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Hands-On Mathematics for Deep Learning

You're reading from  Hands-On Mathematics for Deep Learning

Product type Book
Published in Jun 2020
Publisher Packt
ISBN-13 9781838647292
Pages 364 pages
Edition 1st Edition
Languages
Author (1):
Jay Dawani Jay Dawani
Profile icon Jay Dawani

Table of Contents (19) Chapters

Preface 1. Section 1: Essential Mathematics for Deep Learning
2. Linear Algebra 3. Vector Calculus 4. Probability and Statistics 5. Optimization 6. Graph Theory 7. Section 2: Essential Neural Networks
8. Linear Neural Networks 9. Feedforward Neural Networks 10. Regularization 11. Convolutional Neural Networks 12. Recurrent Neural Networks 13. Section 3: Advanced Deep Learning Concepts Simplified
14. Attention Mechanisms 15. Generative Models 16. Transfer and Meta Learning 17. Geometric Deep Learning 18. Other Books You May Enjoy

Adjacency matrix

As you can imagine, writing down all the pairs of connected nodes (that is, those that have edges between them) to keep track of the relationships in a graph can get tedious, especially as graphs can get very large. For this reason, we use what is known as the adjacency matrix, which is the fundamental mathematical representation of a graph.

Let's suppose we have a graph with n nodes, each of which has a unique integer label () so that we can refer to it easily and without any ambiguity whatsoever. For the sake of simplicity, in this example, n = 6. Then, this graph's corresponding adjacency matrix is as follows:

Let's take a look at the matrix for a moment and see why it is the way it is. The first thing that immediately pops out is that the matrix has a size of 6 × 6 (or n × n) because size is important to us. Next, we notice that...

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