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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

Simple Logic Circuits

AND Gate

The following are some easy problems that use a perceptron. We will look at logic circuits here. Let's think about an AND gate first. An AND gate consists of two inputs and one output. The table of input and output signals in Figure 2.2 is called a "truth table." As shown in Figure 2.2, the AND gate outputs 1 when two inputs are 1. Otherwise, it outputs 0:

Figure 2.2: Truth table of an AND gate

Now, we will use a perceptron to express this AND gate. We will determine the values of w1, w2, and θ so that they satisfy the truth table of Figure 2.2. What values can we set to create a perceptron that satisfies the conditions of Figure 2.2?

Actually, there is an infinite number of combinations of the parameters that satisfy Figure 2.2. For example, when (w1, w2, θ) = (0.5, 0.5, 0.7), the perceptron works as shown in Figure 2.2. (0.5, 0.5, 0.8) and (1.0, 1.0, 1.0) also satisfy the conditions of the AND...

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