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> tldr; The problem people are having is figuring out input and output for domains other than image processing. This and some of your other comments in this th
by orthoganol 9y ago
> tldr; The problem people are having is figuring out input and output for domains other than image processing.
This and some of your other comments in this thread lead me to believe you are unaware of how DL is being used in NLP. I would argue real industry use of DL for NLP dwarfs that of DL for images. Personal assistants, search, customer service, sales, chat are the big general cases, let alone company-specific tasks (.e.g. financial data, reviews) or other NLP domains that are not particularly industry-adopted (.e.g. translation, summarization). There's a lot of demand for NLP DL experts, and it's no surprise this year that Socher's NLP DL class had 700 Stanford students enrolled.
- sillysaurus3 9y agoOkay, but you cherry-picked a quote and ignored the central argument, which is that this website doesn't address how to map your problem to the domain of deep learning. Nobody cares how many students the DL class at Stanford had enrolled. Your comment is kind of trying to defend deep learning (when it needs no defense) while putting down the parent without actually providing any meaningful assistance.
- white-flame 9y agoI think that's exactly what he's pointing at: People in general are unaware as to how to map such datasets into NNs. You could help by giving an example of some common application strategies from NLP, instead of just saying that it's done.