Neural Networks: Multilayer Perceptron

This lecture covers the history of neural networks, starting from the Threshold Logic Unit to the development of the multilayer perceptron. It explains the mathematical models of neurons, the perceptron classification rule, and the training algorithm. The lecture delves into the convergence guarantee, the limitations of linear models, and the concept of universal approximation using ReLU functions. It also discusses the backpropagation algorithm, the computational cost, and the challenges in interpreting the operations of a trained multilayer perceptron.