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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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Toc

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

Summary

In this chapter, we used our understanding of graph structures, including trees and networks, from Chapter 8, Storage and Feature Extraction of Graphs, Trees, and Networks, and learned about some practical graph-oriented problems and popular algorithms for solving them.

We began by learning about graph searches where we traverse a graph to discover its structure and perhaps do some calculations at each vertex. Then, we moved on to perhaps the most common graph search algorithm, DFS. We did an example on a small graph by hand before writing a Python implementation of the algorithm, which we confirmed led to the same results as the example we did by hand.

Then, we moved on to a very practical problem: finding the shortest paths between vertices in networks. This problem has applications in finding optimal travel routes, sending messages over a computer network through good paths, efficiently delivering electricity over electrical grids, and many other areas. With some networks...

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