If I wanted to get into research ML, I'd pick Julia no doubt. It allows both conventional ML techniques where we throw tons of parameters at the problem, but ad

If I wanted to get into research ML, I'd pick Julia no doubt. It allows both conventional ML techniques where we throw tons of parameters at the problem, but additionally a more nimble style where we can train over ordinary functions. Combine that with all the cutting edge applied math packages often being automatically compatible with the autodiff and GPU array backends, even if the library authors didn't think about that... it's a recipe for a lot of interesting possibilities.