Serious science and mathematics readings discussion

It's that time of the year again.

Welcome to all the wonderful new members to the group. We have ballooned from 5 to 20 within a month! Don't hesitate to invite your math/science loving friends, partners, neighbors, colleagues to participate in the collective endeavor of seeking knowledge.

Please mention (at most) two books in the comments in this thread that you would like to read with the group in February, preferably with a short summary on why it is a meaningful choice.

I will create a poll and add entries to it as the suggestions start to flow in.

My two suggestions for February are :

1. Understanding Deep Learning, Simon J. Prince : Neither filled with rigorous mathematical proofs nor concerned with exploring library implementations. In stead, its focus is on conceptual understanding of the theory and what ideas make Deep Learning work. Starts off with the basic perceptron and gradient descent techniques all the way to the most exciting recent development i.e. Transformers.

2. Algorithms and Data Structures for Massive Datasets, Dzejla Medjedovic, Emin Tahirovic, and Ines Dedovic : Goes over data structures like Bloom/quotient filters, Count-min sketch, Hyperloglog and techniques such as sampling from real time data streams and external memory models that are used when dealing with datasets of humongous sizes where the optimal algorithms (typically taught in undergrad curriculum i.e. simple hash tables) are no longer feasible and a tradeoff between space/time constraints and accuracy needs to be made.

I didn't see any suggestions here so didn't create the poll.
Reading Understanding Deep Learning for February.