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Applied

Deep Learning

A Case-Based Approach to Understanding

Deep Neural Networks

—

Umberto Michelucci

www.iebukes.com

Applied Deep Learning: A Case-Based Approach to Understanding

Deep Neural Networks

ISBN-13 (pbk): 978-1-4842-3789-2 ISBN-13 (electronic): 978-1-4842-3790-8

https://doi.org/10.1007/978-1-4842-3790-8

Library of Congress Control Number: 2018955206

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Printed on acid-free paper

UmbertoMichelucci

toelt.ai, Dübendorf, Switzerland

www.iebukes.com

Chapter 1: Computational Graphs andTensorFlow �� 1

How to Set Up Your Python Environment �� 1

Creating an Environment �� 3

Installing TensorFlow �� 9

Jupyter Notebooks �� 11

Basic Introduction to TensorFlow �� 14

Computational Graphs �� 14

Tensors �� 17

Creating and Running a Computational Graph �� 19

Computational Graph with tf�constant �� 19

Computational Graph with tf�Variable �� 20

Computational Graph with tf�placeholder �� 22

Differences Between run and eval �� 25

Dependencies Between Nodes �� 26

Tips on How to Create and Close a Session �� 27

Chapter 2: Single Neuron �� 31

The Structure of a Neuron �� 31

Matrix Notation �� 35

Python Implementation Tip: Loops and NumPy �� 36

Activation Functions �� 38

Table of Contents

About the Author �� xi

About the Technical Reviewer �� xiii

Acknowledgments ��xv

Introduction ��xvii

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