Conclusion

PyTorch is useful because it shortens deep-learning work without changing the underlying ideas.

You can now read the common pieces of a small PyTorch experiment:

• tensors carry arrays, shapes, dtypes, devices, and gradient settings
• requires_grad tells autograd which values need derivatives
• modules package forward computation with trainable state
• parameters are the tensors the optimizer updates
• losses turn model output into scalar training signals
• optimizers turn gradients into parameter updates
• data loaders feed batches into repeated training steps
• saved state records learned values and experiment progress

This chapter also marks a shift. Earlier chapters made the machinery visible. Later subjects will use framework code when writing every operation by hand would distract from the model idea.

That does not mean the course becomes framework-first. It means PyTorch becomes a compact notation for ideas the reader has already met.

The next major subject, Language Modeling, uses this foundation to study models that predict sequences. There, the details of tokens, context, recurrence, attention precursors, and probability over vocabulary become the center of the story.