Lec 07 Backpropagation in Neural Network

Summary

This lecture explains the mathematical foundations of the backpropagation algorithm, demonstrating how to compute gradients in a neural network for weight optimization.

Highlights

Introduction to Backpropagation00:00:00

The session introduces backpropagation as the essential method for calculating the gradient of the loss function required for updating neural network weights during training.

Toy Neural Network Setup00:00:45

A simple neural network architecture is defined, consisting of an input layer, one hidden layer, and an output layer, using sigmoid activation functions and mean squared error as the loss function.

Mathematical Derivation of Gradients00:04:38

The lecture provides a step-by-step derivation for the gradient of the loss function with respect to weights in the output layer using the chain rule.

Backpropagating to Hidden Layers00:17:37

The derivation continues to explain how to calculate gradients for weights in the hidden layers by propagating the error backwards from the output layer.

Weight Updation and Summary00:32:57

The session concludes with the final weight update rule using the calculated gradients and learning rate, summarizing the iterative nature of the training process.