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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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Toc

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

Overview of attention

When we go about our lives (in the real world), our brains don't observe every detail in our environment at all times; instead, we focus on (or pay greater attention to) information that is relevant to the task at hand. For example, when we are driving, we are able to adjust our focal length to focus on different details, some of which are closer and others are further away, and then act on what we observe. Similarly, when we are conversing with others, we usually don't listen carefully to each and every word; we listen to only part of what is spoken and use it to infer the relationships with some of the words to figure out what the other person is saying. Often, when we are reading/listening to someone, we can use some words to infer what the person is going to say next based on what we have already read/heard.

But why do we need these attention...

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