Before reading this post, it is helpful to understand what a neural Network is. Please see the following if you need a refresher:
What is a Neural Network?
Before describing how a Generative Adversarial Network (GAN) works, let us start with the goal, which is to create a network that can generate outputs that are indistinguishable from the given set of real objects.
A neural network, at its most basic level, can be thought of as a function that consists of an input, a transformation process, and an output. One helpful analogy for this is the Plinko game in The Price is Right.
GAN used to generate art based on the Diogenes Lantern wildflower
Overview: In the real world, you would normally start with a data set and attempt to find a model that best fits that problem; however, there is a lot of value in starting with a data set that you know will exactly fit a model and add complexity to see the effects of the assumptions made by a model.
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