Classic Computer Science Problems in Python

Summary

Classic Computer Science Problems in Python deepens your knowledge of problem-solving techniques from the realm of computer science by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems!



Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.



About the Technology

Computer science problems that seem new or unique are often rooted in classic algorithms, coding techniques, and engineering principles. And classic approaches are still the best way to solve them! Understanding these techniques in Python expands your potential for success in web development, data munging, machine learning, and more.



About the Book

Classic Computer Science Problems in Python sharpens your CS problem-solving skills with time-tested scenarios, exercises, and algorithms, using Python. You'll tackle dozens of coding challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. You'll especially enjoy the feeling of satisfaction as you crack problems that connect computer science to the real-world concerns of apps, data, performance, and even nailing your next job interview!



What's Inside

  • Search algorithms
  • Common techniques for graphs
  • Neural networks
  • Genetic algorithms
  • Adversarial search
  • Uses type hints throughout
  • Covers Python 3.7


About the Reader

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 Beginne (Apress, 2014) and Classic Computer Science Problems in Swift (Manning, 2018).



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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