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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

Implementing Perceptrons

Easy Implementation

Let's implement the preceding logic circuits with Python. Here, we will define the AND function, which takes x1 and x2 as arguments:

def AND(x1, x2):
    w1, w2, theta = 0.5, 0.5, 0.7
    tmp = x1*w1 + x2*w2
    if tmp <= theta:
        return 0
    elif tmp > theta:
        return 1

The w1, w2, and theta parameters are initialized within the function. When the sum of the weighted inputs exceeds the threshold, it returns 1; otherwise, it returns 0. Let's check that the outputs are the same as the ones shown in Figure 2.2:

AND(0, 0) # 0 (output)
AND(1, 0) # 0 (output)
AND(0, 1) # 0 (output)
AND(1, 1) # 1 (output)

The outputs are as we expected. With that, you have built an AND gate. Although you can use a similar procedure to build a NAND or OR gate,...

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