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Creating synthetic data for replication & privacy protection using generative adversarial networks.

Author: Christian Arnold
Publisher: London : SAGE Publications Ltd, 2019.
Edition/Format:   eVideo : Clipart/images/graphics : English
Summary:
Christian Arnold, PhD, Lecturer in Politics at Cardiff University, discusses his research using generative adversarial networks (GANs) to create synthetic data for replication and privacy protection, including how GANs work, issues addressed by GANs, recommendations to students interested in research using GANs, and why social scientists should be working with big data and using data science methods.
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Details

Material Type: Clipart/images/graphics, Internet resource, Videorecording
Document Type: Internet Resource, Computer File, Visual material
All Authors / Contributors: Christian Arnold
ISBN: 9781526496300 1526496305
OCLC Number: 1104479696
Language Note: Closed-captions in English.
Performer(s): Academic, Christian Arnold PhD.
Description: 1 online resource (1 video file (00:12:49)) : sound, colour
Other Titles: Creating synthetic data for replication and privacy protection using generative adversarial networks

Abstract:

Christian Arnold, PhD, Lecturer in Politics at Cardiff University, discusses his research using generative adversarial networks (GANs) to create synthetic data for replication and privacy protection, including how GANs work, issues addressed by GANs, recommendations to students interested in research using GANs, and why social scientists should be working with big data and using data science methods.

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