient computation of derivatives without the manual labor typically involved in calculus. By making use of the capabilities of JAX, this chapter will guide you through the details of automatic differentiation, an important feature that distinguishes JAX from other frameworks.

You will find how JAX simplifies the process of calculating gradients, which are needed for training machine learning models. We'll begin by examining the fundamental principles behind automatic differentiation and its importance in computational efficiency. As you progress, you'll gain hands-on experience using JAX's grad function to compute derivatives of complex functions with ease.

Moreover, we'll cover practical examples that show how automatic differentiation can be smoothly integrated into various machine learning workflows. By the end of this chapter, you'll have a solid understanding of how automatic differentiation operates within JAX, equipping you with the skills needed to enhance your data science projects.