Neural Networks: Structure and Power

This lecture covers the basic structure and representation power of neural networks, transitioning from linear models to neural networks, the role of hidden layers, the inference and training times, and the theoretical questions in deep learning. It delves into the approximation capabilities of neural networks, the expressive power, and the success of optimization, emphasizing the generalization miracle. The lecture also explores the representational power of neural networks through theoretical results and practical examples, showcasing how neural networks can approximate complex functions effectively.