Book Image

Hands-On Convolutional Neural Networks with TensorFlow

By : Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo
Book Image

Hands-On Convolutional Neural Networks with TensorFlow

By: Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo

Overview of this book

Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images.

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Table of Contents (17 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Packt Upsell
Packt Upsell
Contributors
Contributors
Preface
Preface
Free Chapter
1
Setup and Introduction to TensorFlow
Setup and Introduction to TensorFlow
The TensorFlow way of thinking
Setting up and installing TensorFlow
TensorFlow API levels
Building your first TensorFlow model
Creating TensorFlow graphs
Feeding data with placeholders
Initializing variables
Training our model
Evaluating a trained model
The session
Summary
2
Deep Learning and Convolutional Neural Networks
Deep Learning and Convolutional Neural Networks
AI and ML
Artificial neural networks
Convolutional neural networks
Building a CNN model in TensorFlow
Summary
3
Image Classification in TensorFlow
Image Classification in TensorFlow
CNN model architecture
Datasets
Image classification with TensorFlow
Model Initialization
Improving generalization by regularizing
Summary
4
Object Detection and Segmentation
Object Detection and Segmentation
Image classification with localization
Object detection as classification – Sliding window
Single Shot Detectors – You Only Look Once
Semantic segmentation
Instance segmentation
Summary
5
VGG, Inception Modules, Residuals, and MobileNets
VGG, Inception Modules, Residuals, and MobileNets
Substituting big convolutions
VGGNet
GoogLeNet
Residual Networks
MobileNets
Summary
6
Autoencoders, Variational Autoencoders, and Generative Adversarial Networks
Autoencoders, Variational Autoencoders, and Generative Adversarial Networks
Why generative models
Autoencoders
Variational autoencoders
Generative adversarial networks
Summary
7
Transfer Learning
Transfer Learning
When?
How? An overview
How? Code example
Summary
8
Machine Learning Best Practices and Troubleshooting
Machine Learning Best Practices and Troubleshooting
Building Machine Learning Systems
Data Preparation
Bias and Variance
Data Imbalance
Evaluation Metrics
Code Structure best Practice
Recipe for CNN creation
Summary
9
Training at Scale
Training at Scale
Storing data in TFRecords
Making efficient pipelines
Distributed computing in TensorFlow
When data does not fit on one computer
Scaling computation in the cloud
Summary
References
References
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 7
Chapter 9
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Other Books You May Enjoy
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Index
Index

Copyright and Credits

Hands-On Convolutional Neural Networks with TensorFlow

Copyright © 2018 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Commissioning Editor: Amey VarangaonkarAcquisition Editor: Siddharth MandalContent Development Editor: Aditi GourTechnical Editor: Vaibhav DwivediCopy Editor: Safis EditingProject Coordinator: Hardik BhindeProofreader: Safis EditingIndexer: Tejal Daruwale SoniGraphics: Jason MonteiroProduction Coordinator: Deepika Naik

First published: August 2018

Production reference: 1240818

Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.

ISBN 978-1-78913-033-1

www.packtpub.com

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