Learn JAX from the ground up and discover how its most powerful features fit together in a practical machine learning workflow.
JAX brings together NumPy-style array computing, automatic differentiation, compilation, vectorization, and hardware acceleration. These capabilities make it an exciting tool for modern machine learning, but they can also make JAX feel difficult to approach when you are just getting started.
JAX Fundamentals for Modern Machine Learning provides a clear, practical introduction designed for Python users who want to understand JAX without being overwhelmed by research-level examples or unnecessary complexity.
Instead of treating JAX as a collection of isolated functions, this book teaches the fundamentals progressively. You will begin with arrays and JAX-friendly Python, then build your understanding of gradients, JIT compilation, vectorization, neural networks, and accelerated execution as each concept becomes useful.
Throughout the book, you will gradually build a handwritten digit classifier with a neural network, giving every major JAX concept a practical purpose.
Inside this book, you will learn how to:
Create, reshape, inspect, and manipulate JAX arrays
Understand shapes, data types, broadcasting, and immutable updates
Write pure functions and manage state in a JAX-friendly way
Work correctly with JAX random number keys
Organize model parameters using PyTrees
Calculate derivatives and gradients with jax.grad
Compute values and gradients together with jax.value_and_grad
Speed up numerical functions using jax.jit
Understand tracing, recompilation, and common JIT errors
Vectorize computations and batches with jax.vmap
Combine vmap, grad, and jit in practical workflows
Build a neural network from basic JAX operations
Create loss and accuracy functions
Compute gradients and update model parameters during training
Build and run a compiled training step
Evaluate a trained classifier and make predictions
Save and reload learned model parameters
Understand CPU, GPU, and TPU execution in JAX
Measure performance and work with asynchronous execution
Diagnose common JAX errors involving shapes, tracers, devices, randomness, and training
The examples stay focused on the skills you actually need to understand JAX. You will not be asked to memorize a large framework or copy a finished machine learning system without understanding how it works.
By the end of the book, you will have built a complete beginner-friendly machine learning project while developing a practical understanding of the JAX tools that make modern numerical computing fast, composable, and powerful.
Whether you are coming from Python, NumPy, machine learning, or another numerical computing library, this book will give you the foundation you need to start using JAX with confidence.
