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Deep learning on Windows = building ...

Amaratunga, Thimira.

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Deep learning on Windows = building deep learning computer vision systems on Microsoft Windows /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning on Windows/ by Thimira Amaratunga.
其他題名:	building deep learning computer vision systems on Microsoft Windows /
作者:	Amaratunga, Thimira.
出版者:	Berkeley, CA :Apress : : 2021.,
面頁冊數:	xviii, 338 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-1-4842-6431-7
ISBN:	9781484264317

Deep learning on Windows = building deep learning computer vision systems on Microsoft Windows /
Amaratunga, Thimira.

Deep learning on Windowsbuilding deep learning computer vision systems on Microsoft Windows /[electronic resource] :by Thimira Amaratunga. - Berkeley, CA :Apress :2021. - xviii, 338 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9781484264317

Standard No.: 10.1007/978-1-4842-6431-7doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

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520 $a 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.
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