Neural Networks: Two-layer Networks and Backpropagation

This lecture covers the theory behind two-layer neural networks and the backpropagation algorithm, explaining the concepts of Hilbert space, reproducible kernel Hilbert space, positive semidefinite matrices, and the universal approximation theorem. It also delves into the practical aspects of learning feature spaces, activation functions, and the process of approximating continuous functions.