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This course introduces Flax, a JAX-based machine learning framework, through model construction and training examples. It covers custom modules, parameters and variables, Optax optimization, state management, dropout, BatchNorm, and a CNN trained on MNIST.
Syllabus
Intro - Flax is performant and reproducible
Deepnote walk-through sponsored
Flax basics
Flax vs Haiku
Benchmarking Flax
Linear regression toy example
Introducing Optax Adam state example
Creating custom models
self.param example
self.variable example
Handling dropout, BatchNorm, etc.
CNN on MNIST example
TrainState source code
CNN dropout modification
Outro and summary
Taught by
Aleksa Gordić - The AI Epiphany