Classic Computer Science Problems in Python - GracieMcfarland/GracieMcfarlandpdf GitHub Wiki

 

Classic Computer Science Problems in Python



Classic Computer Science Problems in Python






8221Highly recommended to everyone interested in deepening their understanding of Python and practical computer science.8221 \|nbspKey FeaturesnbspgtnbspMaster formal techniques taught in college computer science classesnbspnbspgtnbspConnect computer science theory to real-world applications, data, and performancenbspnbspgtnbspPrepare for programmer interviewsnbspnbspgtnbspRecognize the core ideas behind most 8220new8221 challengesnbspnbspgtnbspCovers Python 3.7nbspNote:nbspPurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About The Book Programming problems that seem new or unique are usually rooted in well-known engineering principles. Classic Computer Science Problems in Python guides you through time-tested scenarios, exercises, and algorithms that will prepare you for the 8220new8221 problems you8217ll face when you start your next project. 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. What You Will Learn 8226 Search algorithms 8226 Common techniques for graphs 8226 Neural networks 8226 Genetic algorithms 8226 Adversarial search 8226 Uses type hints throughout This Book Is Written For For intermediate Python programmers. About The Author David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. He is the author of Dart for Absolute Beginners (Apress, 2014), Classic Computer Science Problems in Swift (Manning, 2018), and Classic Computer Science Problems in Java (Manning, 2020) Table of Contents 1. Small problems 2. Search problems 3. Constraint-satisfaction problems 4. Graph problems 5. Genetic algorithms 6. K-means clustering 7. Fairly simple neural networks 8. Adversarial search 9. Miscellaneous problems

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