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 leveraging the capabilities of JAX, this chapter will guide you through the intricacies of automatic differentiation, a key feature that distinguishes JAX from other frameworks.

You will discover how JAX streamlines the process of calculating gradients, which are crucial 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 explore practical examples that illustrate how automatic differentiation can be seamlessly 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.