Stark, Emily Nicole

The success of deep learning in applications including computer vision, natural language processing, and even the game of Go can only be a orded by powerful computational resources and vast data sets. Data sets coming from the medical application are often much smaller and harder to acquire. Here a novel data approach is explained and used to demonstrate how to use deep learning as a step in data discovery, classi cation, and ultimately support for further investigation. Data sets used to illustrate these successes come from common ion-separation techniques that allow for gas samples to be quantitatively analyzed. The success of this data approach allows for the deployment of deep learning to smaller data sets.