Backpropagation Explained for Beginners (Part 2): There Has to Be a Better Way

This piece continues an explanation of backpropagation, highlighting the inefficiency of repeatedly using classical differentiation for each network parameter. It proposes the chain rule as a more systematic solution for calculating gradients in a neural network. The core idea is to follow the 'dependency path,' which traces how a change in a single weight propagates through intermediate calculations to affect the final loss. The process is broken down into smaller, manageable steps by calculating the partial derivative at each stage, such as the derivative of the loss with respect to the prediction.