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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

Convolutions and pooling

In Chapter 7, Feedforward Neural Networks, we saw how deep neural networks are built and how weights connect neurons in one layer to neurons in the previous or following layer. The layers in CNNs, however, are connected through a linear operation known as convolution, which is where their name comes from and what makes it such a powerful architecture for images.

Here, we will go over the various kinds of convolution and pooling operations used in practice and what the effect of each is. But first, let's see what convolution actually is.

Two-dimensional convolutions

In mathematics, we write convolutions as follows:

What this means is that we have a function, f, which is our input and a function...

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