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Hands-On Mathematics for Deep Learning

You're reading from  Hands-On Mathematics for Deep Learning

Product type Book
Published in Jun 2020
Publisher Packt
ISBN-13 9781838647292
Pages 364 pages
Edition 1st Edition
Languages
Author (1):
Jay Dawani Jay Dawani
Profile icon Jay Dawani

Table of Contents (19) Chapters

Preface 1. Section 1: Essential Mathematics for Deep Learning
2. Linear Algebra 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

Understanding optimization and it's different types

In optimization, our goal is to either minimize or maximize a function. For example, a business wants to minimize its costs while maximizing its profits or a shopper might want to get as much as possible while spending as little as possible. Therefore, the goal of optimization is to find the best case of , which is denoted by x* (where x is a set of points), that satisfies certain criteria. These criteria are, for our purposes, mathematical functions known as objective functions.

For example, let's suppose we have the equation. If we plot it, we get the following graph:

You will recall from Chapter 1, Vector Calculus, that we can find the gradient of a function by taking its derivative, equating it to 0, and solving for x. We can find the point(s) at which the function has a minimum or maximum, as follows:

After...

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