Deep Learning Business Models
npbay.es
Deep Learning Business Models
1–10 of 36 posts
Re: Deep Learning Business Models
#2WTF is 'the deep learning gold rush'? (Also, the HN title does not match the article title, which is: "Deep Learning Business Models".)
Would love a better explanation from someone than this article gives to support the notion of some kind of massive 'Deep Learning' market that is yet untapped.
Re: Deep Learning Business Models
#3"Deep learning requires a ton of tuning and tweaking, and getting good results is as much art as science."
Funny when you think about it : Deep learning is supposed to avoid that ^^'
Re: Deep Learning Business Models
#4I didn't know there was a deep learning gold rush. Maybe this explains the crazy number of stars on my libdeep library on Github, while there being no comments or issues raised.
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.
Re: Deep Learning Business Models
#5> Deep learning requires very large training sets, and these shouldn’t be shipped around a lot.
To the extent that this is true, companies that offer these services may be driven to integrate more closely with customer data. This may involve custom in-house deployments or ways of getting the data in a cost-effective way from, say, Amazon S3, or wherever the data lives (HDFS, etc).
This leads me to speculate on an additional business model, "Behind The Firewall" Software Deployment. This could be somewhat different from the others suggested in the article: 1. Sell hardware; 2. Open source plus services; 3. Hosted API, “Deep Learning as a Service”; 4. Individual deep learning services.
Re: Deep Learning Business Models
#6For many (most?) analytics tools, the application developer has to specify what gets recorded and what gets fed as an input to predictive analytics. I expect to see more tools that assume any data is fair game; e.g. all data is worth at least a quick look. These tools will probably figure out how to select what is interesting enough to examine more deeply.
Re: Deep Learning Business Models
#7Agreed, was unaware that there was a gold rush, anyone know about specific companies in this space that are still active?
Re: Deep Learning Business Models
#8[deleted]
Re: Deep Learning Business Models
#9WTF is 'the deep learning gold rush'? (Also, the HN title does not match the article title, which is: "Deep Learning Business Models".) Would love a better explanation from someone than this article gives to support the notion of some kind of massive 'Deep Learning' market that is yet untapped.
> the HN title does not match the article title
Right. We changed it. The submitted title was "Business models in the deep learning gold rush".
Re: Deep Learning Business Models
#10I didn't know there was a deep learning gold rush. Maybe this explains the crazy number of stars on my libdeep library on Github, while there being no comments or issues raised. 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 (we…
I've been noticing a slight trend among the same group of people that are likely to use the big data buzzword without really understanding it, to also use deep learning in the same way, to simply mean "gain business insights from [obviously big] data".
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.