> Yes, and it's not so bad.
In the absence of alternatives its workable. Libraries like eigen or armadillo help a lot. But Python with numpy has been extremely popular for a good reason.
In the absence of alternatives its workable. Libraries like eigen or armadillo help a lot. But Python with numpy has been extremely popular for a good reason.
Yes, and it's not so bad. A lot of ML deployments have been based on C/C++ for inference anyway (with Python driving the training). So that's really nothing new. I.e. most Python research code is not deployable in terms of quality / performance.