Deep learning on Windows : building deep learning computer vision systems on Microsoft Windows
Author
Published
[Place of publication not identified] : Apress, 2021.
Status
Available Online
Description
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Format
Language
English
ISBN
9781484264317, 1484264312, 9781484264324, 1484264320
UPC
10.1007/978-1-4842-6431-7
Notes
General Note
Includes index.
Description
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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O'Reilly,O'Reilly Online Learning: Academic/Public Library Edition
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Citations
APA Citation, 7th Edition (style guide)
Amaratunga, T. (2021). Deep learning on Windows: building deep learning computer vision systems on Microsoft Windows . Apress.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Amaratunga, Thimira. 2021. Deep Learning On Windows: Building Deep Learning Computer Vision Systems On Microsoft Windows. Apress.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Amaratunga, Thimira. Deep Learning On Windows: Building Deep Learning Computer Vision Systems On Microsoft Windows Apress, 2021.
MLA Citation, 9th Edition (style guide)Amaratunga, Thimira. Deep Learning On Windows: Building Deep Learning Computer Vision Systems On Microsoft Windows Apress, 2021.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouped Work ID
269a455b-024e-660a-c3af-121001ec4adb-eng
Grouping Information
Grouped Work ID | 269a455b-024e-660a-c3af-121001ec4adb-eng |
---|---|
Full title | deep learning on windows building deep learning computer vision systems on microsoft windows |
Author | amaratunga thimira |
Grouping Category | book |
Last Update | 2024-03-29 07:51:22AM |
Last Indexed | 2024-05-16 02:09:01AM |
Book Cover Information
Image Source | contentCafe |
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First Loaded | Nov 12, 2023 |
Last Used | Nov 12, 2023 |
Marc Record
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Last File Modification Time | Mar 21, 2023 11:04:53 AM |
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505 | 0 | |a 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. | |
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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