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hi everyone im patrick from the assembly ai team and today we learn about generative adversarial networks or short gans so you might have seen this popular example where gans generate images of humans and they look incredibly real gans are indeed really powerful and are one of the most fascinating ideas in deep learning in recent years so today we have a quick look at the theory behind gans and then we code one from scratch using pytorch so lets get started alright so lets look at the theory first and i promise that this wont be too difficult because the idea is actually brilliant its simple but super powerful so gans learn to generate new data with the same statistics as the training set and gans consist of two networks playing an adversarial game against each other thats why the name is generative adversarial networks so the goal is to generate data that is as close as possible to the training data and then we have these two networks that play a game against each other so how