As far as I know the only gold rush is around marketing surrounding this and related buzzwords, i.e., it's the latest thing that your business absolutely must be doing to keep up with your competitors. That particular usage of course has as little to do with actual deep learning as the misappropriation of big data has to do with anything.
Fortunately this trend has been a bit slower to take off, presumably because whereas big data is a fairly nebulous concept, it's much easier to correct someone when they talk about deep learning quite wrongly.
I think at the end of the day, the stuff talked about in the media may or may not have some merit (otherwise why would google or these other companies put resources in to it?)
Rather than read the blogspam, read the papers instead though. Try to understand the merits of what's going on and apply it to your use case.
Blogspam to me is just something that talks about the stuff at such a high level, there's no meat in it. Within that subset I'm talking about academic concepts where you can learn a lot more if you just read the papers.
When the press talks about an academic paper, they tend not to add much value in terms of actually explaining what's going on. I think people drawing their own conclusions from the original works is the better way to go. Obviously not everyone will agree with me here, but so be it.
- students of Hinton/LeCun/Bengio/Ng, and/or
- members of Google/Facebook/Baidu's deep learning group.
An actual deep learning expert can have unrivalled access to infrastructure, excellent colleagues, data, and compensation, at any of Google/Facebook/Baidu etc.
Given this backdrop, it's less surprising that a significant number of the current crop of deep learning companies are unimpressive technologically - those with actual expertise have much better opportunities.
Do you feel that talk is overly optimistic/misleading? Or just that you don't think there's that much money to be made with it?
... and that human error rate is also > 0. Sometimes machine learning bests the humans.
It's not another machine learning algorithm, it's a feature learning algorithm. You can feed raw pixels into an SVM, but you aren't taking advantage of the structure of the data at all.
UPDATE: My commentary may have conflated "data analytics" with "deep learning". I probably should disentangle them.
That would be a big deal IMHO.
Deep learning is not any sort of magic bullet. It may be marginally better than other machine learning methods in specific contexts, but I'm not convinced that there are going to be any deep learning tycoons or deep learning entrepreneurs (were there any SVM tycoons?). But I suppose as a buzz term "deep learning" is better than the meaningless "big data". Just replace the latter with the former in the marketing literature.