Generative adversarial networks (GANs) are a new tool in machine learning, that leverage advances in deep neural networks. Using GANs, one can develop a computer model that is capable of synthesizing highly realistic images, such as human faces and interesting art. There are many applications of GANs, both good and bad (e.g., "fake news"). GANs are one of the most important recent developments in machine learning and artificial intelligence (AI), and in this session participants will be introduced to this modeling framework. It will be explained why and how these models work, and why this framework is fundamentally different from prior methods of learning AI models. Applications of GANs will also be discussed.
This session is part of the Duke+DataScience (+DS) program virtual learning experiences (vLEs). To learn more, please visit https://plus.datascience.duke.edu