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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

Flow-based networks

So far in this chapter, we have studied two kinds of generative models—GANs and VAEs—but there is also another kind, known as flow-based generative models, which directly learn the probability density function of the data distribution, which is something that the previous models do not do. Flow-based models make use of normalizing flows, which overcomes the difficulty that GANs and VAEs face in trying to learn the distribution. This approach can transform a simple distribution into a more complex one through a series of invertible mappings. We repeatedly apply the change of variables rule, which allows the initial probability density to flow through the series of invertible mappings, and at the end, we get the target probability distribution.

Normalizing...

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