© 2021

Deep Learning on Windows

Building Deep Learning Computer Vision Systems on Microsoft Windows

  • Covers deep learning web application design and development

  • Discusses Python, Dlib, Anaconda, and TensorFlow to implement deep learning on Windows

  • Contains real-time deep learning object identification


Table of contents

  1. Front Matter
    Pages i-xviii
  2. Thimira Amaratunga
    Pages 1-14
  3. Thimira Amaratunga
    Pages 15-31
  4. Thimira Amaratunga
    Pages 33-66
  5. Thimira Amaratunga
    Pages 67-100
  6. Thimira Amaratunga
    Pages 101-114
  7. Thimira Amaratunga
    Pages 115-130
  8. Thimira Amaratunga
    Pages 131-179
  9. Thimira Amaratunga
    Pages 181-213
  10. Thimira Amaratunga
    Pages 215-231
  11. Thimira Amaratunga
    Pages 233-251
  12. Thimira Amaratunga
    Pages 253-286
  13. Thimira Amaratunga
    Pages 287-310
  14. Back Matter
    Pages 311-338

About this book


Build deep learning and computer vision systems using Python, TensorFlow, Keras, OpenCV, and more, right within the familiar environment of Microsoft Windows. The book starts with an introduction to tools for deep learning and computer vision tasks followed by instructions to install, configure, and troubleshoot them. Here, you will learn how Python can help you build deep learning models on Windows. 

Moving forward, you will build a deep learning model and understand the internal-workings of a convolutional neural network on Windows. Further, you will go through different ways to visualize the internal-workings of deep learning models along with an understanding of transfer learning where you will learn how to build model architecture and use data augmentations. Next, you will manage and train deep learning models on Windows before deploying your application as a web application. You’ll also do some simple image processing and work with computer vision options that will help you build various applications with deep learning. Finally, you will use generative adversarial networks along with reinforcement learning. 

After reading Deep Learning on Windows, you will be able to design deep learning models and web applications on the Windows operating system. 

You will:

  • Understand the basics of Deep Learning and its history
  • Get Deep Learning tools working on Microsoft Windows
  • Understand the internal-workings of Deep Learning models by using model visualization techniques, such as the built-in plot_model function of Keras and third-party visualization tools
  • Understand Transfer Learning and how to utilize it to tackle small datasets
  • Build robust training scripts to handle long-running training jobs
  • Convert your Deep Learning model into a web application
  • Generate handwritten digits and human faces with DCGAN (Deep Convolutional Generative Adversarial Network)
  • Understand the basics of Reinforcement Learning


Deep Learning Artificial Intelligence AI TensorFlow Windows Keras OpenCV

Authors and affiliations

  1. 1.NugegodaSri Lanka

About the authors

Thimira Amaratunga is an Inventor, a Senior Software Architect at Pearson PLC Sri Lanka with over 12 years of industry experience, and a researcher in AI, Machine Learning, and Deep Learning in Education and Computer Vision domains.

Thimira holds a Master of Science in Computer Science with a Bachelor's degree in Information Technology from the University of Colombo, Sri Lanka. He has filed three patents to date, in the fields of dynamic neural networks and semantics for online learning platforms. Before this, Thimira has published two books on deep learning – ‘Build Deeper: The Deep Learning Beginners’ Guide’ and ‘Build Deeper: The Path to Deep Learning’.

Thimira is also the author of Codes of Interest (, a portal for deep learning and computer vision knowledge, covering everything from concepts to step-by-step tutorials.


Bibliographic information

  • Book Title Deep Learning on Windows
  • Book Subtitle Building Deep Learning Computer Vision Systems on Microsoft Windows
  • Authors Thimira Amaratunga
  • DOI
  • Copyright Information Thimira Amaratunga 2021
  • Publisher Name Apress, Berkeley, CA
  • eBook Packages Professional and Applied Computing Professional and Applied Computing (R0) Apress Access Books
  • Softcover ISBN 978-1-4842-6430-0
  • eBook ISBN 978-1-4842-6431-7
  • Edition Number 1
  • Number of Pages XVIII, 338
  • Number of Illustrations 181 b/w illustrations, 7 illustrations in colour
  • Topics Microsoft and .NET
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