MS won't be so fruitful unless you do some research and publish (which is difficult and also many university don't support giving research work to MS student).
Its better to go for Ph.D.
Remember for going into openai or google brain you need to be among top even after Ph.D.
I'll note that my MS was hugely useful and didn't result in publications directly. The mileage of your MS or PhD is dependent on many factors.
OpenAI and Google Brain, like most other more research driven deep learning institutions, are more interested in the results you can produce rather than the accreditation you hold. Publications obviously count but well used or written deep learning projects / packages would too. Many PhDs who come out having spent many years in academia still wouldn't get an offer from these places and many of the talented people I know in these places don't have a PhD either.
To the parent of this post, I'd also look into what I'd refer to as "Masters in industry" i.e. Google Brain Residency[1] and other similar opportunities. From their page, "The residency program is similar to spending a year in a Master's or Ph.D. program in deep learning. Residents are expected to read papers, work on research projects, and publish their work in top tier venues. By the end of the program, residents are expected to gain significant research experience in deep learning.". This is likely an even more direct path than most institutions would provide. Though obviously the competition is fierce, many of my friends who participated in this ended up with a paper in a top tier conference by the end of the.
The best way to get the attention of those companies is to do peer-reviewed, published research in ML. Which is certainly possible while getting a Master's at one of those universities.
If you've no experience with ML stuff, you might want to start with Andrew Ng's course, which has a small bit on neural networks (MLP and backpropagation), with examples in Matlab/Octave. I found it useful, along with "Make Your Own Neural Network" by Tariq Rashid. Very good intro to coding MLPs directly in Python.
Ng's course would be a great place to start, imho - though I am a bit biased: I started out my journey by taking the ML Class in 2011. Lot's of "concretely's" strewn about!
Anyhow, it was a great introduction, and light on the calc (but more emphasis on probability and linear algebra). If you have matlab or octave experience, it will also help (I didn't - the revelation of having a vector primitive was wonderful once I got the swing of it, though).
Note again, though, that I took the ML Class - not the Coursera version; I have heard that they are identical, but it has been 5+ years since I took it, too.
Strongly agree. Andrew's course is a great choice to take before this one, if you have the time. It's not a prerequisite however.
The Udacity course has a very different aim - it covers much less territory and takes much less time. If you're just wanting to get a taste of deep learning, it's a very good option, but it's not a great platform to build longer term capability on IMHO.
