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> looking at the source, though - I'm not sure I want to go there - [Scheme] looks like a mashup of Pascal, Lisp, and RPN

Cool, you got to discover Scheme today! It's one of the classical languages that defines the programming world we live in.

> data science and deep learning in general has pretty much "blessed" Python and C++ as the languages for such tasks.

It's reasonable to expect that the languages a community uses for its programs bear some proportionality to the broader programming community unless you have a very severe historical isolation of that community (i.e., MUMPS in medical informatics). Python and C++ are extremely common languages. You should expect the usual long tail of other languages as well. And under the hood, it's really all about CUDA anyway.




> Cool, you got to discover Scheme today!

There's nothing wrong with implementing the tool in Scheme, but the problem is that typical ML frameworks implemented in Python use Python as their "glue" language (which already can be somewhat problematic performance wise). This approach is using a text serialization and sh as the glue language.

Sure, it's conceptually neat, but for exploration, it's not even competitive with regular Python, let alone e.g. Python in a Jupyter notebook.

I could see an approach using scheme itself as the exploratory glue language being quite competitive. Dropping down into shell pipelines is decidedly worse.


I think the pipelines are absurd, too (actually, I think the entire Unix shell is a Rube Goldberg contraption that we would be better off without), but that's not the focus of the comment I was replying to.




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