Convolutional Neural Networks: Structure & Regularization

This lecture delves into Convolutional Neural Networks (CNNs), exploring their structure, key components, and regularization techniques. The instructor explains the importance of convolutional layers, data augmentation, weight decay, and dropout in enhancing model performance and generalization. The lecture also covers the concept of skip connections in deep networks and the impact of weight decay on optimization dynamics. Practical examples and comparisons of different models on image classification tasks are provided to illustrate the effectiveness of CNNs and regularization methods.