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Hands-On Mathematics for Deep Learning

You're reading from   Hands-On Mathematics for Deep Learning Build a solid mathematical foundation for training efficient deep neural networks

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781838647292
Length 364 pages
Edition 1st Edition
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Author (1):
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Jay Dawani Jay Dawani
Author Profile Icon Jay Dawani
Jay Dawani
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Essential Mathematics for Deep Learning
2. Linear Algebra FREE CHAPTER 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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