Deep Learning

This lecture covers the fundamentals of deep learning, starting with data representations, attributes, bag of words, histograms, data pre-processing, missing and noisy data handling, data normalization, artificial neural networks, activation functions, training techniques like gradient-based learning and backpropagation, and the architecture of convolutional neural networks. The instructor explains the concepts using examples and visualizations, such as simple artificial neural networks, bag of visual words, and multilayer perceptrons. The lecture also delves into standard architectures like LeNet-5, AlexNet, VGG, ResNet, and U-Net, as well as tricks of the trade in deep learning, such as pre-training, data augmentation, and normalization techniques.