Coconut VS thinc

Compare Coconut vs thinc and see what are their differences.

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Coconut thinc
27 4
3,941 2,790
- 0.6%
9.4 6.9
3 days ago 10 days ago
Python Python
Apache License 2.0 MIT License
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.

Coconut

Posts with mentions or reviews of Coconut. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-10-19.

thinc

Posts with mentions or reviews of thinc. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-28.

What are some alternatives?

When comparing Coconut and thinc you can also consider the following projects:

Toolz - A functional standard library for Python.

quantulum3 - Library for unit extraction - fork of quantulum for python3

fn.py - Functional programming in Python: implementation of missing features to enjoy FP

jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Pyrsistent - Persistent/Immutable/Functional data structures for Python

horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

funcy - A fancy and practical functional tools

extending-jax - Extending JAX with custom C++ and CUDA code

returns - Make your functions return something meaningful, typed, and safe!

dm-haiku - JAX-based neural network library

effect - effect isolation in Python, to facilitate more purely functional code

AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.