I'm not averse to C++11 - I personally think it's a great language for certain tasks, and have used it extensively for performance-critical ML - see [1] for som

I'm not averse to C++11 - I personally think it's a great language for certain tasks, and have used it extensively for performance-critical ML - see [1] for some public work. The value proposition of Julia (and Python with Cython) is that you don't have to choose between the productivity you have from a dynamic language (IPython/IJulia [2] are incredibly productive environments for ML/data science IMO), and you have the ability to easily optimize hotspots in your code as you come across them - whether that be adding type annotations/disabling bounds checking in Julia, dropping into Cython, etc. [1]: http://tullo.ch/articles/decision-tree-evaluation/ http://tullo.ch/articles/decision-tree-evaluation/ [2]: http://bit.ly/1cOTzJY http://bit.ly/1cOTzJY