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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

Single variable calculus

At its core, calculus is nothing more than the study of relationships and change. Having a keen grasp of calculus will help you better understand how deep learning algorithms work and how to make them work better for you as a practitioner.

Let's move on to understanding what makes calculus such a powerful tool. We start with single variable calculus, which is about functions that take in a single input and produce a single output.

Derivatives

To start with, let's imagine a straight line with the following equation:

In the equation, the following aspects apply:

  • y is a function of x, often written simply as f(x) (which is the notation we will be predominantly using in the remainder of the...
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