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I'm concerned deep networks are being overhyped. They're certainly exciting, but they haven't seen much use in industry yet; it's too early to make claims about
by rm999 14y ago
I'm concerned deep networks are being overhyped. They're certainly exciting, but they haven't seen much use in industry yet; it's too early to make claims about how they have impacted data science.
Also, data science involves a lot more than building predictive models. In my experience >95% of effort goes into something other than building a model. In kaggle contests you usually concentrate on that <5%, which IMO is the fun part but it's not the reality of industry. There are many big breakthroughs in data science that don't involve model building.
edit: I haven't listened to the podcast yet (at work), my comment is more about the title.
- lrei 14y agoI'm genuinely curious (not being snarky or wtv): what do you put 95% of your effort into?
- mshron 14y agoNot the OP but: * Problem definition * Infrastructure * Data transformation * Exploratory analysis (arguably part of model work) * Results presentation Then again, this is an ongoing disagreement I have with the Kaggle folks over what constitutes "data science," where I'm pretty confident that "applied machine learning" is a better explanation of what their contests are about.
- rm999 14y agoI kind of got them to say it here: https://news.ycombinator.com/item?id=4655927 https://news.ycombinator.com/item?id=4655927 BTW, I'm a big fan of the data analysis that came out of okcupid, is that all your work?
- lrei 14y agoI see. Thanks. I'd say data transformation is a part of feature engineering (commonly the bulk of the effort in a ML application). And exploratory analysis is part of model work. W/o those 2 one would be building a model out of dreams and wishes. Data Science is probably a poorly chosen description. I'd say common use includes infrastructure work which for most of us consists in engineering work.
- mtrimpe 14y agoI think this new speech recognition improvement by Microsoft depends on deep networks and it's results are very impressive. http://research.microsoft.com/en-us/news/features/speechrecognition-082911.aspx http://research.microsoft.com/en-us/news/features/speechreco... http://research.microsoft.com/en-us/projects/mavis/ http://research.microsoft.com/en-us/projects/mavis/ That might just be a single application but if it extends into other domains it might end up being very valuable indeed. It will eventually become just another tool of course just like anything else but if it brings 10-20% improvements in even a few other long-stagnant areas I would agree with saying that it is a big deal.