Taming Mr. Walker

A friendly, fully-illustrated introduction to the most important computer programming algorithms. 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 The first edition of Grokking Algorithms proved to over 100,000 readers that learning algorithms doesn't have to be complicated or boring! This revised second edition contains brand new coverage of trees, including binary search trees, balanced trees, B-trees and more. You’ll also discover fresh insights on data structure performance that takes account of modern CPUs. Plus, the book’s fully annotated code samples have been updated to Python 3. Foreword by Daniel Zingaro. 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. A friendly, fully-illustrated introduction to the most important computer programming algorithms. Master the most widely used algorithms and be fully prepared when youre asked about them at your next job interview. With beautifully simple explanations, over 400 fun illustrations, and dozens of relevant examples, youll actually enjoy learning about algorithms with this fun and friendly guide! In Grokking Algorithms, Second Edition you will 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 The first edition of Grokking Algorithms proved to over 100,000 readers that learning algorithms doesn't have to be complicated or boring! This revised second edition contains brand new coverage of trees, including binary search trees, balanced trees, B-trees and more. Youll also discover fresh insights on data structure performance that takes account of modern CPUs. Plus, the books fully annotated code samples have been updated to Python 3. Foreword by Daniel Zingaro. 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, its the perfect way to unlock the power of algorithms in your everyday work and prepare for your next coding interviewno 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. Youll 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 Dijkstras algorithm 10 Greedy algorithms 11 Dynamic programming 12 k-nearest neighbors 13 where to go next A friendly, fully-illustrated introduction to the most important computer programming algorithms. The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. This book will prepare you for those pesky algorithms questions in every programming job interview and help you apply them in your day-to-day work. And if you want to understand them without slogging through dense multipage proofs, this is the book for you. 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 The first edition of Grokking Algorithms proved to over 100,000 readers that learning algorithms doesn't have to be complicated or boring! This new edition now includes fresh coverage of trees, NP complete problems, and code updates to Python 3. With easy-to-read, friendly explanations, clever examples, and exercises to sharpen your skills as you learn, you’ll actually enjoy learning these important algorithms. About the book Grokking Algorithms, Second Edition makes it easy to learn. You’ll never be bored—complex concepts are all explained through fun cartoons and memorable examples that make them stick. You'll start with tasks like sorting and searching, then build your skills to tackle more advanced problems like data compression and artificial intelligence. This revised second edition contains brand new coverage of trees, including binary search trees, balanced trees, B-trees and more. You’ll also discover fresh insights on data structure performance that takes account of modern CPUs. Plus, the book’s fully annotated code samples have been updated to Python 3. By the time you reach the last page, you’ll have mastered the most widely applicable algorithms, know when and how to use them, and be fully prepared when you’re asked about them on your next job interview. About the reader Suitable for self-taught programmers, engineers, job seekers, or anyone who wants to brush up on algorithms. 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. grokking algorithms brief contents contents foreword preface acknowledgments about this book about the author 1 introduction to algorithms Binary search Big O notation 2 selection sort How memory works Arrays and linked lists Which is used more, arrays or linked lists? Selection sort Example code listing 3 recursion Recursion Base case and recursive case The stack 4 quicksort Divide and conquer Quicksort Big O notation revisited 5 hash tables Hash functions Use cases Collisions Performance 6 breadth-first search Introduction to graphs What is a graph? Breadth-first search Implementing the graph Implementing the algorithm 7 trees Your first tree A space odyssey: depth-first search Binary trees Huffman coding 8 balanced trees A balancing act Shorter trees are faster AVL trees: A type of balanced tree Splay trees B-trees 9 Dijkstra’s algorithm Working with Dijkstra’s algorithm Terminology Trading for a piano Negative-weight edges Implementation 10 greedy algorithms The classroom scheduling problem The knapsack problem The set-covering problem 11 dynamic programming The knapsack problem (revisited) Knapsack problem FAQ Longest common substring 12 k-nearest neighbors Classifying oranges vs. grapefruit Building a recommendations system Regression Introduction to machine learning A high-level overview of training an ML model 13 where to go next Linear regression Inverted indexes The Fourier transform Parallel algorithms map/reduce Bloom filters and HyperLogLog HTTPS and the Diffie-Hellman key exchange Locality-sensitive hashing Min heaps and priority queues Linear programming Epilogue A—performance of AVL trees B—NP-hard problems Decision problems The satisfiability problem Hard to solve, quick to verify Reductions NP-hard NP-complete C—answers to exercises index A B C D E F G H I K L M N O P Q R S T U V W