Convolutional Networks: Structure and Optimization

This lecture covers the structure and optimization of convolutional networks, focusing on variance-preserving initialization, convolutional layers, weight sharing, pooling, data augmentation, weight decay, and dropout. It explains the importance of learned convolutional filters, skip connections, residuals, and interpretable activations. The lecture also discusses popular architectures like VGG and ResNet, as well as the entangled effects of various methods on datasets like CIFAR10 and ImageNet.