Deep Learning Building Blocks

This lecture covers the fundamental building blocks of deep learning, including tensors, backpropagation, automatic differentiation, and PyTorch. It explains the concepts of tensors, loss function minimization, forward and backward passes, and the implementation of linear layers, activation functions, and convolutional layers. The lecture also delves into the training loop for MLP classification, recurrent layers, and multi-class classification using neural networks.