The post provides a guide to understanding JAX for PyTorch developers, focusing on the parallels between the two frameworks while training a neural network on the Titanic dataset. Key areas covered include modularity, functional programming, data loading, model definition and training steps, and differences in initialization and backpropagation. The tutorial uses the Flax NNX library and also provides a reference for using the older Flax Linen API.
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Modularity with JAXSort: