I always starts with students by explaining how our intuition breaks in high-dimensions (spikiness, volumes,...) and how that carries when fitting/training models or searching optimization space.

It's a very important fundamental for modern data-science, to give one intuition about stochastic gradient descent, high-dimensional models, ... And this book starts with just that. I'm hooked. Thanks for sharing.

See this older hacker news thread as well: https://news.ycombinator.com/item?id=45116849 A Random Walk in 10 Dimensions (2021)