How to Design and Train Generative Adversarial Networks (GANs)
Get an overview of generative adversarial networks (GANs) and walk through how to design and train one using MATLAB®.
GANs are composed of two deep neural networks, a generator and a discriminator, which are adversaries of each other (thus the term “adversarial”). The generator creates new data instances, while the discriminator evaluates them for authenticity (i.e., it decides whether each instance of data that it reviews belongs to the actual training dataset or not).
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GANs are composed of two deep neural networks, a generator and a discriminator, which are adversaries of each other (thus the term “adversarial”). The generator creates new data instances, while the discriminator evaluates them for authenticity (i.e., it decides whether each instance of data that it reviews belongs to the actual training dataset or not).
Additional Resources:
Learn more: https://bit.ly/3f1cTpd
Join us on Telegram: https://t.me/matlabirawen
Join us on Facebook Group: https://www.facebook.com/groups/matlabcodes
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