This post was originally published at Create Data from Random Noise with Generative Adversarial Networks

Generative adversarial networks, among the most important machine learning breakthroughs of recent times, allow you to generate useful data from random noise. Instead of training one neural network with millions of data points, you let two neural networks contest with each other to figure things out.

In this article, Toptal Freelance Software Engineer Cody Nash gives us an overview of how GANs work and how this class of machine learning algorithms can be used to generate data in data-limited situations.

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This post was originally published at Create Data from Random Noise with Generative Adversarial Networks