The Chain Rule and Backpropagation
Apply composite chain rules to attribute error through output and hidden layers, computing backward gradients and toy parameter updates on paper.
Lessons in this Topic
The Composite Function Chain Rule
Apply composite chain rules to attribute error through output and hidden layers, computing backward gradients and toy parameter updates on paper.
Output Layer Error Attribution Math
Decompose output layer gradients into loss sensitivity, activation slope, and input terms to compute exact credit attribution for output weights.
Hidden Layer Error Backpropagation
Propagate error signals backward through hidden layers by multiplying downstream deltas by transposed weights and intermediate activation slopes.
Toy Parameter Update Step on Paper
Perform a single conceptual parameter update step by scaling computed gradients with a step factor to demonstrate how weights adjust on paper.
The Chain Rule & Backpropagation In Practice
Master composite chain rules, output layer error attribution, hidden backpropagation deltas, and single-step parameter updates through manual calculations.