Abstract
For many AI projects, deep learning techniques are increasingly being used as the building blocks for innovative solutions ranging from image classification to object detection, image segmentation, image similarity, and text analytics (e.g., sentiment analysis, key phrase extraction). GANs, first introduced by Goodfellow et al. (2014), are emerging as a powerful new approach toward teaching computers how to do complex tasks through a generative process. As noted by Yann LeCun (at http://bit.ly/LeCunGANs ), GANs are truly the “coolest idea in machine learning in the last 20 years.”
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© 2018 Mathew Salvaris, Danielle Dean, Wee Hyong Tok
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Salvaris, M., Dean, D., Tok, W.H. (2018). Generative Adversarial Networks. In: Deep Learning with Azure. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3679-6_8
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DOI: https://doi.org/10.1007/978-1-4842-3679-6_8
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-3678-9
Online ISBN: 978-1-4842-3679-6
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