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UID:pretalx-juliacon2023-B3GMAE@pretalx.com
DTSTART;TZID=EST:20230726T100000
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DESCRIPTION:We introduce Ignite.jl\, a package that streamlines neural netw
 ork training and validation by replacing traditional for/while loops and c
 allback functions with a powerful and flexible event-based pipeline. The k
 ey feature of Ignite.jl is the separation of the training step from the tr
 aining loop\, which gives users the flexibility to easily incorporate even
 ts such as artifact saving\, metric logging\, and model validation into th
 eir training pipelines without having to modify existing code.
DTSTAMP:20260606T134155Z
LOCATION:Online talks and posters
SUMMARY:Ignite.jl: A brighter way to train neural networks - Jonathan Douce
 tte
URL:https://pretalx.com/juliacon2023/talk/B3GMAE/
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