I find NumPy way too complex for the relatively simple operations used in machine learning. The amount of implicit rules like broadcasting, silently truncating int64 => double, einsum complexities etc. is just mind boggling.
The result is a couple of dense lines but one cannot just read them without going into a deep analysis for each line.
It is a pity that this has been accepted as the standard for machine learning. Worse, now every package has its own variant of NumPy (e.g. "import jax.numpy as jnp" in the article), which is incompatible with the standard one:
I really would like a simpler array library that does stricter type checking, supports saner type specifications for composite types, does not broadcast automatically (except perhaps for matrix * scalar) and does one operation at a time. Casting should be explicit as well.
Bonus points if it isn't tied and inextricably linked to Python.
I think numpy closely maps to how I think so it’s not as hard to read these dense lines as it would be to read expanded versions. I think my point of view is shared by a lot of leading researchers and this is why it is used more heavily.
The kinds of type safety you want might be good for other use cases but for ML research they get in the way too much.
The result is a couple of dense lines but one cannot just read them without going into a deep analysis for each line.
It is a pity that this has been accepted as the standard for machine learning. Worse, now every package has its own variant of NumPy (e.g. "import jax.numpy as jnp" in the article), which is incompatible with the standard one:
https://jax.readthedocs.io/en/latest/jax.numpy.html
I really would like a simpler array library that does stricter type checking, supports saner type specifications for composite types, does not broadcast automatically (except perhaps for matrix * scalar) and does one operation at a time. Casting should be explicit as well.
Bonus points if it isn't tied and inextricably linked to Python.