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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
Author Profile Icon Koki Saitoh
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

Activation Function

The activation function represented by equation (3.3) changes output values at a threshold and is called a "step function" or a "staircase function." Therefore, we can say, "a perceptron uses a step function as the activation function." In other words, a perceptron chooses a "step function" as the activation function from many candidate functions. When a perceptron uses a step function as the activation function, what happens if a function other than a step function is used as the activation function? Well, by changing the activation function from a step function to another function, we can move to the world of a neural network. The next section will introduce an activation function for a neural network.

Sigmoid Function

One of the activation functions often used in neural networks is the sigmoid function, represented by equation (3.6):

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