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Deep Learning from the Basics

You're reading from   Deep Learning from the Basics Python and Deep Learning: Theory and Implementation

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800206137
Length 316 pages
Edition 1st Edition
Languages
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Authors (2):
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Shigeo Yushita Shigeo Yushita
Author Profile Icon Shigeo Yushita
Shigeo Yushita
Koki Saitoh Koki Saitoh
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Koki Saitoh
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Toc

Table of Contents (11) Chapters Close

Preface Introduction 1. Introduction to Python FREE CHAPTER 2. Perceptrons 3. Neural Networks 4. Neural Network Training 5. Backpropagation 6. Training Techniques 7. Convolutional Neural Networks 8. Deep Learning Appendix A

Numerical Differentiation

The gradient method uses information from the gradient to determine which direction to follow. This section describes what a gradient is and its characteristics, beginning with a "derivative."

Derivative

For example, let's assume that you ran 2 km in 10 minutes from the start of a full marathon. You can calculate the speed as 2 / 10 = 0.2 [km/minute]. You ran at a speed of 0.2 km per minute.

In this example, we calculated how much the "running distance" changed over "time." Strictly speaking, this calculation indicates the "average speed" for 10 minutes because you ran 2 km in 10 minutes. A derivative indicates the amount of change at "a certain moment." Therefore, by minimizing the time of 10 minutes (the distance in the last 1 minute, the distance in the last 1 second, the distance in the last 0.1 seconds, and so on), you can obtain the amount of change at a certain moment (instantaneous speed...

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