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PyTorch

Tensors, autograd and neural networks in Python.

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This course has 3 lessons. Start with the first one and use the next / previous links at the bottom of each lesson — the sidebar keeps the whole course in order.

Lessons

  1. Tensors and autogradThe core data structure, moving work to the GPU, and the automatic differentiation that makes training possible.
  2. The training loopA complete, correct training and evaluation loop — loss, optimiser, batching, and validation that you can trust.
  3. Modules, data and savingWriting models as nn.Module, feeding data with Dataset/DataLoader, and persisting weights correctly.

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Last refreshed 2026-09-17.