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Jeremy Howard: fast.ai Deep Learning Courses and Research
Lex Fridman Podcast
Multi Gpus
Andi: There's a constraint on the accuracy that your trained model is supposed to achieve. We realized if we actually just used 64 by 64 images, it trained a pretty good model. And then we could take that same model and just give it a couple of epochs to learn two 24 b two 24 images, and it was basically already trained. He says multi-gpu training in general has become less clunky but anything that slows down iteration speed is a waste of time. "Why test things on one point three million images? Most of us don't use one point 3 million images"
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