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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

Multivariable calculus

Now that we have gone through single variable calculus and understand what calculus is about, it is time for us to go a step deeper and look at multivariable calculus. Multivariable calculus has a lot of similarities with single variable calculus, except—as the name suggests—here, we will be dealing with functions that accept two or more variables as input.

Multivariable calculus is used everywhere in the real world and has applications in every field and industry, from healthcare to economics, to finance, to robotics, to aerospace, and so on. An example of this could be trying to model how air curves around an airplane, to understand how aerodynamic it is and where the design of the airplane body can be improved. This is something we would not be able to do with single variable calculus.

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