”Highly recommended to everyone interested in deepening their understanding of Python and practical computer science.” |
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
Note:
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