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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 biological neural networks

The human brain is capable of some remarkable feats—it performs very complex information processing. The neurons that make up our brains are very densely connected and perform in parallel with others. These biological neurons receive and pass signals to other neurons through the connections (synapses) between them. These synapses have strengths associated with them and increasing or weakening the strength of the connections between neurons is what facilitates our learning and allows us to continuously learn and adapt to the dynamic environments we live in.

As we know, the brain consists of neurons—in fact, according to recent studies, it is estimated that the human brain contains roughly 86 billion neurons. That is a lot of neurons and a whole lot more connections. A very large number of these neurons are used simultaneously...

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