Multilayer Perceptron: Training and Backpropagation

This lecture covers the training of Multilayer Perceptrons (MLPs) with a focus on gradient-based learning and backpropagation algorithm. It explains the challenges of training MLPs due to the non-convex nature of the problem and the use of gradient descent. The lecture also delves into the backpropagation algorithm, detailing how to compute gradients for each layer. It further explores stochastic gradient descent and its mini-batch variant, highlighting their importance in optimizing the training process of neural networks.