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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

Summary

With this, we conclude our chapter on linear algebra. So far, we have learned all the fundamental concepts of linear algebra, such as matrix multiplication and factorization, that will lead you on your way to gaining a deep understanding of how deep neural networks (DNNs) work and are designed, and what it is that makes them so powerful.

In the next chapter, we will be learning about calculus and will combine it with the concepts learned earlier on in this chapter to understand vector calculus.

You have been reading a chapter from
Hands-On Mathematics for Deep Learning
Published in: Jun 2020
Publisher: Packt
ISBN-13: 9781838647292
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