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Practical Discrete Mathematics

Practical Discrete Mathematics

By : Ryan T. White, Ray
4.6 (17)
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Practical Discrete Mathematics

Practical Discrete Mathematics

4.6 (17)
By: Ryan T. White, Ray

Overview of this book

Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks. Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathematics, you’ll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you’ll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science. By the end of this book, you’ll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning.
Table of Contents (17 chapters)
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1
Part I – Basic Concepts of Discrete Math
7
Part II – Implementing Discrete Mathematics in Data and Computer Science
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12
Part III – Real-World Applications of Discrete Mathematics

Part II – Implementing Discrete Mathematics in Data and Computer Science

This part of the book covers applications of discrete mathematics to core concepts of computer science, including linear algebra; the complexity of algorithms in the worst case and on average; storing and extracting features from graphs, trees, and networks, graph searches; and finding shortest paths on networks.

This part comprises the following chapters:

  • Chapter 6, Computational Algorithms in Linear Algebra
  • Chapter 7, Computational Requirements for Algorithms
  • Chapter 8, Storage and Feature Extraction of Graphs, Trees, and Networks
  • Chapter 9, Searching Data Structures and Finding Shortest Paths

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