Here are 66 books that Algorithms fans have personally recommended if you like
Algorithms.
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I love pulling back the curtain on how computers work. I want to go from thinking "that's magic" to "that's unbelievably clever but now I understand how it works." Each time I am able to do this feels like a hard-won but therefore meaningful step toward understanding. I want others to experience this empowering shift. I have a PhD in computer science education, and I want to know what helps people learn. More importantly, I want to know how we can use such discoveries to write more effective books. The books I appreciate most are those that demonstrate not only mastery of the subject matter but also mastery of teaching.
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.
Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science. August 6, 2009 Author, Jon Kleinberg, was recently cited in the New York Times for his statistical analysis research in the Internet age.
I love pulling back the curtain on how computers work. I want to go from thinking "that's magic" to "that's unbelievably clever but now I understand how it works." Each time I am able to do this feels like a hard-won but therefore meaningful step toward understanding. I want others to experience this empowering shift. I have a PhD in computer science education, and I want to know what helps people learn. More importantly, I want to know how we can use such discoveries to write more effective books. The books I appreciate most are those that demonstrate not only mastery of the subject matter but also mastery of teaching.
How do we know whether an algorithm is correct? While intuition is helpful, for tricky algorithms nothing beats the formal proof. But I don’t want a proof for proof’s sake: I want it to deepen my understanding of the algorithm. The proofs in this book series are the best I’ve seen: they are self-contained, described step by step, and serve to sharpen your understanding of what the algorithm is really doing. Couple that fact with the self-check questions, exercises with solutions, and associated video lectures, and what we have here is a wonderful resource for the motivated algorithms learner.
Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and database system implementation. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject---a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. The exposition is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. Part 1 of the book series covers asymptotic analysis and big-O notation, divide-and-conquer algorithms and the…
I love pulling back the curtain on how computers work. I want to go from thinking "that's magic" to "that's unbelievably clever but now I understand how it works." Each time I am able to do this feels like a hard-won but therefore meaningful step toward understanding. I want others to experience this empowering shift. I have a PhD in computer science education, and I want to know what helps people learn. More importantly, I want to know how we can use such discoveries to write more effective books. The books I appreciate most are those that demonstrate not only mastery of the subject matter but also mastery of teaching.
This is the book that started it all for me… and I think it holds up just fine today. I see value in confronting the old Pascal code every so often: it’s a reminder of how little we need in order to make our algorithms fast, and how much is happening behind the scenes by our modern programming languages. To this day this book has some of my favourite presentations of Dijkstra’s Algorithm and sorting.
The authors' treatment of data structures in Data Structures and Algorithms is unified by an informal notion of "abstract data types," allowing readers to compare different implementations of the same concept. Algorithm design techniques are also stressed and basic algorithm analysis is covered. Most of the programs are written in Pascal.
I love pulling back the curtain on how computers work. I want to go from thinking "that's magic" to "that's unbelievably clever but now I understand how it works." Each time I am able to do this feels like a hard-won but therefore meaningful step toward understanding. I want others to experience this empowering shift. I have a PhD in computer science education, and I want to know what helps people learn. More importantly, I want to know how we can use such discoveries to write more effective books. The books I appreciate most are those that demonstrate not only mastery of the subject matter but also mastery of teaching.
For an overview book that focuses on intuition—a book that is intentionally designed to evade formality—to make my list, it has to be really, really good. This one is. I appreciate the inclusion of real code in multiple programming languages and the step-by-step traces of algorithms. I appreciate the care taken with the Big O material and the way that abstract data types are introduced. This is one of very few books whose recursion material I like—the ‘napkin’ approach to recursion is wonderfully done.
If you thought that data structures and algorithms were all just theory, you're missing out on what they can do for your code. Learn to use Big O Notation to make your code run faster by orders of magnitude. Choose from data structures such as hash tables, trees, and graphs to increase your code's efficiency exponentially. With simple language and clear diagrams, this book makes this complex topic accessible, no matter your background. This new edition features practice exercises in every chapter, and new chapters on topics such as dynamic programming and heaps and tries. Get the hands-on info you…
Computers have fascinated me since my childhood, having fond memories of my dad's ZX81, but even so I played around I was never truly captured by the programming until I recognized it as a way of writing rather than raw engineering. Through my studies of media sciences I found my fascination with how language can shape perception, and through my work in developer advocacy, I found how communities are shaped as well. Now I am fascinated with how different programming languages can shape thinking, having had the opportunity to solve problems at large companies in nonmainstream languages.
Smalltalk is not as common or popular as it once was, but the influence its design had on programming is hard to overstate.
Reading this book has changed how I think about object orientation, and how I design systems, learning the patterns of Smalltalk makes it clear what object oriented design is really intended to achieve.
This classic book is the definitive real-world style guide for better Smalltalk programming. This author presents a set of patterns that organize all the informal experience successful Smalltalk programmers have learned the hard way. When programmers understand these patterns, they can write much more effective code. The concept of Smalltalk patterns is introduced, and the book explains why they work. Next, the book introduces proven patterns for working with methods, messages, state, collections, classes and formatting. Finally, the book walks through a development example utilizing patterns. For programmers, project managers, teachers and students -- both new and experienced. This book…
I’ve spent most of my life writing code—and too much of that life teaching new programmers how to write code like a professional. If it’s true that you only truly understand something after teaching it to someone else, then at this point I must really understand programming! Unfortunately, that understanding has not led to an endless stream of bug-free code, but it has led to some informed opinions on programming and books about programming.
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.
Once upon a time, I was a computer science researcher, working in the research labs of companies like Microsoft and Hewlett-Packard. Later I started teaching computer science to college students and writing books about algorithms. I love computers and I love algorithms. Most of all, I love explaining algorithms to other people. In fact, one of my most important missions in life is to advance the public understanding of computer science and algorithms. So if you read any of the books on my list, you’ll bring me one step closer to achieving my mission. Go ahead, read one now!
The most important unanswered question in computer science has a huge public relations problem. Back in the 1970s, this question became known as “P=NP?”—and who could write an exciting book about that? Luckily for us, Lance Fortnow can. As one of the world’s foremost experts on P-vs-NP, he takes us on a wild and truly accessible ride through the most important question about computing. I’ve seen many attempts at making “P=NP?” accessible/understandable/intriguing for non-experts. But Fortnow nails it like nobody else, reformulating P-vs-NP as a search for one of the golden tickets in Charlie and the Chocolate Factory. (Which is another one of my favorite books, even though it’s not going to make it onto this list about algorithms.)
The P-NP problem is the most important open problem in computer science, if not all of mathematics. Simply stated, it asks whether every problem whose solution can be quickly checked by computer can also be quickly solved by computer. The Golden Ticket provides a nontechnical introduction to P-NP, its rich history, and its algorithmic implications for everything we do with computers and beyond. Lance Fortnow traces the history and development of P-NP, giving examples from a variety of disciplines, including economics, physics, and biology. He explores problems that capture the full difficulty of the P-NP dilemma, from discovering the shortest…
My life has been about programming for as long as I can remember. Learning to code was a way to connect with my dad and express my creativity at a young age. Since I grew up with code, it became the way I understood the world; often I could look at a process or program and immediately see its source code in my mind. I developed a very strong sense of aesthetics searching for “perfect code,” which for me was code that was not only error-free but resistant to errors. My studies, research, and career is about moving myself and all programmers closer to that goal: Software that never fails.
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.
The new edition of a guide to distributed algorithms that emphasizes examples and exercises rather than the intricacies of mathematical models.
This book offers students and researchers a guide to distributed algorithms that emphasizes examples and exercises rather than the intricacies of mathematical models. It avoids mathematical argumentation, often a stumbling block for students, teaching algorithmic thought rather than proofs and logic. This approach allows the student to learn a large number of algorithms within a relatively short span of time. Algorithms are explained through brief, informal descriptions, illuminating examples, and practical exercises. The examples and exercises allow readers to…
Tim Harford is the author of nine books, including The Undercover Economist and The Data Detective, and the host of the Cautionary Tales podcast. He presents the BBC Radio programs More or Less, Fifty Things That Made The Modern Economy, and How To Vaccinate The World. Tim is a senior columnist for the Financial Times, a member of Nuffield College, Oxford, and the only journalist to have been made an honorary fellow of the Royal Statistical Society.
This is a clever and highly readable guide to the brave new world of algorithms: what they are, how they work, and their strengths and weaknesses. It’s packed with stories and vivid examples, but Dr Fry is a serious mathematician and when it comes to the crunch she is well able to show it with clear and rigorous analysis.
When it comes to artificial intelligence, we either hear of a paradise on earth or of our imminent extinction. It's time we stand face-to-digital-face with the true powers and limitations of the algorithms that already automate important decisions in healthcare, transportation, crime, and commerce. Hello World is indispensable preparation for the moral quandaries of a world run by code, and with the unfailingly entertaining Hannah Fry as our guide, we'll be discussing these issues long after the last page is turned.
I build and use emerging technological innovations in business, and I also teach others how they might too! I’m a serial entrepreneur and a Professor at the Wharton School of the University of Pennsylvania. As an entrepreneur, I co-founded and developed the core IP for Yodle Inc, a venture-backed firm that was acquired by Web.com. I’m now the founder of Jumpcut Media – a startup using data and Web3 technologies to democratize opportunities in Film and TV. In all this work, I'm often trying to assess how emerging technologies may affect business and society in the long run and how I can apply them to create new products and services.
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.
Algorithms increasingly run our lives. They find books, movies, jobs, and dates for us, manage our investments, and discover new drugs. More and more, these algorithms work by learning from the trails of data we leave in our newly digital world. Like curious children, they observe us, imitate, and experiment. And in the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask.Machine learning is the automation of discovery,the scientific method on steroids,that enables intelligent robots and…
