Convolutional Neural Networks

This lecture covers the basics of Convolutional Neural Networks (CNNs), starting with fully connected layers and image processing. It then delves into 1D and 2D convolutions, derivative filters, pooling layers, and the architecture of CNNs. The lecture also discusses examples, PyTorch translations, and the impact of CNNs on image classification. It concludes with applications like hand pose estimation, connectomics, and tubularity estimation using UNet architecture.