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Practical Discrete Mathematics

You're reading from   Practical Discrete Mathematics Discover math principles that fuel algorithms for computer science and machine learning with Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781838983147
Length 330 pages
Edition 1st Edition
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Authors (2):
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Ryan T. White Ryan T. White
Author Profile Icon Ryan T. White
Ryan T. White
Archana Tikayat Ray Archana Tikayat Ray
Author Profile Icon Archana Tikayat Ray
Archana Tikayat Ray
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Table of Contents (17) Chapters Close

Preface 1. Part I – Basic Concepts of Discrete Math
2. Chapter 1: Key Concepts, Notation, Set Theory, Relations, and Functions FREE CHAPTER 3. Chapter 2: Formal Logic and Constructing Mathematical Proofs 4. Chapter 3: Computing with Base-n Numbers 5. Chapter 4: Combinatorics Using SciPy 6. Chapter 5: Elements of Discrete Probability 7. Part II – Implementing Discrete Mathematics in Data and Computer Science
8. Chapter 6: Computational Algorithms in Linear Algebra 9. Chapter 7: Computational Requirements for Algorithms 10. Chapter 8: Storage and Feature Extraction of Graphs, Trees, and Networks 11. Chapter 9: Searching Data Structures and Finding Shortest Paths 12. Part III – Real-World Applications of Discrete Mathematics
13. Chapter 10: Regression Analysis with NumPy and Scikit-Learn 14. Chapter 11: Web Searches with PageRank 15. Chapter 12: Principal Component Analysis with Scikit-Learn 16. Other Books You May Enjoy

Computational complexity of algorithms

In this section, we will learn about what algorithms are, the complexity of algorithms, and what they mean in terms of time and space and Big-O notation (compact notation for classifying the time and space needed for an algorithm). By the end of this section, you should have a good understanding of what algorithms are and their characteristics, such as complexity, and be able to determine the Big-O notation for the complexity of algorithms.

Algorithms are a step-by-step procedure/instruction to solve a problem or to obtain a desired output. They can be implemented in any programming language. Some of the important categories of algorithms from a data structure point of view are as follows:

  • Search: Used to search for an item in a data structure
  • Sort: Used to sort items in a required order
  • Insert: Used to insert items into a data structure
  • Update: Used to update an existing item in a data structure
  • Delete: Used...
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