Deep Learning: Convolutional Networks

This lecture covers deep learning concepts related to convolutional neural networks, including the extension of multilayer perceptrons, autograd, stochastic gradient descent, convolutions, pooling, backpropagation, and the use of dynamic graphs for derivatives calculation. The instructor explains the forward propagation, backward propagation, and parameter derivatives calculation in deep networks. The lecture also discusses the challenges of writing complex models and the advantages of using libraries like PyTorch for automatic graph construction. Practical implementations of stochastic gradient descent and mini-batch processing are presented, along with the importance of shared parameters in deep learning models.