Auto-Differentiation in JAX: A Foundations Guide

Deep learning relies heavily on computing gradients quickly and accurately, but the underlying mechanics of how frameworks calculate these derivatives can often feel like a black box. This text-only course demystifies the JAX programming model, showing you exactly how automatic differentiation works under the hood. By reading through clear, step-by-step explanations, you will transition from writing basic mathematical expressions to designing highly optimized, differentiable programs. What you'll learn: - Understand the core principles of automatic differentiation and how JAX differs from other frameworks - Master the chain rule and reverse-mode accumulation techniques used to compute gradients efficiently - Practice writing functional, pure Python code that integrates seamlessly with JAX's transformation engine - Apply JAX's grad, value_and_grad, and jacobian transformations to standard mathematical functions - Configure and debug gradient computations to prevent common numerical stability issues like overflow and underflow - Explore modern JAX optimization techniques including just-in-time (jit) compilation and vectorization (vmap) for high-performance workflows This course begins with foundational concepts, defining key mathematical terms and the functional programming paradigms that JAX relies on. You will then progress through structured text lessons that unpack the mechanics of JAX transformations, showing you how to trace and compile your code for maximum efficiency. Designed specifically for beginners to JAX, this course requires only a basic familiarity with Python and linear algebra. Take your first step toward mastering high-performance numerical computing today.