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I'm new to deep learning. I was wondering what's the relationship between a deep model complexity (e.g. total number of parameters, or depth) and the dataset size?

Assuming I want to do a binary classification with 10K data for a problem like fire detection. How should I know what complexity I should go for?

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    $\begingroup$ In general, this is may not be an easy question to answer. The branch of artificial intelligence that studies these issues is called computational (and statistical) learning theory. See also cs.stackexchange.com/q/75327/20691. I will eventually try to answer this question, but I don't know when. $\endgroup$ – nbro Dec 11 '19 at 23:02
  • $\begingroup$ Thanks. Yeah try to write something so i can give u some credit. $\endgroup$ – Tina J Dec 12 '19 at 3:23

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