Neural Network Training and Testing with JAX and Flax

Modern deep learning requires tools that are fast, flexible, and scalable. JAX and Flax have emerged as the premier ecosystem for researchers and engineers who need high-performance numerical computing and clean neural network APIs. This text-based course guides you through the core concepts of functional programming in machine learning, showing you how to build and optimize models from scratch. You will transition from understanding basic array operations to managing stateful neural networks, structuring clean training loops, and evaluating model performance with modern best practices. What you'll learn: - Understand the foundational mechanics of JAX, including pure functions, immutable state, and just-in-time (JIT) compilation - Define neural network architectures using the Flax Linen API, focusing on convolutional layers - Manage model parameters and state updates cleanly without side effects - Configure optimization pipelines using Optax to update weights efficiently - Write custom training and evaluation loops that calculate loss and track performance metrics - Apply modern debugging and profiling techniques to identify bottlenecks in your execution pipeline We begin with the absolute basics, exploring key terminology, the philosophy of functional programming, and how JAX differs from traditional deep learning frameworks. From there, you will progress step-by-step through data pipeline setup, loss function design, and state management, culminating in a complete training and testing pipeline. This course is designed for software developers, data scientists, and machine learning enthusiasts who are new to JAX and Flax. No prior experience with these specific libraries is required, though a basic understanding of Python and core machine learning concepts will help you get the most out of the material. Start reading today to unlock the power of high-performance functional deep learning.