Nim has a popular ML library "inspired by Numpy and PyTorch": https://github.com/mratsim/Arraymancer https://github.com/mratsim/Arraymancer One of the advantag

Nim has a popular ML library "inspired by Numpy and PyTorch": https://github.com/mratsim/Arraymancer https://github.com/mratsim/Arraymancer One of the advantages of the language for DS/ML is the native "C like" performance with very low developer friction. Another advantage from a library perspective is being able to automate fast, low level boiler plate code from easy to use DSLs using AST macros. For example a DSL could generate bespoke code for different data layouts and pipelines etc., giving you the best possible performance without the user worrying about it.