by

Ciro Santilli (@cirosantilli, 34)

Training and inference

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.

Table of contents
- Inference (ML) Training and inference
- Training (ML) Training and inference

Inference (ML)

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Training (ML)

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