Master the most widely used algorithms and be fully prepared when you're asked about them at your next job interview. With beautifully simple explanations, over 400 fun illustrations, and dozens of relevant examples, you'll actually enjoy learning about algorithms with this fun and friendly guide!

In Grokking Algorithms, Second Edition you will discover:

• Search, sort, and graph algorithms
• Data structures such as arrays, lists, hash tables, trees, and graphs
• NP-complete and greedy algorithms
• Performance trade-offs between algorithms
• Exercises and code samples in every chapter
• Over 400 illustrations with detailed walkthroughs

Foreword by Daniel Zingaro.

Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.

About the technology

The algorithms you use most often have already been discovered, tested, and proven. Grokking Algorithms, Second Edition makes it a breeze to learn, understand, and use them. With beautifully simple explanations, over 400 fun illustrations, and dozens of relevant examples, it's the perfect way to unlock the power of algorithms in your everyday work and prepare for your next coding interview--no math required!

About the book

Grokking Algorithms, Second Edition teaches you important algorithms to speed up your programs, simplify your code, and solve common programming problems. Start with tasks like sorting and searching, then build your skills to tackle advanced problems like data compression and artificial intelligence. You'll even learn to compare the performance tradeoffs between algorithms. Plus, this new edition includes fresh coverage of trees, NP-complete problems, and code updates to Python 3.

What's inside

• Search, sort, and graph algorithms
• Data structures such as arrays, lists, hash tables, trees, and graphs
• NP-complete and greedy algorithms
• Exercises and code samples in every chapter

About the reader

No advanced math or programming skills required.

About the author

Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io.

Table of Contents

1 Introduction to algorithms
2 Selection sort
3 Recursion
4 Quicksort
5 Hash tables
6 Beadth-first search
7 Trees
8 Balanced trees
9 Dijkstra's algorithm
10 Greedy algorithms
11 Dynamic programming
12 k-nearest neighbors
13 where to go next

Features include: The use of boxes to strengthen the narrative: pieces that provide historical context, descriptions of how the algorithms are used in practice, and excursions for the mathematically sophisticated.

Carefully chosen advanced topics that can be skipped in a standard one-semester course, but can be covered in an advanced algorithms course or in a more leisurely two-semester sequence.

An accessible treatment of linear programming introduces students to one of the greatest achievements in algorithms. An optional chapter on the quantum algorithm for factoring provides a unique peephole into this exciting topic. In addition to the text, DasGupta also offers a Solutions Manual, which is available on the Online Learning Center.

Algorithms is an outstanding undergraduate text, equally informed by the historical roots and contemporary applications of its subject. Like a captivating novel, it is a joy to read. Tim Roughgarden Stanford University

Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and machine learning. 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 for anyone with at least a little programming experience. The exposition emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details---like a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. Part 1 covers asymptotic analysis and big-O notation, divide-and-conquer algorithms and the master method, randomized algorithms, and several famous algorithms for sorting and selection.

Now Includes the Long-Anticipated Volume 4B!

Countless readers have spoken about the profound personal influence of Knuth's work. Scientists have marveled at the beauty and elegance of his analysis, while ordinary programmers have successfully applied his cookbook solutions to their day-to-day problems. All have admired Knuth for the breadth, clarity, accuracy, and good humor found in his books.

Primarily written as a reference, some people have nevertheless found it possible and interesting to read each volume from beginning to end. A programmer in China even compared the experience to reading a poem. Whatever your background, if you need to do any serious computer programming, you will find your own good reason to make each volume in this series a readily accessible part of your scholarly or professional library.

These five books comprise what easily could be the most important set of information on any serious programmer's bookshelf.

This set now includes Volume 4B, the sequel to Volume 4A, which extends Knuth's exploration of combinatorial algorithms. These algorithms are of keen interest to software designers because . . . a single good idea can save years or even centuries of computer time.

Donald Knuth may very well be a great master of the analysis of algorithms, but more than that, he is an incredible and tireless storyteller who always strikes the perfect balance between theory, practice, and fun. [The Art of Computer Programming, Volume 4B, Combinatorial Algorithms, Part 2, ] dives deep into the fascinating exploration of search spaces (which is quite like looking for a needle in a haystack or, even harder, to prove the absence of a needle in a haystack), where actions performed while moving forward must be meticulously undone when backtracking. It introduces us to the beauty of dancing links for removing and restoring the cells of a matrix in a dance which is both simple to implement and very efficient. And it studies the iconic and versatile satisfiability problem and carefully analyses various ingredients of SAT solvers. --Christine Solnon, Department of Computer Science, INSA Lyon

This box set includes the following volumes:
The Art of Computer Programming: Volume 1: Fundamental Algorithms, 3rd Edition
The Art of Computer Programming, Volume 2: Seminumerical Algorithms, 3rd Edition
The Art of Computer Programming: Volume 3: Sorting and Searching, 2nd Edition
The Art of Computer Programming, Volume 4A: Combinatorial Algorithms, Part 1
The Art of Computer Programming, Volume 4B: Combinatorial Algorithms, Part 2

Are you hitting a wall with data structures and algorithms? Whether you're a student prepping for coding interviews or an independent learner, this book is your essential guide to efficient problem-solving in programming.

UNLOCK THE POWER OF DATA STRUCTURES & ALGORITHMS:
Learn the intricacies of hash tables, recursion, dynamic programming, trees, graphs, and heaps. Become proficient in choosing and implementing the best solutions for any coding challenge.

REAL-WORLD, COMPETITION-PROVEN CODE EXAMPLES:
The programs and challenges in this book aren't just theoretical--they're drawn from real programming competitions. Train with problems that have tested and honed the skills of coders around the world.

GET INTERVIEW-READY:
Prepare yourself for coding interviews with practice exercises that help you think algorithmically, weigh different solutions, and implement the best choices efficiently.

WRITTEN IN C, USEFUL ACROSS LANGUAGES:
The code examples are written in C and designed for clarity and accessibility to those familiar with languages like C++, Java, or Python. If you need help with the C code, no problem: We've got recommended reading, too.

Algorithmic Thinking is the complete package, providing the solid foundation you need to elevate your coding skills to the next level.

Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout.

New for the fourth edition

• New chapters on matchings in bipartite graphs, online algorithms, and machine learning

• New material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays

• 140 new exercises and 22 new problems

• Reader feedback-informed improvements to old problems

• Clearer, more personal, and gender-neutral writing style

• Color added to improve visual presentation

• Notes, bibliography, and index updated to reflect developments in the field

• Website with new supplementary material

Think you know JavaScript? Think again. This isn't your typical coding book--it's a deep dive into the powerful world of data structures and algorithms that will transform the way you approach problem solving in JavaScript.

Whether you're a frontend developer tackling complex applications, a backend engineer building scalable systems, or a programmer preparing for technical interviews, this book will revolutionize the way you code.

Key features include:

• Modern JavaScript techniques: Use the latest language features and functional programming principles for cleaner, more efficient code.
• Performance-focused approach: Analyze and optimize algorithms using Big O notation.
• Essential algorithms explained: Implement and fine-tune core algorithms like quicksort, merge sort, digital search, and binary search.
• Algorithm design strategies: Solve challenging problems with techniques like recursion, dynamic programming, backtracking, and brute-force search.
• Advanced data structures: Explore complex structures such as binary search trees, heaps, and graphs.

Ready to break free from ordinary JavaScript? Whether your aim is to build cutting-edge web applications, optimize critical systems, or land your dream job, this book equips you with the advanced JavaScript knowledge that sets true experts apart.

My absolute favorite for this kind of interview preparation is Steven Skiena's The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace ... graph problems are -- they should be part of every working programmer's toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. ... every 1 - pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types. (Steve Yegge, Get that Job at Google)

Steven Skiena's Algorithm Design Manual retains its title as the best and most comprehensive practical algorithm guide to help identify and solve problems. ... Every programmer should read this book, and anyone working in the field should keep it close to hand. ... This is the best investment ... a programmer or aspiring programmer can make. (Harold Thimbleby, Times Higher Education)

It is wonderful to open to a random spot and discover aninteresting algorithm. This is the only textbook I felt compelled to bring with me out of my student days.... The color really adds a lot of energy to the new edition of the book! (Cory Bart, University of Delaware)

The is the most approachable book on algorithms I have. (Megan Squire, Elon University)

---

This newly expanded and updated third edition of the best-selling classic continues to take the mystery out of designing algorithms, and analyzing their efficiency. It serves as the primary textbook of choice for algorithm design courses and interview self-study, while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students.

The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Practical Algorithm Design, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, the Hitchhiker's Guide to Algorithms, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations, and an extensive bibliography.

NEW to the third edition:

-- New and expanded coverage of randomized algorithms, hashing, divide and conquer, approximation algorithms, and quantum computing

-- Provides full online support for lecturers, including an improved website component with lecture slides and videos

-- Full color illustrations and code instantly clarify difficult concepts

-- Includes several new war stories relating experiences from real-world applications

-- Over 100 new problems, including programming-challenge problems from LeetCode and Hackerrank.

-- Provides up-to-date links leading to the best implementations available in C, C++, and Java

Additional Learning Tools:

-- Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them

-- Exercises include job interview problems from major software companies

-- Highlighted take home lessons emphasize essential concepts

-- The no theorem-proof style provides a uniquely accessible and intuitive approach to a challenging subject

-- Many algorithms are presented with actual code (written in C)

Written by a well-known algorithms researcher who received the IEEE Computer Science and Engineering Teaching Award, this substantially enhanced third edition of The Algorithm Design Manual is an essential learning tool for students and professionals needed a solid grounding in algorithms. Professor Skiena is also the author of the popular Springer texts, The Data Science Design Manual and Programming Challenges: The Programming Contest Training Manual.

The Art of Computer Programming is a multivolume work on the analysis of algorithms and has long been recognized as the definitive description of classical computer science. The five volumes published to date--Volumes 1, 2, 3, 4A, and 4B--already comprise a unique and invaluable resource in programming theory and practice. Countless readers have spoken about the profound personal influence of Knuth's writings. Scientists have marveled at the beauty and elegance of his analysis, while practicing programmers have successfully applied his cookbook solutions to their day-to-day problems. All have admired Knuth for the breadth, clarity, accuracy, and good humor found in his books.

To continue the set, and to update parts of the existing volumes, Knuth has created a series of small books called fascicles, which are published at regular intervals. Each fascicle encompasses a section or more of wholly new or revised material. Ultimately, the content of these fascicles will be rolled up into the comprehensive, final versions of each volume, and the enormous undertaking that began in 1962 will be complete.

Volume 4, Fascicle 7, which is brimming with lively examples, forms the first third of what will eventually become hardcover Volume 4C. It introduces and explores an important general framework for modeling and solving combinatorial problems, called the Constraint Satisfaction Problem (CSP). The concluding sections of Volume 4B contain expositions of two analogous frameworks, namely XCC (exact covering with colors) and SAT (Boolean satisfiability); the XCC solvers and SAT solvers are now joined by CSP solvers, completing a powerful trio of techniques. Each member of the trio has its own strengths, while separately helping to understand the other two.

This fascicle illuminates how the CSP framework is tied to dozens of other parts of computer science: Scene analysis (computer vision); efficient algorithms that embed one graph in another; fascinating instances of graceful graphs; new ways to look ahead when backtracking; new heuristics to guide a search that backtracks through a massive space of possibilities; situations when backtracking isn't necessary.

New sparse-set data structures are introduced, leading to a technique called dancing cells--which often is even better than dancing links! Recreational topics appear throughout, including some new takes on the classic problem of a knight's tour, as well as modern puzzles such as fillomino.

Nearly 500 exercises are provided, arranged carefully for self-instruction, together with detailed answers (in fact, sometimes also with answers to the answers). All the while, the author pays significant attention to the history of the subject and its human dimensions.

Illuminates how algorithms, intertwined with human biases, damage political discourse and civic engagement

Algorithmic Worldmaking is an urgent exploration of the dynamic relationship between algorithms that encode their human creators' assumptions and the humans whose choices are shaped by these algorithms in search engines, social media, and other digital spaces. Transcending discussions of one or the other, Jeremy David Johnson traces the corrupting political and social influences that arise from their mutual interaction.

Johnson uses the concept of kosmos in its sense of a dynamic order to frame the interplay between algorithms, humans, and their environments. He first shows how algorithms, far from being objective or unbiased, perpetuate human errors. Johnson then suggests a framework of four parts--navigation, exploration, maintenance, and monetization--to map the variety of political consequences to a society influenced by these four factors.

Citing controversies at major platforms such as Google, YouTube, and Facebook, Johnson demonstrates how algorithms limit and shape human thought. He makes several persuasive arguments. First, algorithms and humans share agency but humans have exceptional responsibility. Second, the algorithmic kosmos mirrors and shapes social oppression. Third, algorithms incentivize capitalist exploitation. Last, these influences damage democratic deliberation.

This landmark study is essential for scholars and students of political science, media studies, and those interested in the perilous implications of algorithmic systems on civic and political life.

A hands-on roadmap to using Python for artificial intelligence programming

In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples.

Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes:

• Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learning
• Expansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learning
• Practical AI and Python cheat sheet quick references

This hands-on AI programming guide is perfect for anyone with a basic knowledge of programming--including familiarity with variables, arrays, loops, if-else statements, and file input and output--who seeks to understand foundational concepts in AI and AI development.

Graph Algorithms the Fun Way offers a refreshing approach to complex concepts by blending humor, imaginative examples, and practical Python implementations to reveal the power and versatility of graph based problem-solving in the real world. Through clear diagrams, engaging examples, and Python code, you'll build a solid foundation for addressing graph problems in your own projects.

Explore a rich landscape of cleverly constructed scenarios where:

• Hedge mazes illuminate depth-first search
• Urban explorations demonstrate breadth-first search
• Intricate labyrinths reveal bridges and articulation points
• Strategic planning illustrates bipartite matching

• Implement powerful algorithms, including Dijkstra's, A*, and Floyd-Warshall
• Tackle puzzles and optimize pathfinding with newfound confidence
• Uncover real-world applications in social networks and transportation systems
• Develop robust intuition for when and why to apply specific graph techniques

Prepare to view the world through the lens of graphs--where connections reveal insights and algorithms unlock new possibilities.

Optimization problems are everywhere in daily life. What's the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems.

In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn:

- The core concepts of search and optimization
- Deterministic and stochastic optimization techniques
- Graph search algorithms
- Trajectory-based optimization algorithms
- Evolutionary computing algorithms
- Swarm intelligence algorithms
- Machine learning methods for search and optimization problems
- Efficient trade-offs between search space exploration and exploitation
- State-of-the-art Python libraries for search and optimization

Inside this comprehensive guide, you'll find a wide range of optimization methods, from deterministic search algorithms to stochastic derivative-free metaheuristic algorithms and machine learning methods. Don't worry--there's no complex mathematical notation. You'll learn through in-depth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world. Plus, get hands-on experience with practical exercises to optimize and scale the performance of each algorithm.

Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.

About the technology

Every time you call for a rideshare, order food delivery, book a flight, or schedule a hospital appointment, an algorithm works behind the scenes to find the optimal result. Blending modern AI methods with classical search and optimization techniques can deliver incredible results, especially for the messy problems you encounter in the real world. This book shows you how.

About the book

Optimization Algorithms explains in clear language how optimization algorithms work and what you can do with them. This engaging book goes beyond toy examples, presenting detailed scenarios that use actual industry data and cutting-edge AI techniques. You will learn how to apply modern optimization algorithms to real-world problems like pricing products, matching supply with demand, balancing assembly lines, tuning parameters, coordinating mobile networks, and cracking smart mobility challenges.

What's inside

- Graph search algorithms
- Metaheuristic algorithms
- Machine learning methods
- State-of-the-art Python libraries for optimization
- Efficient trade-offs between search space exploration and exploitation

About the reader

Requires intermediate Python and machine learning skills.

About the author

Dr. Alaa Khamis is an AI and smart mobility technical leader at General Motors and a lecturer at the University of Toronto.

The technical editor on this book was Frances Buontempo.

Table of Contents

PART 1
1 Introduction to search and optimization
2 A deeper look at search and optimization
3 Blind search algorithms
4 Informed search algorithms
PART 2
5 Simulated annealing
6 Tabu search
PART 3
7 Genetic algorithms
8 Genetic algorithm variants
PART 4
9 Particle swarm optimization
10 Other swarm intelligence algorithms to explore
PART 5
11 Supervised and unsupervised learning
12 Reinforcement learning
included with the eBook only:
Appendix A
Appendix B
Appendix C

The leading introduction to computer algorithms in use today, including fifty algorithms every programmer should know

Princeton Computer Science professors, Robert Sedgewick and Kevin Wayne, survey the most important computer algorithms in use and of interest to anyone working in science, mathematics, and engineering, and those who use computation in the liberal arts. They provide a full treatment of data structures and algorithms for key areas that enable you to confidently implement, debug, and put them to work in any computational environment.

Fundamentals:

• Basic programming models
• Data abstraction
• Bags, queues, and stacks
• Analysis of algorithms

Sorting

• Elementary sorts
• Mergesort
• Quicksort
• Priority queues
• Applications

Graphs

• Undirected graphs
• Directed graphs
• Minimum spanning trees
• Shortest paths

Strings

• String sorts
• Tries
• Substring search
• Regular expressions
• Data compression

These algorithms are generally ingenious creations that, remarkably, can each be expressed in just a dozen or two lines of code. As a group, they represent problem-solving power of amazing scope. They have enabled the construction of computational artifacts, the solution of scientific problems, and the development of commercial applications that would not have been feasible without them.

A hands-on, easy-to-comprehend guide that is perfect for anyone who needs to understand algorithms.

With the explosive growth in the amount of data and the diversity of computing applications, efficient algorithms are needed now more than ever. Programming languages come and go, but the core of programming--algorithms and data structures--remains the same.

Absolute Beginner's Guide to Algorithms is the fastest way to learn algorithms and data structures. Using helpful diagrams and fully annotated code samples in Javascript, you will start with the basics and gradually go deeper and broader into all the techniques you need to organize your data.

• Start fast with data structures basics: arrays, stacks, queues, trees, heaps, and more
• Walk through popular search, sort, and graph algorithms
• Understand Big-O notation and why some algorithms are fast and why others are slow
• Balance theory with practice by playing with the fully functional JavaScript implementations of all covered data structures and algorithms

Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today's web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code, with examples in JavaScript, Python, and Ruby. This new and revised second edition features new chapters on recursion, dynamic programming, and using Big O in your daily work.

Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You'll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions.

Use these techniques today to make your code faster and more scalable.

Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaul

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

- Familiarize yourself with advanced deep learning architectures

- Explore newer topics, such as handling hidden bias in data and algorithm explainability

- Get to grips with different programming algorithms and choose the right data structures for their optimal implementation

Book Description

The ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works.

You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them.

Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use.

You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT.

Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.

By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.

What you will learn

- Design algorithms for solving complex problems

- Become familiar with neural networks and deep learning techniques

- Explore existing data structures and algorithms found in Python libraries

- Implement graph algorithms for fraud detection using network analysis

- Delve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examples

- Create a recommendation engine that suggests relevant movies to subscribers

- Grasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMs

Who this book is for

This computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.

Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful.

Python programming experience is a must, knowledge of data science will be helpful but not necessary.

Table of Contents

- Overview of Algorithms

- Data Structures Used in Algorithms

- Sorting and Searching Algorithms

- Designing Algorithms

- Graph Algorithms

- Unsupervised Machine Learning Algorithms

- Traditional Supervised Learning Algorithms

- Neural Network Algorithms

- Algorithms for Natural Language Processing

- Understanding Sequential Models

- Advanced Sequential Modeling Algorithms

- Recommendation Engines

- Algorithmic Strategies for Data Handling

- Cryptography

- Large-Scale Algorithms

- Practical Considerations

Specifically, the font size is larger, and the page size is 7x10 (the regular format uses 6x9).

The content is identical.

This is the Java version of our book. See our website for links to the C++ version.Have you ever...

• Wanted to work at an exciting futuristic company?
• Struggled with an interview problem thatcould have been solved in 15 minutes?
• Wished you could study real-world computing problems?

If so, you need to read Elements of Programming Interviews (EPI).

EPI is your comprehensive guide to interviewing for software development roles.

The core of EPI is a collection of over 250 problems with detailed solutions. The problems are representative of interview questions asked at leading software companies. The problems are illustrated with 200 figures, 300 tested programs, and 150 additional variants.

The book begins with a summary of the nontechnical aspects of interviewing, such as strategies for a great interview, common mistakes, perspectives from the other side of the table, tips on negotiating the best offer, and a guide to the best ways to use EPI. We also provide a summary of data structures, algorithms, and problem solving patterns.

Coding problems are presented through a series of chapters on basic and advanced data structures, searching, sorting, algorithm design principles, and concurrency. Each chapter stars with a brief introduction, a case study, top tips, and a review of the most important library methods. This is followed by a broad and thought-provoking set of problems.

A practical, fun approach to computer science fundamentals, as seen through the lens of common programming interview questions.
Jeff Atwood/Co-founder, Stack Overflow and Discourse