Lecture
This lecture introduces the No Free Lunch Theorem, which states that all optimization algorithms are equivalent when averaged across all possible problems. It explores how deep learning, specifically deep networks optimized with gradient descent, can perform well on real-world problems by matching the structure of the problems. The instructor discusses the geometry of information flow in neural networks and provides examples of how deep networks can match the structure of real-world problems, such as images and music.