**Deep Learning: Foundations and Concepts**

*Christopher M. Bishop and Hugh Bishop*

🐢

An in-depth introduction to the various pivotal concepts, algorithms, and architectures behind the modern deep learning revolution. Quite mathematically notation dense which admittedly made some sections difficult to read, but I generally found myself leaving each chapter satisfied. Likely due to the large scope of the book, some areas are rigorously reviewed while others I felt could be expanded upon more. In particular, some topics I would have liked to have seen included more were universal approximation theorems, state space models, and deep reinforcement learning. Overall an educational textbook that I would highly recommend for learning the fundamental technical aspects behind deep learning.

**Rating:** 8/10

**How to Solve It**

*George Pólya*

🎯

*Can you see it at a glance?* A mathematically-inclined study into the practice of heuristic (or as the book terms it, “plausible”) reasoning for general problem solving. Well-written with clear example figures, the book adopts a sort of nonlinear structure with references to succeeding sections and material – although it is still best read front to back. The majority of content revolves around the *Dictionary*, which contains short musings on various skills, stories, and topics related to solving problems. Some personal favorites of mine were: Determination, hope, success; Generalization; Heuristic/Heuristic reasoning; Modern heuristic; Pappus; The future mathematician; and Working backwards.

**Rating:** 9/10

**Fractals: On the Edge of Chaos**

*Oliver Linton*

An informative and well-diagrammed overview of fractal systems and some of the math behind them. The author’s passion for the subject is clear throughout the book, and I spent a lot of time admiring the book’s many illustrations of fractal geometry.

**Rating:** 7/10