Mathematics for Computer Science — Lehman, Leighton, Meyer (MIT 6.042). (free) The single best broad foundation text for this whole part; covers logic, number theory, graphs, probability in one book.
How to Bake Pi — Eugenia Cheng. Builds real mathematical intuition from everyday analogies; great for Ch. 3–5.
Naked Statistics — Charles Wheelan. The most readable plain-English introduction to Ch. 8–9's ideas, no formulas required to get the intuition.
The Art of Computer Programming, Vol. 1 — Donald Knuth. Denser, but the canonical reference for Ch. 1's bit-level fundamentals.

Blogs / articles

BetterExplained (betterexplained.com) — intuitive, visual explanations of exponents, logs, and Euler's number; consistently the best "aha" source for Ch. 5–6.
Math Is Fun (mathsisfun.com) — quick, clear reference for any formula in Ch. 1–9 when you need a refresher, not a lecture.

Video

3Blue1Brown (YouTube) — "Essence of..." series; short, visual, intuition-first. Start here for anything that feels abstract.
Khan Academy — full, free, structured courses covering Ch. 1–9 in order with exercises.
Professor Leonard (YouTube) — long-form, thorough lecture-style algebra and calculus, good if you want the full derivation, not just intuition.

Books

Linear Algebra Done Right — Sheldon Axler. Rigorous, conceptual treatment of Ch. 10–11 for readers who want the "why," not just the mechanics.
Introduction to Algorithms (CLRS) — Cormen, Leiserson, Rivest, Stein. The standard reference for Ch. 17–18 (graphs, complexity).
Probability and Statistics for Computer Science — David Forsyth. Written specifically for engineers, ties directly into Ch. 14–16.
Elementary Number Theory — David Burton. Approachable, thorough treatment of Ch. 19, written as preparation for cryptography.

Blogs / articles

Distill.pub (archived but still excellent) — beautifully illustrated, rigorous explanations bridging Ch. 10–13 and ML.
Terence Tao's blog (terrytao.wordpress.com) — advanced but exceptionally clear; useful once the basics from Part 2 are solid.

Video

MIT OCW 18.06 Linear Algebra — Gilbert Strang's full course, free, the standard reference lecture series for Ch. 10–11.
3Blue1Brown — "Essence of Linear Algebra" and "Essence of Calculus" series, directly aligned with Ch. 10–13.
StatQuest with Josh Starmer (YouTube) — short, clear videos on Ch. 14–16 (distributions, Bayes) with a friendly, repetition-based teaching style.

Books

Mathematics for Machine Learning — Deisenroth, Faisal, Ong. (free PDF) Purpose-built bridge from Part 2 math straight into ML; the most direct next step after this guide.
Deep Learning — Goodfellow, Bengio, Courville. (free online) The standard reference for Ch. 20–21 in full mathematical depth.
Pattern Recognition and Machine Learning — Christopher Bishop. Denser, more classically statistical treatment of Ch. 22.
Information Theory, Inference, and Learning Algorithms — David MacKay. (free) The best single book connecting Ch. 23 to ML and coding theory.

Blogs / articles

colah's blog (colah.github.io) — Christopher Olah's visual explanations of backpropagation and neural network internals; the best intuition bridge for Ch. 21.
Jay Alammar's blog (jalammar.github.io) — "The Illustrated Transformer" and related posts; excellent for seeing Ch. 10's matrix math in a real production architecture.
Sebastian Raschka's blog and newsletter — practical, math-grounded deep-learning explanations aimed at working engineers.

Video

Andrej Karpathy — "Neural Networks: Zero to Hero" (YouTube series). Builds backpropagation and a GPT from scratch, in code, matching this guide's "derive it, don't memorize it" approach to Ch. 21.
3Blue1Brown — "Neural Networks" series, the visual companion to Ch. 20–21.
StatQuest — ML-specific playlist covering gradient descent, cross-entropy, PCA, and regularization at an approachable pace.

Books

Serious Cryptography — Jean-Philippe Aumasson. The best modern, practitioner-focused treatment of Ch. 24.
Handbook of Applied Cryptography — Menezes, van Oorschot, Vanstone. (free PDF, author-hosted) The classic deep reference for the number theory behind Ch. 19 and 24.
Cryptography Engineering — Ferguson, Schneier, Kohno. Practical, implementation-aware follow-up once the math in Ch. 24 is solid.
Thinking Strategically — Avinash Dixit & Barry Nalebuff. The most accessible general introduction to Ch. 28's game theory.

Blogs / articles

A Few Thoughts on Cryptographic Engineering — Matthew Green (blog.cryptographyengineering.com). Deep, readable dives into real cryptographic failures and design decisions.
Troy Hunt's blog (troyhunt.com) — practical, real-incident-driven writing on password entropy, breaches, and the k-anonymity design used in Have I Been Pwned (Ch. 26).

Video

Computerphile (YouTube) — short, clear explainers on RSA, Diffie-Hellman, and hashing, well matched to Ch. 19 and 24.
Khan Academy's cryptography series — a free, structured path through the same modular-arithmetic-to-RSA progression as Ch. 19/24.

Books

Computer Networking: A Top-Down Approach — Kurose & Ross. The standard reference for Ch. 25 (congestion control, queueing) and Ch. 27.
The Art of Computer Systems Performance Analysis — Raj Jain. The deep, rigorous reference for queueing theory and capacity planning math in Ch. 25.
TCP/IP Illustrated, Vol. 1 — W. Richard Stevens. Ties Ch. 25's AIMD math directly to real packet traces.
Feedback Control for Computer Systems — Philipp K. Janert. A rare, excellent book applying Ch. 29's control theory specifically to software systems (autoscalers, rate limiters) rather than mechanical/electrical ones.

Blogs / articles

High Scalability (highscalability.com) — real-world architecture write-ups that repeatedly touch Ch. 25's queueing and capacity math.
The Cloudflare Blog (blog.cloudflare.com) — frequent, math-heavy postmortems and deep dives directly illustrating Ch. 18, 25, and 27 concepts in production incidents.

Video

MIT 6.033 (Computer System Engineering) lecture recordings — covers distributed systems and networking fundamentals underlying Ch. 17/25.
Practical Networking (YouTube) — clear walkthroughs of subnetting/CIDR math (Ch. 25) for hands-on practice.

Further Reading