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DTSTART;TZID=America/Detroit:20201110T100000
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UID:10000385-1605002400-1605009600@micde.umich.edu
SUMMARY:Image Segmentation using Deep Learning with FastAI
DESCRIPTION:This workshop will demonstrate how to perform image segmentation using the FastAI [fast.ai] Python library\, which is built on the deep learning library PyTorch. Some familiarity with Python is expected\, but no previous experience with FastAI or PyTorch is needed. The workshop will be done online via BlueJeans. We will run the code using Google Colab\, which requires a Google account.
URL:https://micde.umich.edu/event/image-segmentation-using-deep-learning-with-fastai/
LOCATION:Your Desktop
CATEGORIES:Workshops
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DTSTART;TZID=America/Detroit:20201116T130000
DTEND;TZID=America/Detroit:20201116T160000
DTSTAMP:20260607T011046
CREATED:20230905T171255Z
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UID:10000389-1605531600-1605542400@micde.umich.edu
SUMMARY:Advanced Graphics Optimization For Data Visualization In Unity3D
DESCRIPTION:BlueJeans link will be shared with registered attendees 24 hours before start \nModern 3D game engines and computer hardware can render convincing graphics\, rivaling that of pre-rendered 3D animation. But video games still require special optimization techniques and tricks. This relates directly to perceived capabilities for data visualization and serious applications: we can generate and render thousands of interactive objects. But what about millions? \nThis workshop will go over different techniques to render as many objects as possible at once in Unity3D\, with the context of visualizing data as a point-cloud. Examples will include (but not be limited to) GPU Instancing\, Unity’s Particle System\, and Compute Shaders. It is strongly recommended that attendees be familiar with Unity3D prior to this workshop to get the most out of the session.
URL:https://micde.umich.edu/event/advanced-graphics-optimization-for-data-visualization-in-unity3d/
LOCATION:Your Desktop
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DTSTART;TZID=America/Detroit:20201119T133000
DTEND;TZID=America/Detroit:20201119T160000
DTSTAMP:20260607T011046
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UID:10000396-1605792600-1605801600@micde.umich.edu
SUMMARY:Map making in R
DESCRIPTION:The focus of the workshop is twofold: to learn cartography principles for generating single and multivariable choropleth maps\, and explore functionalities of R for generating static and interactive web maps. We will use the COVID19 data available at (https://github.com/nytimes/covid-19-data) and combine it with information from Census and other sources to visualize spatial patterns and make maps. Participants should be proficient in R and vector data GIS.
URL:https://micde.umich.edu/event/map-making-in-r/
LOCATION:Your Desktop
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