As a data scientist, I feel that Nim has tremendous potential for data science, machine learning and deep learning.
In particular, it's currently non-trivial to bridge the gap between deep learning research (mostly Python and sometimes Lua) and production (C for embedded devices, javascript for web services ...). For the past 3 months I've been working on Arraymancer, a tensor library that currently provides a subset of Numpy functionality in a fast and ergonomic library. It features: * Creating tensors from nested sequences and arrays (even 10 level of nesting) * Pretty printing of up to 4D tensors (would need help to generalize) * Slicing with Nim syntax * Slices can be mutated * Reshaping, broadcasting, concatenating tensors. Also permuting their dimensions. * Universal functions * Accelerated matrix and vector operations using BLAS * Iterators (on values, coordinates, axis) * Aggregate and statistics (sum, mean, and a generic aggregate higher order function) Next steps (in no particular order) include: * adding CUDA support using andrea's nimcuda package * adding Neural Network / Deep Learning functions * Improving the documentation and adding the library on Nimble The library: [https://github.com/mratsim/Arraymancer](https://github.com/mratsim/Arraymancer) I welcome your feedback or expected use case. I especially would love to know the pain points people have with deep learning and putting deep learning models in production.
