Neural Networks: Multilayer Learning

This lecture covers the fundamentals of multilayer neural networks and deep learning, including topics such as back-propagation, convolutional layers, gradient descent, weight decay, and classification. It explains the process of training a neural network, the concept of loss minimization, and the importance of activation functions. The lecture also delves into specific network architectures like LeNet, AlexNet, and VGG-16, illustrating their structure and functionality.