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DTSTART:20230519T200000Z
DTEND:20230519T213000Z
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SUMMARY:Machine Learning: Finding pre-trained models for transfer learning
DESCRIPTION:Abstract:\nTraining models from scratch requires way too much 
 data\, time\, and computing power (or money) to be a practical option. This 
 is why transfer learning has become such a common practice: by starting 
 with models trained on related problems\, you are saving time and achieving 
 good results with little data.\nNow\, where do you find such models?\nIn 
 this workshop\, we will have a look at some of the most popular pre-trained 
 models repositories and libraries (Model Zoo\, PyTorch Hub\, and Hugging 
 Face)\; see how you can also search models in the literature and on 
 GitHub\, and finally learn how to import models into 
 PyTorch.\n\nPrerequisites:\nIf you want to follow the hands-on part of this 
 workshop\, please make sure to have an up-to-date version of PyTorch 
 (https://pytorch.org/get-started/locally/) on your laptop.\n\n 
 \n\nPresenter:\nMarie-Hélène Burle. An evolutionary and behavioural 
 ecologist by training\, Software/Data Carpentry instructor\, and open 
 source advocate\, Marie-Hélène Burle develops and delivers training for 
 researchers on high-performance computing tools (R\, Python\, Julia\, Git\, 
 Bash scripting\, machine learning\, parallel scientific programming\, and 
 HPC) for Simon Fraser University and the Digital Research Alliance of 
 Canada.
ORGANIZER;CN="Eugene Barsky":MAILTO:eugene.barsky@ubc.ca
CATEGORIES:Data, Research Commons, Research Data Management
CONTACT;CN="Eugene Barsky":MAILTO:eugene.barsky@ubc.ca
STATUS:CONFIRMED
UID:LibCal-3707039
URL:https://libcal.library.ubc.ca/event/3707039
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