Most people use PyTorch without really knowing what's happening underneath. This series breaks the foundations down into the simplest possible explanations — one concept at a time, with code you can run and exactly what goes in and comes out.
This is Part 1 of 5. By the end you'll understand the five building blocks every neural network is made of: creating tensors, doing math on them, reshaping them, computing gradients, and bending them with activation functions.
No assumed knowledge. Let's go.
1. What a tensor actually is
Everything in deep learning is built from one object: the tensor. Don't let the name scare you — a tensor is just a box of numbers.
