Learn JAX with Real Code Examples

Functional programming approach with pure functions

Autograd-based differentiation engine

XLA (Accelerated Linear Algebra) backend for compilation

Device-agnostic computations with CPU/GPU/TPU support

Composable transformations (`jit`, `grad`, `vmap`, `pmap`)

Functional transformations applied to pure functions

Array-based numerical computations

JIT-compiled execution for performance

Composable transformations (`grad`, `vmap`, `pmap`)

Integration with ML frameworks for neural networks

Functional API with pure functions

Differentiation engine for automatic gradients

XLA-backed compilation for acceleration

Device abstraction for CPU/GPU/TPU

Composable transformation pipeline for research workloads

Deep reinforcement learning pipelines

Physics and biology simulations requiring gradients

Transformer and neural network research

Generative models and probabilistic programming

Large-scale TPU/GPU research experiments

Functional programming and pure functions

Composability of transformations

High performance via XLA compilation

Automatic differentiation of arbitrary Python functions

Hardware-agnostic with CPU/GPU/TPU support

Vectorize functions using `vmap` for batch efficiency

Parallelize across devices with `pmap`

Use JIT compilation to accelerate repeated computations

Distribute computations across multi-GPU/TPU clusters

Cache intermediate computations when feasible

Upgrade JAX via pip

Verify jaxlib version matches hardware

Test old scripts with latest JAX transformations

Update Flax/Optax dependencies if used

Ensure reproducibility with PRNGKey management