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Shih-Chia Huang - Principles and Labs for Deep Learning

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Book:
Principles and Labs for Deep Learning
Author:
ShihChia Huang / TrungHieu Le
Publisher:
Academic Press
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Year:
2021
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Principles and Labs for Deep Learning provides the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of results. The structured methods and labs provided by Dr. Huang and Dr. Le enable readers to become proficient in TensorFlow to build deep Convolutional Neural Networks (CNNs) through custom APIs, high-level Keras APIs, Keras Applications, and TensorFlow Hub. Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific learning outcome.

Deep Learning has been successfully applied in diverse fields such as computer vision, audio processing, robotics, natural language processing, bioinformatics and chemistry. Because of the huge scope of knowledge in Deep Learning, a lot of time is required to understand and deploy useful, working applications, hence the importance of this new resource. Both theory lessons and experiments are included in each chapter to introduce the techniques and provide source code examples to practice using them. All Labs for this book are placed on GitHub to facilitate the download. The book is written based on the assumption that the reader knows basic Python for programming and basic Machine Learning.

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Principles and Labs for Deep Learning

First Edition

Shih-Chia Huang

Professor, Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan

Trung-Hieu Le

Assistant Professor, Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan

Lecturer, Faculty of Information Technology, Hung Yen University of Technology and Education, Hung Yen, Vietnam

Table of Contents

Cover image

Title page

Preface

Environment installation

Abstract

1: Python installation

2: TensorFlow installation

3: Python extension installation

4: Jupyter notebook

5: PyCharm IDE

6: GitHub labs

Chapter 1: Introduction to TensorFlow

Abstract

1.1: Deep learning

1.2: Introduction to TensorFlow

1.3: Improvement of TensorFlow

1.4: Eager execution

1.5: Keras

1.6: Tf.data

Chapter 2: Neural networks

Abstract

2.1: Introduction to neural networks

2.2: Introduction to Kaggle

2.3: Experiment 1: House price prediction

2.4: Introduction to TensorBoard

2.5: Experiment 2: Overfitting problem

Chapter 3: Binary classification problem

Abstract

3.1: Machine learning algorithms

3.2: Binary classification problem

3.3: Experiment: Pokmon combat prediction

Chapter 4: Multi-category classification problem

Abstract

4.1: Convolutional neural network

4.2: Multi-category classification

4.3: Experiment: CIFAR-10 image classification

Chapter 5: Training neural network

Abstract

5.1: Backpropagation

5.2: Weight initialization

5.3: Batch normalization

5.4: Experiment 1: Verification of three weight initialization methods

5.5: Experiment 2: Verification of batch normalization

5.6: Comparison of different neural networks

Chapter 6: Advanced TensorFlow

Abstract

6.1: Advanced TensorFlow

6.2: Using high-level keras API and custom API of TensorFlow

6.3: Experiment: implementation of two network models using high-level keras API and custom API

Chapter 7: Advanced TensorBoard

Abstract

7.1: Advanced TensorBoard

7.2: Experiment 1: Using tf.summary.image API to visualize training results

7.3: Experiment 2: Hyperparameter tuning with TensorBoard HParams

Chapter 8: Convolutional neural network architectures

Abstract

8.1: Popular convolutional neural network architectures

8.2: Experiment: Implementation of inception-v3 network

Chapter 9: Transfer learning

Abstract

9.1: Transfer learning

9.2: Experiment: Using Inception-v3 for transfer learning

Chapter 10: Variational auto-encoder

Abstract

10.1: Introduction to auto-encoder

10.2: Introduction to variational auto-encoder

10.3: Experiment: Implementation of variational auto-encoder model

Chapter 11: Generative adversarial network

Abstract

11.1: Generative adversarial network

11.2: Introduction to WGAN-GP

11.3: Experiment: Implementation of WGAN-GP

Chapter 12: Object detection

Abstract

12.1: Computer vision

12.2: Introduction to object detection

12.3: Object detection methods

12.4: Experiment: Implementation of YOLO-v3

Index

Academic Press is an imprint of Elsevier

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No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publishers permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website:

This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.

Library of Congress Cataloging-in-Publication Data

A catalog record for this book is available from the Library of Congress

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library

ISBN 978-0-323-90198-7

For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Mara Conner

Acquisitions Editor: Chris Katsaropoulos

Editorial Project Manager: Isabella C. Silva

Production Project Manager: Swapna Srinivasan

Cover Designer: Greg Harris

Typeset by SPi Global, India

Preface

Shih-Chia Huang; Trung-Hieu Le, Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan

In 1943, Warren McCulloch and Walter Pitts introduced a computational model based on a threshold logic algorithm, paving the way for the development of artificial intelligence (AI). The field of AI research was founded at the Dartmouth summer research project on artificial intelligence in 1956. In the nearly 80 years of development history, AI has experienced many ups and downs, especially the two AI winters, which are known as the periods of reduced funding and interest in AI research. After AlphaGo, a deep learning-based computer Go program developed by Google DeepMind, defeated a professional Go player in 2015, AI has once again attracted considerable research attention. In recent years, AI with deep learning algorithms has been dramatically improved and successfully applied to many problems in academia and industry. Recognizing the potential and power of deep learning, many world-class technology companies, such as Google, Facebook, Microsoft, Tesla, and others, have invested a lot of manpower and resources in researching and developing their products using deep learning.

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