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Rajalingappaa Shanmugamani - Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras

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Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras: summary, description and annotation

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Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks

Key Features
  • Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision
  • Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more
  • Includes tips on optimizing and improving the performance of your models under various constraints
Book Description

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning.

In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.

What you will learn
  • Set up an environment for deep learning with Python, TensorFlow, and Keras
  • Define and train a model for image and video classification
  • Use features from a pre-trained Convolutional Neural Network model for image retrieval
  • Understand and implement object detection using the real-world Pedestrian Detection scenario
  • Learn about various problems in image captioning and how to overcome them by training images and text together
  • Implement similarity matching and train a model for face recognition
  • Understand the concept of generative models and use them for image generation
  • Deploy your deep learning models and optimize them for high performance
Who This Book Is For

This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python-and some understanding of machine learning concepts-is required to get the best out of this book.

Table of Contents
  1. Introduction to Deep Learning
  2. Image Classification
  3. Image Retrieval
  4. Object Detection
  5. Semantic Segmentation
  6. Similarity Learning
  7. Generative Models
  8. Image Captioning
  9. Video Classification
  10. Deployment

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Deep Learning for Computer Vision Expert techniques to train advanced - photo 1
Deep Learning for Computer Vision
Expert techniques to train advanced neural networks using TensorFlow and Keras
Rajalingappaa Shanmugamani

BIRMINGHAM - MUMBAI Deep Learning for Computer Vision Copyright 2018 Packt - photo 2

BIRMINGHAM - MUMBAI
Deep Learning for Computer Vision

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: Amey Varangaonkar
Acquisition Editor: Aman Singh
Content Development Editor: Varun Sony
Technical Editor: Dharmendra Yadav
Copy Editors: Safis Editing
Project Coordinator: Manthan Patel
Proofreader: Safis Editing
Indexer: Pratik Shirodkar
Graphics: Tania Dutta
Production Coordinator: Shantanu Zagade

First published: January 2018

Production reference: 1220118

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

ISBN 978-1-78829-562-8

www.packtpub.com

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Foreword

Deep learning is revolutionizing AI, and over the next several decades, it will change the world radically. AI powered by deep learning will be on par in scale with the industrial revolution. This, understandably, has created excitement and fear about the future. But the reality is that just like the industrial revolution and machinery, deep learning will improve industrial capacity and raise the standards of living dramatically for humankind. Rather than replace jobs, it will create many more jobs of a higher stand. This is why this book is so important and timely. Readers of this book will be introduced to deep learning for computer vision, its power, and many applications. This book will give readers a grounding in the fundamentals of an emerging industry that will grow exponentially over the next decade.

Rajalingappaa Shanmugamani is a great researcher whom I have worked with previously on several projects in computer vision. He was the lead engineer in designing and delivering a complex computer vision and deep learning system for fashion search that was deployed in the real world with great success. Among his strengths is his ability to take up state-of-the-art research in complex problems and apply them to real-world situations. He can also break down complex ideas and explain them in simple terms as is demonstrated in this book. Raja is a very ambitious person with great work ethics, and in this book, he has given a great overview of the current state of computer vision using deep learning, a task not many can do in today's industry. This book is a great achievement by Raja and Im sure the reader will enjoy and benefit from it for many years to come.

Dr. Stephen Moore

Chief Technology Officer, EmotionReader, Singapore

Contributors
About the author

Rajalingappaa Shanmugamani is currently working as a Deep Learning Lead at SAP, Singapore. Previously, he has worked and consulted at various startups for developing computer vision products. He has a Masters from Indian Institute of Technology Madras where his thesis was based on applications of computer vision in the manufacturing industry. He has published articles in peer-reviewed journals and conferences and applied for few patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

I thank my spouse Ezhil, family and friends for their immense support. I thank all the teachers, colleagues, managers and mentors from whom I have learned a lot. I thank Jean Ooi for creating the graphics for the book.
About the reviewers

Nishanth Koganti received B.Tech in Electrical Engineering from Indian Institute of Technology Jodhpur, India in 2012, M.E and PhD in Information Science from Nara Institute of Science and Technology, Japan in 2014, 2017 respectively. He is currently a Postdoctoral researcher at the University of Tokyo, Japan. His research interests are in assistive robotics, motor-skills learning, and machine learning. His graduate research was on the development of a clothing assistance robot that helps elderly people to wear clothes.

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Preface

Deep Learning for Computer Vision is a book intended for readers who want to learn deep-learning-based computer vision techniques for various applications. This book will give the reader tools and techniques to develop computer-vision-based products. There are plenty of practical examples covered in the book to follow the theory.

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