For many years I have been using PyTorch to construct and train deep learning models. Even though I have learned its syntax and rules, something has always aroused my curiosity: what is happening…

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This post explains the internals of PyTorch, including GPU support and automatic differentiation. It also demonstrates how to recreate PyTorch from scratch, including building a tensor library, implementing modules for neural networks, and creating custom optimizers.

Recreating PyTorch from Scratch (with GPU Support and Automatic Differentiation)

#3 — Automatic Differentation (Autograd)