PyTorch as Compression

What this chapter does

PyTorch appears after tensors, forward passes, losses, gradients, updates, batching, and numerical stability are already visible. This chapter maps those known moves to framework names and code patterns.

Lessons

Read these in order.

Start with the chapter introduction, then move through the topic lessons. The order is chosen so each page can reuse ideas from the pages before it.

01
Introduction
Why PyTorch belongs after the underlying deep-learning moves are visible.
02
Tensors
PyTorch tensors as arrays with shape, dtype, device, and gradient settings.
03
Requires Grad and Autograd
`requires_grad`, `backward`, and automatic differentiation as chain-rule bookkeeping.
04
Modules and Forward
Modules as named containers for forward computation and trainable state.
05
Parameters
Trainable tensors, parameter counting, and optimizer-visible state.
06
Loss Functions
Loss functions as scalar training signals, not mysterious framework calls.
07
Optimizers
Zeroing gradients, backward passes, and optimizer steps in the PyTorch loop.
08
Data Loaders
Data loaders as practical batch suppliers for repeated training steps.
09
From Visible NumPy to PyTorch
Mapping each short PyTorch training-loop line back to visible computation.
10
Saving and Loading Preview
State dictionaries as saved learned tensors and experiment state.

Review and practice

Close the chapter deliberately.

Use the conclusion and revision notes before the chapter exercises. Hints and solutions are collected here, while lesson-level exercises reveal their own help inline.

Conclusion

What PyTorch compression establishes before Language Modeling.

Summary and Revision Notes

A compact review of tensors, autograd, modules, parameters, losses, optimizers, data loaders, and saved state.

Exercises

Chapter-level practice for reading PyTorch as compressed deep-learning computation.

Hints

Low-spoiler nudges for the Chapter 17 exercises.

Solutions

Explained solutions for the Chapter 17 exercises.

Before moving on

Map PyTorch tensors to arrays with shapes, dtypes, devices, and gradient settings.
Explain `requires_grad`, `backward`, modules, parameters, losses, and optimizers as compressed known moves.
Read a small PyTorch training step without treating it as magic.
Explain what data loaders and saved state add to a practical experiment.

Where this leads

Language Modeling
Transformers
Reinforcement Learning