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Practical Computer Vision Applications Using Deep Learning with CNNs

With Detailed Examples in Python Using TensorFlow and Kivy

  • Book
  • © 2018

Overview

Authors:
  1. Ahmed Fawzy Gad

    1. Menoufia, Egypt

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  • Explains the basic concepts of deep learning using numerical examples
  • Discusses the practical use of deep convolutional neural networks in computer vision with Python
  • Covers deploying trained models

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xxii
    Download chapter PDF

  2. Recognition in Computer Vision

    • Ahmed Fawzy Gad
    Pages 1-44

  3. Artificial Neural Networks

    • Ahmed Fawzy Gad
    Pages 45-106

  4. Recognition Using ANN with Engineered Features

    • Ahmed Fawzy Gad
    Pages 107-128

  5. ANN Optimization

    • Ahmed Fawzy Gad
    Pages 129-181

  6. Convolutional Neural Networks

    • Ahmed Fawzy Gad
    Pages 183-227

  7. TensorFlow Recognition Application

    • Ahmed Fawzy Gad
    Pages 229-294

  8. Deploying Pretrained Models

    • Ahmed Fawzy Gad
    Pages 295-338

  9. Cross-Platform Data Science Applications

    • Ahmed Fawzy Gad
    Pages 339-380

  10. Back Matter

    Pages 381-405
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Keywords

About this book

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms. 



For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model.


After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads.


This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production. 





What You Will Learn 
  • Understand how ANNs and CNNs work 
  • Create computer vision applications and CNNs from scratch using Python
  • Follow a deep learning project from conception to production using TensorFlow
  • Use NumPy with Kivy to build cross-platform data science applications



Who This Book Is For
Data scientists, machine learning and deep learning engineers, software developers.


Authors and Affiliations

  • Menoufia, Egypt

    Ahmed Fawzy Gad

About the author

Ahmed Fawzy Gad is a teaching assistant at the Faculty of Computers and Information (FCI), Menoufia University, Egypt. He has done his MSc in Computer Science. Ahmed is interested in deep learning, machine learning, computer vision, and Python. He aims to add value to the data science community by sharing his writings and tutorials. He is the author of the book "Practical Computer Vision Applications Using Deep Learning with CNN's" published by Apress. 






Bibliographic Information

  • Book Title: Practical Computer Vision Applications Using Deep Learning with CNNs

  • Book Subtitle: With Detailed Examples in Python Using TensorFlow and Kivy

  • Authors: Ahmed Fawzy Gad

  • DOI: https://doi.org/10.1007/978-1-4842-4167-7

  • Publisher: Apress Berkeley, CA

  • eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)

  • Copyright Information: Ahmed Fawzy Gad 2018

  • Softcover ISBN: 978-1-4842-4166-0Published: 06 December 2018

  • eBook ISBN: 978-1-4842-4167-7Published: 05 December 2018

  • Edition Number: 1

  • Number of Pages: XXII, 405

  • Number of Illustrations: 200 b/w illustrations

  • Topics: Artificial Intelligence, Python, Open Source

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