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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

Graph Laplacian

Earlier in this chapter, in the Adjacency matrix section, we learned about the adjacency matrix and how we can use it to tell what the structure of a graph is. However, there are other ways of representing graphs in matrix form.

Now, let's suppose we have an undirected, unweighted graph. Then, its Laplacian matrix will be a symmetric n × n matrix, L, whose elements are as follows:

Here, . We can also write this as follows:

Here, Ai,j is the adjacency matrix and δi,j is the Kronecker delta. We can rewrite this in matrix form, as follows:

Here, we have the following:

Similarly, we can also write the graph Laplacian matrix for a weighted graph by replacing the adjacency matrix here with the one we defined previously for weighted graphs.

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