COVID+DS: PyTorch for image analysis with deep learning
The goal of computer vision is for computers to be able to understand visual content (e.g. images, videos, 3D, stereo), usually for the purpose of making predictions (classification, detection, captioning, generation, etc.). Modern computer vision models are almost universally based on convolutional neural networks (CNNs), whose recent developments have lead to increasing adoption and deployment of deep learning models in a wide number of fields. In this hands-on session, we'll introduce how to build CNNs in PyTorch, as well as how to load datasets and pre-trained models using PyTorch's vision library, Torchvision. These tools form the foundation for the chest CT imaging COVID diagnosis work presented the following week (on July 28).
This session is part of the Duke+Data Science (+DS) program virtual series on COVID-19 + Data Science. Please join us for a 8-week series on data science methods with direct applications to the COVID-19 pandemic. Learn from Duke experts about the state-of-the-art in these 1-hour virtual sessions. For more information, please visit https://plus.datascience.duke.edu