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Deep Learning on Windows

Building Deep Learning Computer Vision Systems on Microsoft Windows

  • Thimira Amaratunga
Book

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

Introduction

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

Keywords

Deep Learning Artificial Intelligence AI TensorFlow Windows Keras OpenCV

Authors and affiliations

  • Thimira Amaratunga
    • 1
  1. 1.NugegodaSri Lanka

Bibliographic information

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