Introduction to Optax and Optimizers in JAX

Optimizing neural networks requires a deep understanding of gradient descent and the algorithms that control learning rates. This text-based course introduces you to Optax, the standard optimization library for the JAX ecosystem, designed to make gradient transformations modular and composable. You will start with foundational optimization concepts before diving into practical code implementations. By working through this course, you will transition from understanding basic gradient descent to building custom, multi-step optimization pipelines for modern deep learning architectures. What you'll learn: - Understand the core philosophy of Optax and how it integrates with JAX's functional programming model - Configure and apply key optimization algorithms including SGD, Adam, and AdaBelief - Chain multiple gradient transformations together to implement weight decay, gradient clipping, and learning rate schedules - Manage optimization states cleanly across training iterations without side effects - Implement modern best practices such as learning rate warmups and cosine schedules for stable training This course begins with essential mathematical and conceptual definitions of gradient transformations, then guides you step-by-step through setting up optimizers, transforming gradients, and updating model parameters in clean, idiomatic JAX code. This course is designed for beginners to intermediate developers in machine learning who want to master optimization in JAX. No prior experience with Optax is required, though a basic familiarity with Python and linear algebra is helpful. Start reading today to master gradient optimization in the JAX ecosystem.