Название: Classic Computer Science Problems in Python Автор: David Kopec Издательство: Manning Publications ISBN: 1617295981 Год: 2019 Страниц: 213 Язык: английский Формат: pdf (true), djvu Размер: 10.1 MB
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, 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!
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!
About the Technology: 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.
Why Python? Python is used in pursuits as diverse as data science, film-making, computer science education, IT management, and much more. There really is no computing field that Python has not touched (except maybe kernel development). Python is loved for its flexibility, beautiful and succinct syntax, object-oriented purity, and bustling community. The strong community is important because it means Python is welcoming to newcomers and has a large ecosystem of available libraries for developers to build upon. For the preceding reasons, Python is sometimes thought of as a beginner-friendly language, and that characterization is probably true. Most people would agree that Python is easier to learn than C++, for example, and its community is almost certainly friendlier to newcomers.
What's Inside: Search algorithms Common techniques for graphs Neural networks Genetic algorithms Adversarial search Uses type hints throughout Covers Python 3.7
About the Reader: For intermediate Python programmers.
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