Overview
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This tutorial develops advanced JAX skills for building neural networks, covering state management, PyTrees, automatic differentiation, custom layers, multi-device and multi-machine training, and a brief MAML implementation.
Syllabus
My get started with JAX repo
Stateful to stateless conversion
PyTrees in depth
Training an MLP in pure JAX
Custom PyTrees
Parallelism in JAX TPUs example
Communication between devices
value_and_grad and has_aux
Training an ML model on multiple machines
stop grad, per example grads
Implementing MAML in 3 lines
Outro
Taught by
Aleksa Gordić - The AI Epiphany