[algaexe]

As much as I like the phrase "buzzword-powered horseshit," there are always opportunities to apply new technologies in a creative way. In this case, it seems obvious to me that NNs will do a good job in identifying latent features that would otherwise slip past a linear model.

[_coveredInBees]

Sure, NNs "could" do that...but they are far more likely to just memorize a bunch of stuff when someone who isn't well versed with ML just applies an out of the box solution to a problem, looks at a simplified cross-validation number (I hope) and assumes that NN is better than some simpler model.

If the OP had actually posted a blog talking through things he did and how the NN helped and why he believes it is actually learning something useful and not just learning the noise, then this would certainly be interesting. But I've seen far too many people take powerful, flexible models with a large number of parameters and throw them at simple problems, with insufficient data and thought.

I'd normally give someone the benefit of the doubt...but taking the sum total of the quality of the linked web page, the complete lack of any meaningful information, as well as op's reply to my comment that added no real useful information regarding the actual benefit of throwing deep learning at this, I'm still inclined to stick to my original feelings regarding this post.