Krishnendu Kar - Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques
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- Book:Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques
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Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language
Key Features- Gain a fundamental understanding of advanced computer vision and neural network models in use today
- Cover tasks such as low-level vision, image classification, and object detection
- Develop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkit
Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. Youll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, youll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, youll learn to use visual search methods using transfer learning. Youll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GANs, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. Youll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, youll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, youll have a solid understanding of computer vision and be able to confidently develop models to automate tasks.
What you will learn- Explore methods of feature extraction and image retrieval and visualize different layers of the neural network model
- Use TensorFlow for various visual search methods for real-world scenarios
- Build neural networks or adjust parameters to optimize the performance of models
- Understand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpainting
- Evaluate your model and optimize and integrate it into your application to operate at scale
- Get up to speed with techniques for performing manual and automated image annotation
This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required to get started with this book.
Table of Contents- Computer Vision and Tensorflow Fundamentals
- Content Recognition using Local Binary Pattern
- Face Recognition and Tracking using Viola Jones Algorithm & OpenCV
- Deep learning on images
- Neural Network Architecture & Models
- Visual Search using Transfer Learning
- Object Detection using YOLO
- Semantic Segmentation and Neural Style Transfer
- Action Recognition using Multitask Deep Learning
- Object Classification and Detection using RCNN
- Deep Learning on Edge Devices with GPU/CPU Optimization
- Cloud Computing Platform for Computer Vision
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Copyright 2020 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: Mrinmayee Kawalkar
Acquisition Editor: Nelson Morris
Content Development Editor: Nazia Shaikh
Senior Editor: Ayaan Hoda
Technical Editor: Utkarsha S. Kadam
Copy Editor: Safis Editing
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Proofreader: Safis Editing
Indexer: Manju Arasan
Production Designer: Nilesh Mohite
First published: May 2020
Production reference: 1140520
Published by Packt Publishing Ltd.
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ISBN 978-1-83882-706-9
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Krishnendu Kar is passionate about research on computer vision and solving AI problems to make our life simpler. His core expertise is deep learning - computer vision, IoT, and agile software development. Krish is also a passionate app developer and has a dash cam-based object and lane detection and turn by turn navigation and fitness app in the iOS app store - Nity Map AI Camera& Run timer .
Meng-Chieh Ling has a Ph.D. degree in theoretical condensed matter physics from Karlsruhe Institute of Technology in Germany. He switched from physics to data science to pursue a successful career. After working for AGT International in Darmstadt for 2 years, he joined CHECK24 Fashion as a data scientist in Dsseldorf. His responsibilities include applying machine learning to improve the efficiency of data cleansing, automatic attribute tagging with deep learning, and developing image-based recommendation systems.
Amin Ahmadi Tazehkandi is an Iranian author, software engineer, and computer vision expert. He has worked at numerous software companies across the globe and has a long list of awards and achievements, including a countrywide hackathon win and an award-winning paper. Amin is an avid blogger and long-time contributor to the open source, cross-platform, and computer vision developer communities. He is the proud author of Computer Vision with OpenCV 3 and Qt5, and Hands-On Algorithms for Computer Vision.
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.
Computer vision is a technique by which machines gain human-level ability to visualize, process, and analyze images or videos. This book will focus on using TensorFlow to develop and train deep neural networks to solve advanced computer vision problems and deploy solutions on mobile and edge devices.
You will start with the key principles of computer vision and deep learning and learn about various models and architectures, along with their pros and cons. You will cover various architectures, such as VGG, ResNet, Inception, R-CNN, YOLO, and many more. You will use various visual search methods using transfer learning. The book will help you to learn about various advanced concepts of computer vision, including semantic segmentation, image inpainting, object tracking, video segmentation, and action recognition. You will explore how various machine learning and deep learning concepts can be applied in computer vision tasks such as edge detection and face recognition. Later in the book, you will focus on performance tuning to optimize performance, deploying dynamic models to improve processing power, and scaling to handle various computer vision challenges.
By the end of the book, you will have an in-depth understanding of computer vision and will know how to develop models to automate tasks.
This book is for computer vision professionals, image processing professionals, machine learning engineers, and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. Familiarity with Python programming and TensorFlow will be required for this book.
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