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  • Classic Computer Science Problems in Python

  • 著者: David Kopec
  • ナレーター: Lisa Farina
  • 再生時間: 5 時間 6 分

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Classic Computer Science Problems in Python

著者: David Kopec
ナレーター: Lisa Farina
¥1,750で会員登録し購入

無料体験終了後は月額¥1,500。いつでも退会できます。

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あらすじ・解説

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!

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.

What's 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

  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

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©2019 Manning Publications (P)2019 Manning Publications

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