Approaches to Understanding and Resolving Issues in Deep Learning

This chapter explores different approaches to address problems in deep learning, including targeting shallow neural networks, sequential linearized learning, and understanding neural networks starting from simple ones. The speaker also discusses the importance of mechanistic interpretability in deep learning and expresses concerns about the potential dangers of GPT models without proper understanding.