Backpropagation: tracing the training signal backward
3Blue1Brown explains how training examples influence weights and biases across a neural network.
Understand the backward pass
Grant Sanderson starts from a digit classifier's desired output changes and works backward through its layers. The 2017 explanation connects individual training examples with parameter adjustments, then introduces averaging and mini-batches. Knowing forward propagation and gradient descent makes this progression easier to follow.
Understand training difficulties
Google's training notes add context about vanishing gradients, exploding gradients and inactive ReLU units. They also explain that common libraries automate backpropagation. Read these as additional training considerations; debugging their causes usually requires calculus knowledge.
The focus here is intuition. This guide follows the creator's detailed written lesson.
