Classic Computer Science Problems in Python pdf
Classic Computer Science Problems in Python
Author: David Kopec
Description:
This engaging and practical book explores timeless computer science problems and how to solve them using Python. It serves as an excellent guide for developers and students to sharpen their problem-solving skills through classic challenges.
David Kopec introduces foundational algorithms and techniques, covering topics such as search problems, constraint-satisfaction problems, graph algorithms, genetic algorithms, and neural networks. The book takes a hands-on approach, offering clear explanations and Python code examples that allow readers to implement and experiment with solutions on their own.
What You'll Learn:
- Explore dynamic programming and backtracking techniques for efficient problem-solving.
- Understand classic search algorithms like breadth-first search and depth-first search.
- Dive into optimization problems, including genetic algorithms and simulated annealing.
- Get started with neural networks and how to implement them in Python.
Who Is It For?
This book is ideal for programmers with some Python experience looking to deepen their understanding of computer science principles, as well as students and educators seeking practical examples of algorithms in action.
Whether you're preparing for coding interviews or enhancing your algorithmic thinking, Classic Computer Science Problems in Python is a resourceful companion for mastering the art of computational problem-solving.
