23,92 €
Mehr erfahren.
- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Explore TensorFlow's capabilities to perform efficient deep learning on images
Key Features
- Discover image processing for machine vision
- Build an effective image classification system using the power of CNNs
- Leverage TensorFlow's capabilities to perform efficient deep learning
Book Description
TensorFlow is Google's popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks.
Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow's capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow.
By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.
What you will learn
- Build machine learning models particularly focused on the MNIST digits
- Work with Docker and Keras to build an image classifier
- Understand natural language models to process text and images
- Prepare your dataset for machine learning
- Create classical, convolutional, and deep neural networks
- Create a RESTful image classification server
Who this book is for
Hands-On Deep Learning for Images with TensorFlow is for you if you are an application developer, data scientist, or machine learning practitioner looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and basics of deep learning are required to get the best out of this book.
Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:
Seitenzahl: 89
Veröffentlichungsjahr: 2018
Ähnliche
Hands-On Deep Learning for Images 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 author, 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: Sunith ShettyAcquisition Editor: Joshua NadarContent Development Editor: Dinesh PawarTechnical Editor: Suwarna Patil Copy Editor: SAFISProject Coordinator: Nidhi JoshiProofreader: SAFISIndexer: Pratik ShirodkarGraphics: Jisha ChirayilProduction Coordinator: Shantanu Zagade
First published: July 2018
Production reference: 1300718
Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.
ISBN 978-1-78953-867-0
www.packtpub.com
Mapt is an online digital library that gives you full access to over 5,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website.
Why subscribe?
Spend less time learning and more time coding with practical eBooks and Videos from over 4,000 industry professionals
Improve your learning with Skill Plans built especially for you
Get a free eBook or video every month
Mapt is fully searchable
Copy and paste, print, and bookmark content
PacktPub.com
Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at www.PacktPub.com and as a print book customer, you are entitled to a discount on the eBook copy. Get in touch with us at [email protected] for more details.
At www.PacktPub.com, you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on Packt books and eBooks.
Contributors
About the author
Will Ballard is the chief technology officer at GLG, responsible for engineering and IT. He was also responsible for the design and operation of large data centers that helped run site services for customers including Gannett, Hearst Magazines, NFL.com, NPR, The Washington Post, and Whole Foods. He has also held leadership roles in software development at NetSolve (now Cisco), NetSpend, and Works.com (now Bank of America).
Packt is searching for authors like you
If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.
Table of Contents
Title Page
Copyright and Credits
Hands-On Deep Learning for Images with TensorFlow
Packt Upsell
Why subscribe?
PacktPub.com
Contributors
About the author
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Reviews
Machine Learning Toolkit
Installing Docker
The machine learning Docker file
Sharing data
Machine learning REST service
Summary
Image Data
MNIST digits
Tensors – multidimensional arrays
Turning images into tensors
Turning categories into tensors
Summary
Classical Neural Network
Comparison between classical dense neural networks
Activation and nonlinearity
Softmax
Training and testing data
Dropout and Flatten
Solvers
Hyperparameters
Grid searches
Summary
A Convolutional Neural Network
Convolutions
Pooling
Building a convolutional neural network
Deep neural network
Summary
An Image Classification Server
REST API definition
Trained models in Docker containers
Making predictions
Summary
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
TensorFlow is Google's popular offering for machine learning and deep learning. It has quickly become a popular choice of tool for performing fast, efficient, and accurate deep learning tasks.
This book shows you practical implementations of real-world projects, teaching you how to leverage TensorFlow's capabilities to perform efficient deep learning. In this book, you will be acquainted with the different paradigms of performing deep learning, such as deep neural nets, convolutional neural networks, recurrent neural networks, and more, and how they can be implemented using TensorFlow.
This will be demonstrated with the help of end-to-end implementations of three real-world projects on popular topic areas such as natural language processing, image classification, and fraud detection.
By the end of this book, you will have mastered all the concepts of deep learning and their implementations with TensorFlow and Keras.
Who this book is for
This book is for application developers, data scientists, and machine learning practitioners looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and the basics of deep learning is required to get the most out of this book.
What this book covers
Chapter 1, Machine Learning Toolkit, looks into installing Docker, setting up a machine learning Docker file, sharing data back with your host computer, and running a REST service to provide the environment.
Chapter 2, Image Data, teaches MNIST digits, how to acquire them, how tensors are really just multidimensional arrays, and how we can encode image data and categorical or classification data as a tensor. Then, we have a quick review and a cookbook approach to consider dimensions and tensors, in order to get data prepared for machine learning.
Chapter 3, Classical Neural Network, covers an awful lot of material! We see the structure of the classical, or dense, neural network. We learn about activation, nonlinearity, and softmax. We then set up testing and training data and learn how to construct the network with Dropout and Flatten. We also learn all about solvers, or how machine actually learns. We then explore hyperparameters, and finally, we fine-tune our model by means of grid search.
Chapter 4, A Convolutional Neural Network, teaches you convolutions, which are a loosely connected way of moving over an image to extract features. Then we learn about pooling, which summarizes the most important features. We will build a convolutional neural network using these techniques and we combine many layers of convolution and pooling in order to generate a deep neural network.
Chapter 5, An Image Classification Server, uses a Swagger API definition to create a REST API model, which then declaratively generates the Python framework in order for us to serve that API. Then, we create a Docker container that captures not only our running code (that is, our service) but also our pre-trained machine learning model. This then forms a package so that we are able to deploy and use our container. Finally, we use this container to serve and make predictions.
To get the most out of this book
You'll need:
Experience with command-line shell
Experience with Python scripting or application development
Download the example code files
You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
Log in or register at
www.packtpub.com
.
Select the
SUPPORT
tab.
Click on
Code Downloads & Errata
.
Enter the name of the book in the
Search
box and follow the onscreen instructions.
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
WinRAR/7-Zip for Windows
Zipeg/iZip/UnRarX for Mac
7-Zip/PeaZip for Linux
The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Deep-Learning-for-Images-with-TensorFlow. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Conventions used
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "You just have to type docker --help to make sure that everything is installed."
Any command-line input or output is written as follows:
C:\11519>docker build -t keras .
Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "We're going to select and copy the test command we'll be using later, and click on Apply."
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
