Video description"Whether you're a novice or a seasoned professional, there's an Aha! moment in this book for everyone."
James Watson, Adaptive
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 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!
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.
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!Inside:
- Search algorithms
- Common techniques for graphs
- Neural networks
- Genetic algorithms
- Adversarial search
- Uses type hints throughout
- Covers Python 3.7
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).
A fun way to get hands-on experience with classical computer science problems in modern Python.
Jens Christian Bredahl Madsen, IT Relation
Highly recommended to everyone who is interested in deepening their understanding, not only of the Python language, but also of practical computer science.
Daniel Kenney-Jung, MD, University of Minnesota
Classic problems presented in a wonderfully entertaining way with a language that always seems to have something new to offer.
Sam Zaydel, RackTop Systems
NARRATED BY LISA FARINA
Table of contents
- Chapter 1. Small problems
- Chapter 2. Search problems
- Chapter 3. Constraint-satisfaction problems
- Chapter 4. Graph problems
- Chapter 5. Genetic algorithms
- Chapter 6. K-means clustering
- Chapter 7. Fairly simple neural networks
- Chapter 8. Adversarial search
- Chapter 9. Miscellaneous problems
- Title: Classic Computer Science Problems in Python video edition
- Release date: March 2019
- Publisher(s): Manning Publications
- ISBN: None
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