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#hide
!pip install -Uqq fastbook
import fastbook
fastbook.setup_book()

[[chapter_foundations]]

A Neural Net from the Foundations

This chapter begins a journey where we will dig deep into the internals of the models we used in the previous chapters. We will be covering many of the same things we’ve seen before, but this time around we’ll be looking much more closely at the implementation details, and much less closely at the practical issues of how and why things are as they are.

We will build everything from scratch, only using basic indexing into a tensor. We’ll write a neural net from the ground up, then implement backpropagation manually, so we know exactly what’s happening in PyTorch when we call loss.backward. We’ll also see how to extend PyTorch with custom autograd functions that allow us to specify our own forward and backward computations.