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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

Convolutional Neural Networks

In this chapter, we will cover one of the most popular and widely used deep neural networks—the convolutional neural network (CNN, also known as ConvNet).

It is this class of neural networks that is largely responsible for the incredible feats that have been accomplished in computer vision over the last few years, starting with AlexNet, created by Alex Krizhevsky, Geoffrey Hinton, and Ilya Sutskever, which outperformed all the other models in the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), thus beginning the deep learning revolution.

ConvNets are a very powerful type of neural network for processing data. They have a grid-like topology (that is, there is a spatial correlation between neighboring points) and are tremendously useful in a variety of applications, such as facial recognition, self-driving cars, surveillance...

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