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2 cards from this section. Quick, good the night before a test.
What are the main components of a GAN?
Think about the two roles in creating and verifying data.
What is the purpose of an Autoencoder?
Consider how data is processed and returned.
What are GANs primarily used for?
Think about the context of what GN stands for.
True or False: Autoencoders can only be used for supervised learning.
Consider how Autoencoders learn from data.
Consider a scenario where you have a dataset of images and want to generate new images based on it using GANs. Describe the steps you would take to implement this.
Think about the iterative training process in GANs.
Design an Autoencoder for a dataset of handwritten digits. What architecture would you use and how would you validate its performance?
Consider the architecture that minimizes loss in reconstruction.
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