Kurzbeschreibung:

Classic Computer Science Problems in Python sharpens your CS problem-solving skills with time-tested scenarios, exercises, and algorithms, using Python. Youll tackle dozens of coding challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. Youll 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!
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.
Whats inside

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

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) and Classic Computer Science Problems in Swift (Manning, 2018).
Table of contents

Small problems
Search problems
Constraint-satisfaction problems
Graph problems
Genetic algorithms
K-means clustering
Fairly simple neural networks
Adversarial search
Miscellaneous problems

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