Feedforward Neural Networks: Activation Functions and Backpropagation

This lecture covers the basics of feedforward neural networks, focusing on activation functions like sigmoid, tanh, and rectifier, as well as the backpropagation algorithm. It explains the limitations of linear separation surfaces and introduces powerful methods for nonlinear separation, such as kernel methods and feedforward neural networks. The lecture also discusses the challenges of training neural networks, including the dying ReLU problem and the benefits of using activation functions like ELU. It concludes with a detailed explanation of the backpropagation algorithm for training neural networks.