Classic Computer Science Problems in Python

David Kopec

ISBN 10: 1617295981 / ISBN 13: 9781617295980

Published by Manning Publications, US, 2019

About this Item

Classic Computer Science Problems in Python presents dozens of coding challenges, ranging from simple tasks like finding items in a list with a binary sort algorithm to clustering data using k-means.   Classic Computer Science Problems in Python deepens your Python language skills 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   Key Features ·   Breadth-first and depth-first search algorithms ·   Constraints satisfaction problems ·   Common techniques for graphs ·   Adversarial Search ·   Neural networks and genetic algorithms ·   Written for data engineers and scientists with experience using Python.   For readers comfortable with the basics of Python   About the technology Python is used everywhere for web applications, data munging, and powerful machine learning applications. Even problems that seem new or unique stand on the shoulders of classic algorithms, coding techniques, and engineering principles. Master these core skills, and you'll be ready to use Python for AI, data-centric programming, deep learning, and the other challenges you'll face as you grow your skill as a programmer.   David Kopec teaches at Champlain College in Burlington, VT and is the author of Manning's Classic Computer Science Problemsin Swift.

Seller Inventory # LU-9781617295980

Bibliographic Details

• Title: Classic Computer Science Problems in Python
• Publisher: Manning Publications, US
• Publication Date: 2019
• Language: English
• ISBN 10: 1617295981
• ISBN 13: 9781617295980
• Binding: Paperback
• Condition: New
• Weight: 440 grams

About this title

Synopsis

”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

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).