Fans pick 64 books like The Golden Ticket

A primary reason to love math is because of its usefulness. This book shows two sides of math’s applicability, since it is so ubiquitously used in various algorithms.

On the one hand, such usage can be good, because statistical inferences can make our life easier and enrich it. On the other, when these are not properly designed or monitored, it can lead to catastrophic consequences. Mathematics is a powerful force, as powerful as wind or fire, and needs to be harnessed just as carefully.

Cathy O’Neil’s message in this book spoke deeply to me, reminding me that I need to be always vigilant about the subject I love not being deployed carelessly.  

A graphic novel about Ada Lovelace, Charles Babbage, and their quixotic Victorian escapades designing computers and algorithms nearly a century before their time? As fascinating as that may already sound, it’s only the beginning. This is the only graphic novel I’ve read that has footnotes to the footnotes—immensely amusing footnotes. While reading this book, I feel constantly in the presence of insane genius. (But please read this book on physical paper. It is a work of art.)

I always find myself applying algorithmic thinking in my everyday life—it affects the way I put away dishes, navigate to the store, and organize my to-do lists. And I think others could benefit from that mindset.

So, when I read this book, my reaction was "Yes! That's what I want people to know. I just wish I could have said it that well!" The authors (who I know, but didn't know they wrote a book together), did a fantastic job of selecting algorithms with deep human connections. Really! And they explain them just right, without getting too mathematical but while still hitting the key ideas with clarity and accuracy. Fantastic!

In a world full of buzzwords about the latest AI algorithms, Leslie Valiant is one of the few authors who can shrug off the hype and deliver something truly profound. Valiant is a Turing Award winner whose work has changed the face of computational learning theory. His ideas show how algorithms can arise naturally, as part of evolution. (He calls them ecorithms.) These ideas are stunningly original. I admire Valiant’s no-nonsense prose style immensely—it’s such a pleasure to read something where the ideas and explanations are simultaneously so clear and so deep that they provide their own star power.

Yes, it’s a textbook, albeit a particularly well-written one. You may already have it on your shelf, if you’ve taken a programming class or two.

I’m way too old to have used CLRS as a textbook, though! For me, it’s an effectively bottomless collection of neat little ideas—an easy-to-describe problem, then a series of increasingly clever ways to solve that problem. How often do I end up using one of those algorithms? Not very often! But every time I read the description of an algorithm, I get a nugget of pure joy from the “aha” moment when I first understand how it works.

The building blocks of software are algorithms, so here our journey continues after you have established a deep understanding of programming languages.

Modern software is predominantly distributed, and since this book doesn't assume much it is the perfect introduction to algorithm analysis, concurrency, and distributed systems. And the best part is that you can just jump in and build these algorithms yourself.

This book is essential in that it follows a systematic and scientific approach to software development, advocating for clarity in expressing algorithms, providing a rigorous framework for designing and reasoning about programs, and, fundamentally, always focusing on formal methods and mathematical techniques to ensure correctness and efficiency in programming code.

Most importantly, the book doesn’t just show you how to prove programs correct, but also teaches how to arrive from a definition to an efficient and correct solution, so I would recommend this to every developer.

This book provides an excellent description of the various kinds of machine learning approaches and asks the question of whether there will be a Master Algorithm, one single (universal) algorithm that learns all kinds of tasks from data. The author, Pedro Domingos, introduces the different approaches to building intelligence and the research tribes exploring them – Symbolists (with its foundations in Philosophy and Logic), Connectionists (foundations in Neuro/Cognitive Science), Evolutionaries (foundations in Evolutionary Biology), Bayesians (statistical foundations), and Analogizers (Psychology). He also introduces some of his own ideas on what the master machine learning algorithm might look like. It’s a really useful primer for those who are not deeply immersed in machine learning but it’s written for readers with at least a basic engineering and computer science background.

Knuth’s unique mix of playfulness and rigor came to define computer science as an intellectual discipline: computer science didn’t really have anything to do with computers, but everything to do with a particular way of seeing the world.  Just browse and wonder at the beauty and precision achieved in these books.   

Volume 3 (Sorting and Searching) is my personal favorite, and I encourage you to start there. During the pandemic, I finally got around to reading Volume 4A (Combinatorial Algorithms), which was published thirty plus years after Volume 3. It was the same feeling I had watching the movie The Phantom Menace years after growing up with the original Star Wars trilogy. I had forgotten just how unique and distinctive Knuth’s Art of Computer Programming is.

I’ve probably spent more time with this book than with any other technical book. It’s one of those books where you can get as much out of it as you like by revisiting the material at increasing levels of depth. I appreciate the conversational but rigorous tone, the solved examples, the false starts, the intuition that the authors build, and the applications of algorithm design to realistic problems. The Maximum Flow chapter is not to be missed.