Introduction to JAX: High-Performance Arrays and Automatic Differentiation

Modern machine learning and scientific computing require tools that are both fast and flexible. JAX offers a powerful, numpy-like interface with the added benefits of automatic differentiation and hardware acceleration, but its functional programming paradigm can be challenging for beginners to grasp. This text-based course guides you through the foundational concepts of JAX, helping you transition from standard Python workflows to high-performance, functional array programming. You will start by learning core definitions, JAX's unique design philosophy, and how to work with immutable arrays. Through clear written explanations and structured code analysis, you will master the mechanics of functional updates, random number generation, and automatic differentiation. What you'll learn: - Understand the core architecture of JAX and how its functional programming paradigm differs from standard libraries - Create and manipulate JAX arrays using functional, side-effect-free operations - Generate predictable random numbers using the explicit state-based pseudo-random number generation system - Perform automatic differentiation using grad, value_and_grad, and jacobian transformations - Inspect intermediate JAX expressions using jaxpr to understand how your code compiles under the hood - Apply Just-In-Time compilation to accelerate execution speeds on modern hardware acceleration patterns The course begins with foundational concepts and essential syntax before guiding you through practical differentiation techniques and compilation strategies. It is designed specifically for beginners with basic Python knowledge, requiring no prior experience with JAX or advanced machine learning frameworks. Start reading today to unlock the power of high-performance functional programming in your numerical workflows.