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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
by alexmlamb2 10y ago
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.
https://nips.cc/Conferences/2016/AcceptedPapers 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.
- webmaven 10y agoHmm. even though "understand well enough to reproduce" != "fully appreciate", it seems like you're saying that being able to reproduce 10% of the papers is a reasonable benchmark for 7 years of experience. Is that what you meant?
- t3nary 10y agoHow difficult is it to get into a position at a top company where you can do ML research, without having a PhD? And I'm also wondering about going back to university: Is it difficult to get into a good ML PhD program if you've been in the industry for some years?
- achompas 10y agoVery difficult, at east for research. Top companies with ML research labs (FB, Google, Uber) are basically externalized academic labs. The heads of these labs are simply bringing talent over from their former departments (NYU, Cambridge/UBC, CMU). It is not impossible to work at these labs as an engineer without a PhD, but I don't have a deep understanding of these roles.
- deleted 10y ago[deleted]