Navigating High-Dimensional Spaces in Machine Learning

This chapter explores the complexities of high-dimensional spaces in the context of machine learning and neural networks, emphasizing the challenges of optimizing functions with multiple local and global optima. It highlights the delicate balance between model complexity, generalization, and risks of overfitting, particularly in relation to diffusion models and their resilience. The discussion further illuminates how high-dimensional data can be interpreted through vector space concepts, bridging mathematical tools and everyday experiences.