+DS vLE: Deep Learning with PyTorch for Image Analysis

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 session on "Convolutional Neural Networks for Image Analysis," offered on September 22.

This session is part of the Duke+DataScience (+DS) program virtual learning experiences (vLEs). To learn more, please visit https://plus.datascience.duke.edu