Learn JAX with Real Code Examples

Learn Python and NumPy fundamentals

Understand functional programming principles

Practice autograd and `grad` on simple functions

Experiment with `jit`, `vmap`, and `pmap`

Build research or ML pipelines using Flax/Optax/JAX

Week 1: NumPy-like computations and arrays

Week 2: Automatic differentiation with `grad`

Week 3: JIT compilation and benchmarking

Week 4: Vectorization with `vmap` and parallelization with `pmap`

Week 5: Full ML pipelines with Flax/Optax and TPU/GPU acceleration

What is JAX and how is it different from NumPy?

Explain `grad`, `jit`, `vmap`, and `pmap` with examples

How does JAX handle GPU/TPU acceleration?

How do you implement a neural network using JAX?

What are the advantages of composable transformations in JAX?

Array = JAX array (like NumPy)

grad(f) = derivative of function f

jit(f) = compiled function for speed

vmap(f) = vectorized map over batches

pmap(f) = parallel map across devices

Programming with JAX

Deep Learning with JAX and Flax

Hands-On Differentiable Programming

Functional Machine Learning in Python

JAX for Scientific Computing

JAX official tutorials

Flax/Optax example notebooks

YouTube walkthroughs for JAX ML pipelines

Colab examples for GPU/TPU acceleration

Hands-on exercises for vectorization and parallelization

https://jax.readthedocs.io/

https://github.com/google/jax

JAX GitHub repository

StackOverflow JAX tag

Flax/Haiku communities

Reddit ML and AI research channels

Discord/Slack ML groups

JAX GitHub repository

Google Research JAX discussions

StackOverflow JAX tag

Flax/Haiku communities

Reddit and Discord AI/ML channels