ich images are likely to be faces, and which aren't. Mathematically, this involves modeling a probability distribution on images, that is, a function that tells us which images are likely to be faces and which aren't. This type of problem&mdash;modeling a function on a high-dimensional space&mdash;is exactly the sort of thing neural networks are made for.
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      The big insights that defines a GAN is to set up this modeling problem as a kind of contest. This is where the "adversar