Deep Learning with Haiku and JAX

Modern machine learning demands both high performance and clean, modular code. This text-only course introduces you to Haiku, a powerful library designed to simplify neural network construction while preserving the pure functional programming paradigm of JAX. You will discover how to transition from basic mathematical operations to structured, reusable deep learning models. By the end of this course, you will be able to confidently build, initialize, and train state-of-the-art neural networks using Haiku's elegant state management. What you'll learn: Understand core Haiku concepts including transformed functions, state management, and parameter initialization; Build multi-layer perceptrons (MLPs) and deep feedforward architectures from scratch; Configure convolutional neural networks (CNNs) for structured data and image tasks; Apply normalization techniques like batch and layer normalization to stabilize training; Integrate modern training loops using JAX's native optimizers and automatic differentiation. The course starts with essential foundational concepts, covering JAX basics and why Haiku is necessary for managing stateful neural network parameters. From there, you will progress through structured text explanations and code walkthroughs to construct and train complete deep learning models. This course is designed for machine learning beginners, data scientists, and developers who have a basic understanding of Python and want to learn JAX-based deep learning. No prior experience with Haiku is required. Start reading today to unlock high-performance neural networks with Haiku.