Neural Networks

This lecture covers the fundamentals of neural networks, including the structure of hidden layers, weight adjustments, activation functions, and the universal approximation theorem. It delves into the training process, the challenges of non-polynomial functions, and the concept of rectified linear activation. The lecture also explores the application of neural networks in various fields and discusses the importance of feature learning.