Introducing Generative Modeling
Understanding generative modeling
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1. Introducing Generative Adversarial Networks (GANs)
Course outline and prerequisites
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Set up the virtual environment and run the notebook server
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Introducing GANs
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Instantiating the dataset and data loader
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Viewing training data
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2. Stand-Alone Training of Adversaries
Big picture overview of a GAN
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Training the adversaries
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The generator architecture
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The discriminator architecture
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Understanding the generator and discriminator outputs
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Stand-alone training of a discriminator as a classification model
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Stand-alone training of a generator
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3. Training GANs
Computing losses for generators and discriminators
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Understanding the minimax loss function
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Setting up GAN training
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Visualizing GAN training results
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Problems with GANs and potential mitigations
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Conclusion
Summary and next steps
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Ex_Files_Hands_on_GANs.zip
(63.9 MB)