Deep Learning for Computer Vision with SAS: An Introduction
(eBook)

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Published
SAS Institute, 2020.
Format
eBook
ISBN
9781642959178
Status
Available Online

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Language
English

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Citations

APA Citation, 7th Edition (style guide)

Robert Blanchard., & Robert Blanchard|AUTHOR. (2020). Deep Learning for Computer Vision with SAS: An Introduction . SAS Institute.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Robert Blanchard and Robert Blanchard|AUTHOR. 2020. Deep Learning for Computer Vision With SAS: An Introduction. SAS Institute.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Robert Blanchard and Robert Blanchard|AUTHOR. Deep Learning for Computer Vision With SAS: An Introduction SAS Institute, 2020.

MLA Citation, 9th Edition (style guide)

Robert Blanchard, and Robert Blanchard|AUTHOR. Deep Learning for Computer Vision With SAS: An Introduction SAS Institute, 2020.

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 ID9cb6d2ce-0eb5-c589-5404-cbf7b8701240-eng
Full titledeep learning for computer vision with sas an introduction
Authorblanchard robert
Grouping Categorybook
Last Update2024-05-15 02:01:02AM
Last Indexed2024-06-15 04:31:03AM

Book Cover Information

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First LoadedMar 26, 2023
Last UsedMar 26, 2023

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    [synopsis] => Discover deep learning and computer vision with SAS!

Deep Learning for Computer Vision with SAS®: An Introduction introduces the pivotal components of deep learning. Readers will gain an in-depth understanding of how to build deep feedforward and convolutional neural networks, as well as variants of denoising autoencoders. Transfer learning is covered to help readers learn about this emerging field. Containing a mix of theory and application, this book will also briefly cover methods for customizing deep learning models to solve novel business problems or answer research questions. SAS programs and data are included to reinforce key concepts and allow readers to follow along with included demonstrations.

Readers will learn how to:

• Define and understand deep learning

• Build models using deep learning techniques and SAS Viya

• Apply models to score (inference) new data

• Modify data for better analysis results

• Search the hyperparameter space of a deep learning model

• Leverage transfer learning using supervised and unsupervised methods
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