Deep Learning: Convolutional Neural Networks

This lecture covers the fundamentals of deep learning, focusing on Convolutional Neural Networks (CNNs). It starts with a recap of neural network basics, then delves into the architecture and training of CNNs. The lecture explains the concept of transposed convolutions for fully-convolutional networks and explores standard CNN architectures like LeNet-5, AlexNet, VGG, and ResNet. It also discusses semantic segmentation using CNNs and tricks of the trade in deep learning, such as pre-training, learning rate scheduling, cyclic learning rates, and data augmentation. The lecture concludes with a discussion on adversarial examples in deep networks.