Understanding Deep Learning

This lecture delves into the fundamentals of deep learning, starting with the goal of finding functions to classify images of cats and dogs using neural networks. It covers the hierarchy of features, the working principles of neural networks, and the challenges in understanding machine learning. The lecture explores the concepts of overfitting, underfitting, and the double-descent phenomenon. It also discusses the teacher-student perceptron model and the importance of sample complexity in machine learning tasks.