Phase 3: Frameworks & Debugging · ~90 minutes · Python

Introduction to JAX

PyTorch mutates tensors. TensorFlow builds graphs. JAX compiles pure functions. That last one changes how you think about deep learning.

What you will learn

Write pure-function neural network code using JAX's functional API (jax.numpy, jax.grad, jax.jit, jax.vmap)
Explain the key design difference between PyTorch's eager mutation and JAX's functional compilation model
Apply jit compilation and vmap vectorization to accelerate training loops compared to naive Python
Train a simple network in JAX and contrast the explicit state management with PyTorch's object-oriented approach

Introduction

Type: Build
Languages: Python
Prerequisites: Earlier lessons in this course
Time: ~90 minutes

Objective

Learning objectives

Write pure-function neural network code using JAX's functional API (jax.numpy, jax.grad, jax.jit, jax.vmap)
Explain the key design difference between PyTorch's eager mutation and JAX's functional compilation model
Apply jit compilation and vmap vectorization to accelerate training loops compared to naive Python
Train a simple network in JAX and contrast the explicit state management with PyTorch's object-oriented approach

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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