[pilooch]

Sure, I started as a researcher 14 years ago, then drifted to what I thought was a sweet spot then, half-research / half-programmer. I say 'sweet spot' because many applications did require both the academic and the applied background at the time, so for the sake of thrilling applications, it was worth 'downgrading' to pure engineering work when needed. Now I believe the game has changed a bit, coursera and others are infusing the minds of engineers with highly technical knowledge far more rapidly than before.

Typically I am astonished at the number of implementations of deep learning techniques (Shark does include some, AFAIK).

My past experience is that I had write many AI algorithms myself because I could not find any suitable, free and/or open implementations (or other researchers would not share theirs ;) ).

[pmelendez]

Thanks! That's very inspiring :) I worked in a similar position for a short time long time ago but after that I hadn't been able to find something similar. Good thing to see that people can find those sweet spots! :D

It's very true what you said about all the implementations available now. Although, for most algorithms I tend to try to implement them myself as a learning experience, maybe the biggest exception is standard SVM since it is kinda tricky but even for that there are some online algorithms that are easy to implement.

Thanks for sharing your experience!