Deep Learning Building Blocks

This lecture covers the fundamental building blocks of deep learning, including tensors, loss functions, automatic differentiation, and convolutional layers. It explains the concepts of tensors, gradient computation, and the use of activation functions. The lecture also delves into the practical implementation of linear layers, autograd, and optimization techniques like SGD. Additionally, it explores the complexity of backpropagation and provides optional reading material for advanced topics.