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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

Understanding the basic concepts and terminology

Graph theory was first introduced in the 18th century by Leonhard Euler to solve a famous problem known as the Königsberg bridge problem, which asks whether it is possible to walk around the Königsberg bridge while crossing over each of the seven bridges exactly once. The bridge looks as follows:

Before we move on, try it out for yourself by using your finger to trace along the path or draw it and trace it with a pencil. Did you manage to find a solution? It's alright if you didn't!

Let's stop for a moment and ask ourselves what exactly a graph is. A graph (G) is a mathematical structure made up of two sets—vertices (V(G)) and edges (E(G)). Two vertices (v1 and v2) are connected if there is an edge (e or (v1, v2)) between them. Now that that's settled, there are some rules associated with graphs...

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