Foundations of JAX Programming and Autograd

Modern machine learning requires speed, scalability, and precise mathematical operations. JAX offers a powerful, functional approach to numerical computing that allows developers to run standard Python code on accelerators with ease. This text-based course guides you through the foundational shifts needed to think in JAX, moving away from stateful object-oriented programming toward pure functions and immutable data structures. You will transition from basic array manipulation to utilizing JAX's powerful transformation engine for automatic differentiation and modern optimization workflows. By reading through clear code walkthroughs and conceptual breakdowns, you will build a solid mental model of how JAX compiles and executes code. What you'll learn: - Understand the core principles of the JAX programming model and functional purity - Manipulate immutable JAX arrays and handle random number generation correctly - Compute exact gradients using the Autograd engine for single and vector-valued functions - Write pure functions that integrate seamlessly with JAX's Just-In-Time compilation - Apply automatic differentiation to simple optimization and machine learning problems - Avoid common JAX pitfalls like stateful side effects and in-place mutations This course begins with fundamental definitions, comparing JAX directly with standard NumPy to highlight key differences in memory management and execution. You will then progress step-by-step through gradient computation, vectorization, and compiling code for maximum performance. This course is designed for software developers, data scientists, and machine learning enthusiasts who are new to JAX and want a clear, beginner-friendly introduction to functional numerical programming. No prior experience with JAX is required, though a basic familiarity with Python and linear algebra is helpful. Start reading today to unlock the power of high-performance functional programming with JAX.