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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

Computational Graphs

A computational graph shows the process of calculation. This graph is used as a graph of data structure and is represented by multiple nodes and edges (meaning, straight lines that connect nodes). In this section, we will solve easy problems to familiarize ourselves with computational graphs before advancing step by step into more complex backpropagation.

Using Computational Graphs to Solve Problems

The problems in this section are simple enough that you can solve them with mental arithmetic, but the purpose here is to get familiar with computational graphs. Learning to use computational graphs will be helpful for the complicated calculations we will cover later, so it's important to first master how to use them here.

Question 1: Taro bought 2 apples that were 100 yen apiece. Calculate the amount of money he paid if a 10% consumption tax was applied.

A computational graph shows the process of calculation with nodes and arrows. A node is represented...

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