Deep learning on Windows : building deep learning computer vision systems on Microsoft Windows / Thimira Amaratunga.
2021
Q325.5
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Details
Title
Deep learning on Windows : building deep learning computer vision systems on Microsoft Windows / Thimira Amaratunga.
Author
Amaratunga, Thimira.
ISBN
9781484264317 (electronic bk.)
1484264312 (electronic bk.)
9781484264324 (print)
1484264320
1484264304
9781484264300
1484264312 (electronic bk.)
9781484264324 (print)
1484264320
1484264304
9781484264300
Publication Details
[Place of publication not identified] : Apress, 2021.
Language
English
Description
1 online resource
Item Number
10.1007/978-1-4842-6431-7 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
004.165
004.165
Summary
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.
Note
Includes index.
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text file
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Deep learning on Windows.
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Table of Contents
Chapter 1: What is Deep Learning
Chapter 2: Where to Start Your Deep Learning
Chapter 3: Setting Up Your Tools
Chapter 4: Building Your First Deep Learning Model
Chapter 5: Understanding What We Built
Chapter 6: Visualizing Models
Chapter 7: Transfer Learning
Chapter 8: Starting, Stopping and Resuming Learning
Chapter 9: Deploying Your Model as a Web Application
Chapter 10: Having Fun with Computer Vision
Chapter 11: Introduction to Generative Adversarial Networks
Chapter 12: Basics of Reinforcement Learning
Appendix 1: A History Lesson
Milestones of Deep Learning
Appendix 2: Optional Setup Steps.
Chapter 2: Where to Start Your Deep Learning
Chapter 3: Setting Up Your Tools
Chapter 4: Building Your First Deep Learning Model
Chapter 5: Understanding What We Built
Chapter 6: Visualizing Models
Chapter 7: Transfer Learning
Chapter 8: Starting, Stopping and Resuming Learning
Chapter 9: Deploying Your Model as a Web Application
Chapter 10: Having Fun with Computer Vision
Chapter 11: Introduction to Generative Adversarial Networks
Chapter 12: Basics of Reinforcement Learning
Appendix 1: A History Lesson
Milestones of Deep Learning
Appendix 2: Optional Setup Steps.