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Here is a very simple recommendation to help you overcome this: use a higher-entropy validation process, such as k-fold validation, or even better, iterated k-fold validation with shuffling. Only check your results on the official validation set at the very end.
Yes, it's more expensive, but that cost itself is a regularization factor: it will force you to try fewer ideas instead of throwing spaghetti at the wall and seeing what sticks.
NEVER waste spaghetti.
Throwing science at the wall, not spaghetti ;) Dunno whom said it first, I've heard it in Portal (game) Or was it Portal II? Well, I post both Portals here, see what sticks :D
What do you think about this https://twitter.com/GoogleAI/status/1003722208672268288 … and other "autoML" efforts at google?
"oh! it looks like a Pollock"
That, Sir, is a mixed metaphor of alchemy @alirahimi0 . And why does Francois never engage in peer discussion w the likes of Ali or Ian? Granted, probably w Ian over beers on campus. @goodfellow_ianpic.twitter.com/qZpIRFq80C
Has anyone written about applying the idea of falsification to deep learning? If all ML models are doing is hypothesis generation, aren't most of the generated hypotheses false? Or: what is the underlying theory of deep learning? The world is made of data?
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