This is very, very cool. Having come from the functional programming world, I frequently miss these features when doing machine learning in Python, and haven’t been able to easily replicate them. I think there’s a lot of easy optimization that could happen in day-to-day exploratory machine learning code that bog standard pandas/scikit-learn doesn’t do.
This is encouraging to hear. When I talk about this stuff to ML engineers, some instantly get it, especially when they come from a functional programming background. Others don’t and it feels like there’s a wall between me and them.
I think I can replicate a lot of this in Python, even if it’s a little clunky. It’s just easier to start in Hy and then write a wrapper to port it to Python.
This is very, very cool. Having come from the functional programming world, I frequently miss these features when doing machine learning in Python, and haven’t been able to easily replicate them. I think there’s a lot of easy optimization that could happen in day-to-day exploratory machine learning code that bog standard pandas/scikit-learn doesn’t do.
This is encouraging to hear. When I talk about this stuff to ML engineers, some instantly get it, especially when they come from a functional programming background. Others don’t and it feels like there’s a wall between me and them.
I think I can replicate a lot of this in Python, even if it’s a little clunky. It’s just easier to start in Hy and then write a wrapper to port it to Python.