I don't see the point of writing a machine learning package in Go.

Go is designed for system programming, which is the opposite of scripting. In scientific computing, interactivity is an important feature. Softwares like MATLAB and IPython(now Jupyter) become popular because of this. Go doesn't have a nice REPL like IPython, and it never will. Besides, Go doesn't allow operator overload, which leads to verbose programs. Compare `c = a @ b` with `c := Must(Mul(a, b))`, the former Python statement is much cleaner than the Go one.

Usually, researchers use MATLAB, R, and Python for prototyping and training. The final model will be put into production by using something like TensorFlow Serving or by hand written C++ for performance. Even if the final product uses Go for serving, the Go program mostly runs the model via cgo or RPC.

Write in C/C++, and wrap the library in Python and Go is a much saner option. TensorFlow and MXNet are both good examples.

The problem I see with it is not so much that you don't want a native, compiled implementation of a machine learning as that you want to be able to call it from Python, MatLAB and R. It is somewhat less straightforward to go Python<->Go than Python<->C or even Python<->Rust.

it automatically creates a CPython-2 extension module out of a Go-1.5 package.

I plan to update it for Go>=1.6 and also directly generate a "cffi" python module so CPython-{2,3} and PyPy can be directly supported out of the box.