Classic Computer Science Problems in Python

<b>”Highly recommended to everyone interested in deepening their understanding of Python and practical computer science.” | <b>Key Features</b> Master formal techniques taught in college computer science classes Connect computer science theory to real-world applications, data, and performance Prepare for programmer interviews Recognize the core ideas behind most “new” challenges Covers Python 3.7 <b>Note:</b> Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.</b><br><br><b>About The Book</b><br> <br>Programming problems that seem new or unique are usually rooted in well-known engineering principles. <i>Classic Computer Science Problems in Python</i> guides you through time-tested scenarios, exercises, and algorithms that will prepare you for the “new” problems you’ll face when you start your next project.<br> <br>In this amazing book, you'll tackle dozens of coding challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. As you work through examples for web development, machine learning, and more, you'll remember important things you've forgotten and discover classic solutions that will save you hours of time.<br> <br><b>What You Will Learn</b><br> <br>• Search algorithms<br> • Common techniques for graphs<br> • Neural networks<br> • Genetic algorithms<br> • Adversarial search<br> • Uses type hints throughout<br> <br><b>This Book Is Written For</b><br> <br>For intermediate Python programmers.<br> <br><b>About The Author</b><br> <br><b>David Kopec</b> is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. He is the author of <i>Dart for Absolute Beginners</i> (Apress, 2014), <i>Classic Computer Science Problems in Swift</i> (Manning, 2018), and <i>Classic Computer Science Problems in Java</i> (Manning, 2020)<br> <br><b>Table of Contents</b><br> <br>1. Small problems<br> 2. Search problems<br> 3. Constraint-satisfaction problems<br> 4. Graph problems<br> 5. Genetic algorithms<br> 6. K-means clustering<br> 7. Fairly simple neural networks<br> 8. Adversarial search<br> 9. Miscellaneous problems