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Your secret weapon to understanding—and using!—one of the most powerful influences in the world todayFrom your Facebook News Feed to your most recent insurance premiums—even making toast!—algorithms play a role in virtually everything that happens in modern society and in your personal life. And while they can seem complicated from a distance, the reality is that, with a little help, anyone can understand—and even use—these powerful problem-solving tools!In Algorithms For Dummies, you'll discover the basics of algorithms, including what they are, how they work, where you can find them (spoiler alert: everywhere!), who invented the most important ones in use today (a Greek philosopher is involved), and how to create them yourself.You'll also find: Dozens of graphs and charts that help you understand the inner workings of algorithmsLinks to an online repository called GitHub for constant access to updated codeStep-by-step instructions on how to use Google Colaboratory, a zero-setup coding environment that runs right from your browserWhether you're a curious internet user wondering how Google seems to always know the right answer to your question or a beginning computer science student looking for a head start on your next class, Algorithms For Dummies is the can't-miss resource you've been waiting for.
John Mueller has published more than 100 books on technology, data, and programming. John has a website and blog where he writes articles on technology and offers assistance alongside his published books.Luca Massaron is a data scientist specializing in insurance and finance. A Google Developer Expert in machine learning, he has been involved in quantitative analysis and algorithms since 2000.
Introduction 1Part 1: Getting Started with Algorithms 7Chapter 1: Introducing Algorithms 9Chapter 2: Considering Algorithm Design 23Chapter 3: Working with Google Colab 41Chapter 4: Performing Essential Data Manipulations Using Python 59Chapter 5: Developing a Matrix Computation Class 79Part 2: Understanding the Need to Sort and Search 97Chapter 6: Structuring Data 99Chapter 7: Arranging and Searching Data 117Part 3: Exploring the World of Graphs 139Chapter 8: Understanding Graph Basics 141Chapter 9: Reconnecting the Dots 161Chapter 10: Discovering Graph Secrets 195Chapter 11: Getting the Right Web page 207Part 4: Wrangling Big Data 223Chapter 12: Managing Big Data 225Chapter 13: Parallelizing Operations 249Chapter 14: Compressing and Concealing Data 267Part 5: Challenging Difficult Problems 289Chapter 15: Working with Greedy Algorithms 291Chapter 16: Relying on Dynamic Programming 307Chapter 17: Using Randomized Algorithms 331Chapter 18: Performing Local Search 349Chapter 19: Employing Linear Programming 367Chapter 20: Considering Heuristics 381Part 6: The Part of Tens 401Chapter 21: Ten Algorithms That Are Changing the World 403Chapter 22: Ten Algorithmic Problems Yet to Solve 411Index 417 ntroduction 1Part 1: Getting Started with Algorithms 7Chapter 1: Introducing Algorithms 9Chapter 2: Considering Algorithm Design 23Chapter 3: Working with Google Colab 41Chapter 4: Performing Essential Data Manipulations Using Python 59Chapter 5: Developing a Matrix Computation Class 79Part 2: Understanding the Need to Sort and Search 97Chapter 6: Structuring Data 99Chapter 7: Arranging and Searching Data 117Part 3: Exploring the World of Graphs 139Chapter 8: Understanding Graph Basics 141Chapter 9: Reconnecting the Dots 161Chapter 10: Discovering Graph Secrets 195Chapter 11: Getting the Right Web page 207Part 4: Wrangling Big Data 223Chapter 12: Managing Big Data 225Chapter 13: Parallelizing Operations 249Chapter 14: Compressing and Concealing Data 267Part 5: Challenging Difficult Problems 289Chapter 15: Working with Greedy Algorithms 291Chapter 16: Relying on Dynamic Programming 307Chapter 17: Using Randomized Algorithms 331Chapter 18: Performing Local Search 349Chapter 19: Employing Linear Programming 367Chapter 20: Considering Heuristics 381Part 6: The Part of Tens 401Chapter 21: Ten Algorithms That Are Changing the World 403Chapter 22: Ten Algorithmic Problems Yet to Solve 411Index 417 ntroduction 1Part 1: Getting Started with Algorithms 7Chapter 1: Introducing Algorithms 9Chapter 2: Considering Algorithm Design 23Chapter 3: Working with Google Colab 41Chapter 4: Performing Essential Data Manipulations Using Python 59Chapter 5: Developing a Matrix Computation Class 79Part 2: Understanding the Need to Sort and Search 97Chapter 6: Structuring Data 99Chapter 7: Arranging and Searching Data 117Part 3: Exploring the World of Graphs 139Chapter 8: Understanding Graph Basics 141Chapter 9: Reconnecting the Dots 161Chapter 10: Discovering Graph Secrets 195Chapter 11: Getting the Right Web page 207Part 4: Wrangling Big Data 223Chapter 12: Managing Big Data 225Chapter 13: Parallelizing Operations 249Chapter 14: Compressing and Concealing Data 267Part 5: Challenging Difficult Problems 289Chapter 15: Working with Greedy Algorithms 291Chapter 16: Relying on Dynamic Programming 307Chapter 17: Using Randomized Algorithms 331Chapter 18: Performing Local Search 349Chapter 19: Employing Linear Programming 367Chapter 20: Considering Heuristics 381Part 6: The Part of Tens 401Chapter 21: Ten Algorithms That Are Changing the World 403Chapter 22: Ten Algorithmic Problems Yet to Solve 411Index 417
Chris Minnick, John Paul Mueller, Luca Massaron, Stephanie Diamond, Pam Baker, Daniel Stanton, Shiv Singh, Paul Mladjenovic, Sheryl Lindsell-Roberts, Jeffrey Allan