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Zafar - Hands-on convolutional neural networks with TensorFlow: solve computer vision problems with modeling in TensorFlow and Python

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Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges.

Key Features

  • Learn the fundamentals of Convolutional Neural Networks
    • Harness Python and Tensorflow to train CNNs
    • Build scalable deep learning models that can process millions of items

      Book Description

      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...

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    Hands-On Convolutional Neural Networks with TensorFlow Solve computer - photo 1
    Hands-On Convolutional Neural Networks with TensorFlow
    Solve computer vision problems with modeling in TensorFlow and Python.
    Iffat Zafar
    Giounona Tzanidou
    Richard Burton
    Nimesh Patel
    Leonardo Araujo

    BIRMINGHAM - MUMBAI Hands-On Convolutional Neural Networks with TensorFlow - photo 2

    BIRMINGHAM - MUMBAI
    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 Varangaonkar
    Acquisition Editor: Siddharth Mandal
    Content Development Editor: Aditi Gour
    Technical Editor: Vaibhav Dwivedi
    Copy Editor: Safis Editing
    Project Coordinator: Hardik Bhinde
    Proofreader: Safis Editing
    Indexer: Tejal Daruwale Soni
    Graphics: Jason Monteiro
    Production 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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    Contributors
    About the authors

    Iffat Zafar was born in Pakistan. She received her Ph.D. from the Loughborough University in Computer Vision and Machine Learning in 2008. After her Ph.D. in 2008, she worked as research associate at the Department of Computer Science, Loughborough University, for about 4 years. She currently works in the industry as an AI engineer, researching and developing algorithms using Machine Learning and Deep Learning for object detection and general Deep Learning tasks for edge and cloud-based applications.

    Giounona Tzanidou is a PhD in computer vision from Loughborough University, UK, where she developed algorithms for runtime surveillance video analytics. Then, she worked as a research fellow at Kingston University, London, on a project aiming at prediction detection and understanding of terrorist interest through intelligent video surveillance. She was also engaged in teaching computer vision and embedded systems modules at Loughborough University. Now an engineer, she investigates the application of deep learning techniques for object detection and recognition in videos.

    Richard Burton graduated from the University of Leicester with a master's degree in mathematics. After graduating, he worked as a research engineer at the University of Leicester for a number of years, where he developed deep learning object detection models for their industrial partners. Now, he is working as a software engineer in the industry, where he continues to research the applications of deep learning in computer vision.

    Nimesh Patel graduated from the University of Leicester with an MSc in applied computation and numerical modeling. During this time, a project collaboration with one of University of Leicesters partners was undertaken, dealing with Machine Learning for Hand Gesture recognition. Since then, he has worked in the industry, researching Machine Learning for Computer Vision related tasks, such as Depth Estimation.

    Leonardo Araujo is just the regular, Brazilian, curious engineer, who has worked in the industry for the past 19 years (yes, in Brazil, people work before graduation), doing HW/SW development and research on the topics of control engineering and computer vision. For the past 6 years, he has focused more on Machine Learning methods. His passions are too many to put on the book.

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    Preface

    This book is all about giving a practical, hands-on introduction to machine learning with the aim of enabling anyone to start working in the field. We'll focus mainly on deep learning methods and how they can be used to solve important computer vision problems, but the knowledge acquired here can be transferred to many different domains. Along the way, the reader will also get a grip of how to use the popular deep learning library, TensorFlow.

    Who this book is for

    Anyone interested in a practical guide to machine learning, specifically deep learning and computer vision, will particularly benefit from reading this book. In addition, the following people will also benefit:

    • Machine learning engineers
    • Data scientists
    • Developers interested in learning about the deep learning and computer vision fields
    • Students studying machine learning
    What this book covers
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