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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.

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.