Explore the intricacies of deep learning and challenge conventional wisdom on generalization in this 40-minute lecture from the University of Central Florida. Delve into topics such as the Universal Approximation Theorem, L2 Regularization, Dropout, and Data Augmentation. Examine randomization tests and their results, leading to thought-provoking conclusions and implications. Investigate explicit and implicit regularization techniques, finite-sample expressivity of neural networks, and draw comparisons to linear models. Conclude by analyzing the role of Stochastic Gradient Descent (SGD) in deep learning, ultimately reshaping your understanding of generalization in neural networks.