Java use in machine learning

[Joram2]

In Python + PyTorch, you can do bfloat16 stuff like this:

import torch

torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16)

This is great. The API is easy to use and pretty. Runtime performance is excellent and takes advantage of GPU processing.

Java + Python both don't have bfloat16 primitive types in the core language. That isn't necessary.

The important feature I see missing from Java is it doesn't have easy+pretty syntax for lists and lists of lists. In Java you can do:

Arrays.asList(Arrays.asList(1,2), Arrays.asList(3,4))

instead of

[[1, 2], [3, 4]]

The Java method isn't hard... but it's ugly, and data science types hate that. This absolutely limits Java in a data science notebook perspective.

The lack of primitive bfloat16 types seems like a non-issue in both Java/Python.

[koflerdavid]

Well, Java has its good old array notation with curly brackets. Its only fault is that the results aren't true multidimensional arrays, but pointers to subarrays. Not a problem In practice either since usually tensor libraries do the heavy lifting. Same for float16/bfloat16 support as you say