Optimized C/C++ will usually beat out Go code, while Go will usually far outstrip the cpython interpreter[1]. Go itself doesn't really have any optimization for vector processing using SIMD that would make it a "fast" language for doing things like machine learning, machine vision, etc.; hand-coded C/C++ is always going to beat out any other language here. If you're talking python vs. go though, Go will win hands down.

To be honest my personal viewpoint is that we're better off reducing our dependence on SIMD CPU instructions and using GPUs for this sort of highly-parallel processing instead. Most Processors sold these days come with a GPU built-in, so why not make use of these SIMD units, rather than duplicating them on the CPU? This is just my opinion though, and is sort of irrelevant to the question.

Go has an excellent interface to C (cgo) built in, and SWIG can also be used to wrap C/C++ libraries so they are usable in Go. If your aim is core-for-core speed, then your best option is probably to take some highly optimised C/C++ library and create a wrapper that allows you to access it from Go. This route would be at least as performant as any python implementation using the same technique, if not much faster. If your goal is having a highly-concurrent and safe implementation, it is better to implement such libraries from scratch in Go; this is the method most Go libraries use.

Just as with benchmarks, I don't think anyone can give you a non-subjective answer on whether Go will be faster (so take mine with a grain of salt).

[1] http://shootout.alioth.debian.org/u64q/benchmark.php?test=al...