kotlin-numpy VS multik

Compare kotlin-numpy vs multik and see what are their differences.

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kotlin-numpy multik
3 9
312 616
-0.3% 1.1%
0.0 8.3
almost 2 years ago 3 months ago
Kotlin Kotlin
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

kotlin-numpy

Posts with mentions or reviews of kotlin-numpy. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-12-19.

multik

Posts with mentions or reviews of multik. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-27.

What are some alternatives?

When comparing kotlin-numpy and multik you can also consider the following projects:

kotlindl - High-level Deep Learning Framework written in Kotlin and inspired by Keras

nd4j - Fast, Scientific and Numerical Computing for the JVM (NDArrays)

kotlin-python - Python target for the Kotlin Programming Language. See https://github.com/krzema12/kotlin-python/tree/python-backend/python

kotlin-jupyter - Kotlin kernel for Jupyter/IPython

kmath - Kotlin mathematics extensions library

Kotlin-Multiplatform-Libraries - Kotlin Multiplatform Libraries. Welcome PR if you find or create new Kotlin Multiplatform Library.

kotlin-flow-extensions - Extensions to the Kotlin Flow library.

rsocket-kotlin - RSocket Kotlin multi-platform implementation

KorGE - KorGE Game Engine. Multiplatform Kotlin Game Engine

kotlinx.serialization - Kotlin multiplatform / multi-format serialization

Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.