My background is that I did my undergrad at JHU, then did applied Machine Learning research for two years at Amazon, and now I'm a PhD student in ML/DL. I was very into ML during undergrad, so overall I have ~7 years of experience.
My view is that Machine Learning is a deep field which could take decades to fully appreciate, but which is also easily accessible, especially if one is interested in building applications.
I think that a good litmus test for ML expertise is asking someone what % of all NIPS papers from last year they'd be able to understand well enough to reproduce after a quick reading.
My view is that Machine Learning is a deep field which could take decades to fully appreciate, but which is also easily accessible, especially if one is interested in building applications.
I think that a good litmus test for ML expertise is asking someone what % of all NIPS papers from last year they'd be able to understand well enough to reproduce after a quick reading.
https://nips.cc/Conferences/2016/AcceptedPapers
Even though I have ~7 years of experience doing ML, I would say that I could probably only fully appreciate ~10% of all NIPS papers.