[Palomides]

spoiler: "Python is still the leader, but C++ is now second, then Java, and C at fourth place. R is only at the fifth rank."

[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.

[sndean]

That's the ranking when they queried for deep learning specifically.

For machine learning it was: Python, Java, R, C++, C. I have to wonder if that difference in ordering is actually real.

[staticassertion]

TensorFlow is written in C++ so I'd expect it to impact the deep learning result.

[vonnik]

Every serious lib was written in C/C++ under the hood long before TensorFlow came along.

[staticassertion]

True, at least to some extent. But I think TensorFlow treated C++ as a first class citizen and not as a tool to improve performance.

You wouldn't use sklearn as a C++ developer, for example. But you could totally use TensorFlow.

[throw20161123]

I'm guessing Python for research and prototyping with C++ for production-izing and scaling.

[gtaylor]

Some of these Python libs call out to C for heavy lifting, which can be very performant.

[fooker]

You lose quite a bit of optimization opportunities along the FFI boundaries.