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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 Theory

Now that we have got a taste of linear algebra, calculus, statistics, and optimization, it is time to move on to a very fascinating topic, known as graph theory. This involves, as the name suggests, the study of graphs, which we use to model relationships between objects. We use these graphs to help visualize and analyze problems, which in turn helps us solve them.

Graph theory is a very important field and is used for a variety of problems, including page ranking in search engines, social network analysis, and in a GPS to find the best route home. It is also important for us to further our understanding of deep neural networks since the majority of them are based on a type of graph known as a directed acyclic graph (DAG).

Covering everything in graph theory goes beyond the scope of this chapter (and this book), but we will cover everything that is important for...

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