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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

Chapter 11: Web Searches with PageRank

Searching the web is one of the first things we learn to do on the internet. The purpose, simply, is to find information of a topic of interest, but how does Google, or other search engines, take the words we search and effectively return what we want? This is the question we aim to answer in this chapter.

More specifically, this chapter discusses web searches from both a mathematical and practical perspective. We will first build the mathematical setting for common methods for web searches. We'll then look more deeply at Google's PageRank method and the linear algebra required. We'll then construct an implementation of PageRank that combines this linear algebra with the probabilistic aspects of PageRank we discussed in Chapter 5, Elements of Discrete Probability.

In this chapter, we will cover the following topics:

  • The development of search engines over time
  • How Google's PageRank algorithm works
  • Implementing...
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