Convolutional Neural Networks

This lecture introduces Convolutional Neural Networks (CNNs) and their different layers, such as convolutional, pooling, and fully-connected layers. It covers topics like feature expansion, activation functions, hidden representations, universal approximation, and training strategies like gradient descent and stochastic gradient descent. The lecture also discusses standard CNN architectures like LeNet-5, AlexNet, and VGG, as well as tasks like semantic segmentation and tricks of the trade in deep learning.