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It’s a tradeoff allowing you to fit a larger model into a fixed RAM budget (ie the size of your GPU). Whether this is a good tradeoff is model- and data-specific, but anecdotally I’ve had good luck with it and usually use half precision to good effect (NLP, mostly).


Yes, you can fix (or freeze) some of the weights during the training of a neural network. In fact, this is done in the most common form of transfer learning (which is described here). I don't know exactly how this affects learning in general. In transfer learning, this is definitely beneficial, as we are freezing the weights that are associated with the ...

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