- 224 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Classic Computer Science Problems in Python
About this book
"Whether you're a novice or a seasoned professional, there's an Aha! moment in this book for everyone." - James Watson, Adaptive "Highly recommended to everyone interested in deepening their understanding of Python and practical computer science." — Daniel Kenney-Jung, MD, University of Minnesota Key Features • 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 Purchase 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 "new" problems you'll 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 • Search algorithms
• Common techniques for graphs
• Neural networks
• Genetic algorithms
• Adversarial search
• 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
Tools to learn more effectively
Saving Books
Keyword Search
Annotating Text
Listen to it instead
Information
Table of contents
- Copyright
- Brief Table of Contents
- Table of Contents
- Acknowledgments
- About this book
- About the author
- About the cover illustration
- Introduction
- Chapter 1. Small problems
- Chapter 2. Search problems
- Chapter 3. Constraint-satisfaction problems
- Chapter 4. Graph problems
- Chapter 5. Genetic algorithms
- Chapter 6. K-means clustering
- Chapter 7. Fairly simple neural networks
- Chapter 8. Adversarial search
- Chapter 9. Miscellaneous problems
- Appendix A. Glossary
- Appendix B. More resources
- Appendix C. A brief introduction to type hints
- Index
- List of Figures
- List of Tables
- List of Listings
Frequently asked questions
- Essential is ideal for learners and professionals who enjoy exploring a wide range of subjects. Access the Essential Library with 800,000+ trusted titles and best-sellers across business, personal growth, and the humanities. Includes unlimited reading time and Standard Read Aloud voice.
- Complete: Perfect for advanced learners and researchers needing full, unrestricted access. Unlock 1.4M+ books across hundreds of subjects, including academic and specialized titles. The Complete Plan also includes advanced features like Premium Read Aloud and Research Assistant.
Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app