Convolutional Neural Networks

This lecture covers the concepts of convolution, cross-correlation, and equivariance to translations in the context of neural networks. It explains discrete 2D convolutions, zero padding, max pooling, and the structure of a convolutional layer. The lecture also delves into the idea of multiple channels per layer and provides examples like LeNet5 and AlexNet. Additionally, it touches on recurrent neural networks and their applications.