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2min chapter
061: Interpolation, Extrapolation and Linearisation (Prof. Yann LeCun, Dr. Randall Balestriero)
Machine Learning Street Talk (MLST)
How to Reduce the Training Loss
In practice, that's not treally what we see. Most of the information that we could think is irrelevant for the task is actually kept because it can help in reducing training ros. A regularisaton aso makan rams at exactly. Very interesting that en seing. That's one reason why parametrisation by n is sometimes useful. Because if you know priory which things are useless for your task, you can by end remove them from the data that you fit to your dipnet or whatever model.
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