Deep Learning: No Free Lunch Theorem and Inductive Bias

This lecture discusses the application of deep learning in reinforcement learning, focusing on the No Free Lunch Theorem and its implications for neural networks. The instructor explains how deep networks improve generalization properties and why they perform well on real-world problems. The No Free Lunch Theorem states that no optimization algorithm is universally superior across all problems, highlighting the importance of matching the algorithm's structure to the problem's characteristics. The lecture emphasizes the significance of inductive bias in neural networks, illustrating how prior knowledge can enhance learning efficiency. Examples include convolutional networks, which leverage local translation invariance for image recognition, and the use of Gabor filters for preprocessing. The instructor also explores the role of inductive bias in reinforcement learning, discussing how it can guide the selection of actions based on similar input states. The session concludes with practical insights on implementing these concepts in real-world scenarios, reinforcing the necessity of using prior knowledge in algorithm design.