Lec 07 Backpropagation in Neural Network

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

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.

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.

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

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