Sign In Start Free Trial

Book Overview & Buying
Table Of Contents

Hands-On Convolutional Neural Networks with TensorFlow

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

4 (3)

Hands-On Convolutional Neural Networks with TensorFlow

4 (3)

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

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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

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

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

Image Classification in TensorFlow

Image Classification in TensorFlow

CNN model architecture

Datasets

Image classification with TensorFlow

Model Initialization

Improving generalization by regularizing

Summary

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

VGG, Inception Modules, Residuals, and MobileNets

VGG, Inception Modules, Residuals, and MobileNets

Substituting big convolutions

VGGNet

GoogLeNet

Residual Networks

MobileNets

Summary

Autoencoders, Variational Autoencoders, and Generative Adversarial Networks

Autoencoders, Variational Autoencoders, and Generative Adversarial Networks

Why generative models

Autoencoders

Variational autoencoders

Generative adversarial networks

Summary

Transfer Learning

Transfer Learning

When?

How? An overview

How? Code example

Summary

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

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

Other Books You May Enjoy

Other Books You May Enjoy

Leave a review - let other readers know what you think

Get in touch

Feedback from our readers is always welcome.

General feedback: Email [email protected] and mention the book title in the subject of your message. If you have questions about any aspect of this book, please email us at [email protected].

Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit www.packtpub.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details.

Piracy: If you come across any illegal copies of our works in any form on the Internet, we would be grateful if you would provide us with the location address or website name. Please contact us at [email protected] with a link to the material.

If you are interested in becoming an author: If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, please visit authors.packtpub.com.

Reviews

Please leave a review. Once you have read and used this book, why not leave a review on the site that you purchased it from? Potential readers can then see and use your unbiased opinion to make purchase decisions, we at Packt can understand what you think about our products, and our authors can see your feedback on their book. Thank you!

For more information about Packt, please visit packtpub.com.

CONTINUE READING

83

Tech Concepts

36

Programming languages

73

Tech Tools

Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

50+ new titles added per month and exclusive early access to books as they are being written.

Hands-On Convolutional Neural Networks with TensorFlow

Search

Your notes and bookmarks