neureka VS multik

I am using https://github.com/Gleethos/neureka to do some personal machine learning and also basic data science stuff at my workplace. It is inspired by PyTorch and it's dynamic autograd system (which records the computation graph to then traverse it for backpropagation eagerly). The library is super lightweight, has a nice API and documentation, but it's still very young and not as feature rich.

A lightweight option: Neureka - https://github.com/Gleethos/neureka

https://github.com/Gleethos/neureka A tensor/nd-array library inspired by Numpy and PyTorch. Supports:

If you are into deep learning: I work on an nd-array / tensor library with autograd support. https://github.com/Gleethos/neureka

If you need a simpler array syntax, have a look at the official multi dimensional array library for Kotlin: https://github.com/Kotlin/multik

for JVM https://github.com/Kotlin/multik

Multik (NumPy analogue) - Multidimensional array library for Kotlin.

To work with matrices, I'm using multik, a multidimensional array library for Kotlin. It has been publicly released recently, and you can read more about it on the Kotlin blog.

Can the benchmark add the recent https://github.com/Kotlin/multik And https://github.com/Kotlin/kotlin-numpy