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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

The inspiration behind ConvNets

CNNs are a type of artificial neural network (ANN); they are loosely inspired by the concept that the human visual cortex processes images and allows our brains to recognize objects in the world and interact with them, which allows us to do a number of things, such as drive, play sports, read, watch movies, and so on.

It has been found that computations that somewhat resemble convolutions take place in our brains. Additionally, our brains possess both simple and complex cells. The simple cells pick up basic features, such as edges and curves, while the complex cells show spatial invariance, while also responding to the same cues as the simple cells.

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