
Ask HN: When to not use deep learning? - khiner
Given the &quot;unreasonable effectiveness&quot; of recurrent neural networks, and the increasing ease of their application to a given learning problem, I&#x27;m often left with the following uneasy feeling when reading through academic papers covering more specialized approaches: &quot;Hmm, this was written in 200X... am I wasting my time reading this because deep learning could blow this technique out of the water?&quot;<p>I want to develop my &quot;smell&quot; for judging whether reading through a paper for a non-RNN model is a good use of my time.  Does anyone have any heuristics, red flags, smells, entire domains, etc., that they use to put things immediately in the category of &quot;nah, this is smoked by deep learning techniques&quot;?
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eb0la
Never use deep learning if you have to explain how this works to business
people.

Neural networks work "like magic". If you're using something that can't be
modeled with a formula in excel, or as a tree, then it is not businessplan-
ready.

Probably this might be different for a native-digital company; but for
traditional companies this is the norm.

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minimaxir
You can't use deep learning in cases where normal statistical modeling is
invalid, like multicolinearity among features. (Of course, some deep learning
methods can get around that!)

