A Detailed Implementation on Equinox with JAX Native Modules, Filtered Transforms, Stateful Layers, and End-to-End Training Workflows

A tutorial shows how to use Equinox with JAX to build neural network modules and train them end to end.

It covers core Equinox concepts such as eqx.Module, static fields, PyTrees, and filtered transforms like filter_jit and filter_grad.

The guide also explains stateful layers including BatchNorm and other components like Dropout, Conv1d, and LayerNorm.

A complete toy regression example ties everything together with Optax and clearer parameter handling.