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Deep Learning on Windows: Building Deep Learning Computer Vision Systems on Microsoft Windows
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.
What You Will Learn
Understand the basics of Deep Learning and its historyGet Deep Learning tools working on Microsoft WindowsUnderstand 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 toolsUnderstand Transfer Learning and how to utilize it to tackle small datasetsBuild robust training scripts to handle long-running training jobsConvert your Deep Learning model into a web applicationGenerate handwritten digits and human faces with DCGAN (Deep Convolutional Generative Adversarial Network)Understand the basics of Reinforcement Learning Who This Book Is For
AI developers and enthusiasts wanting to work on the Windows platform.
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.
What You Will Learn
Understand the basics of Deep Learning and its historyGet Deep Learning tools working on Microsoft WindowsUnderstand 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 toolsUnderstand Transfer Learning and how to utilize it to tackle small datasetsBuild robust training scripts to handle long-running training jobsConvert your Deep Learning model into a web applicationGenerate handwritten digits and human faces with DCGAN (Deep Convolutional Generative Adversarial Network)Understand the basics of Reinforcement Learning Who This Book Is For
AI developers and enthusiasts wanting to work on the Windows platform.
429 pages, Kindle Edition
Published December 15, 2020
About the author
Thimira Amaratunga
8 books5 followersSoftware Architect at Pearson | AI Practitioner | Inventor | Author
An inventor. A software architect with over 10 years of industry experience. A practitioner and a researcher in AI & machine learning in education and computer vision domains.
Has a Masters in Computer Science with a Bachelors in IT. Has filed 3 patents to date, in the fields of dynamic neural networks and semantics for online learning platforms. Author of 2 books in deep learning & AI.
An inventor. A software architect with over 10 years of industry experience. A practitioner and a researcher in AI & machine learning in education and computer vision domains.
Has a Masters in Computer Science with a Bachelors in IT. Has filed 3 patents to date, in the fields of dynamic neural networks and semantics for online learning platforms. Author of 2 books in deep learning & AI.
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