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PyTorch for Computer Vision


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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.

Please register if you would like to join; we will sent instructions about the virtual session to all registrants in advance.


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Status Archived
Date Wednesday, March 25th, 2020
Time 4:30pm - 6:30pm
Location Virtual Classroom
Leader Kevin Liang
Enrolled 107