Deep Learning: Theory and Applications

This lecture covers the mathematics behind deep learning, focusing on neural networks, optimization algorithms, and their applications in computer vision tasks. It discusses the power of linear classifiers, the importance of neural networks for non-linearly separable data, and the exponential growth of neural network sizes. The instructor explains the landscape of empirical risk minimization with multilayer networks, the challenges in deep learning and machine learning applications, and the need for robustness. The lecture also addresses the popularity of deep learning since 2010, the role of convolutional architectures in computer vision, and the inductive bias that makes convolution work well. It concludes with a discussion on the deep learning paradigm and the common components in a deep learning pipeline.