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COVID+DS: PyTorch for image analysis with deep learning


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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.

On the day before the session, all registrants will receive an e-mail with a link and meeting information.


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Status Archived
Date Thursday, July 23rd, 2020
Time 4:00pm - 5:00pm
Location Virtual Classroom
Leader Timothy Dunn
Enrolled 228