= Training and inference
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This is the first thing you have to know about <supervised learning>:
* training is when you learn model parameters from input. This literally means learning the best value we can for a bunch of number input numbers of the model. This can easily be on the hundreds of thousands.
* inference is when we take a trained model (i.e. with the parameters determined), and apply it to new inputs
Both of those already have <hardware acceleration> available as of the 2010s.
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