[vonnik]

It's important to distinguish between the API language and the core language used for large-scale computation. Every single deep learning library exhibits that division. The API language(s) indicate the communities the lib seeks to serve. The core languages are lower-level, faster, and help optimize on the hardware. The core languages are always C/C++/Cuda C. The API languages tend to be Python, Java, Scala, R. Conflating the API languages with the computing cores is comparing apples to oranges. http://imgur.com/a/Z6fGr

[Meditato]

The higher level languages are also used to munge the data into lib-readable format. Python and R's standard libs are especially good at putting out various tabular and database formats in preparation for ML input

[jfpuget]

You're absolutely right, and the data doesn't allow to split between api vs core implementation.