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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

Designing the Output Layer

You can use a neural network both for a classification problem and for a regression problem. However, you must change the activation function of the output layer, depending on which of the problems you use a neural network for. Usually, an identity function is used for a regression problem, and a softmax function is used for a classification problem.

Note

Machine learning problems can be broadly divided into "classification problems" and "regression problems." A classification problem is a problem of identifying which class the data belongs to—for example, classifying the person in an image as a man or a woman—while a regression problem is a problem of predicting a (continuous) number from certain input data—for example, predicting the weight of the person in an image.

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