scikit-learn VS tensorflow

Compare scikit-learn vs tensorflow and see what are their differences.

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scikit-learn tensorflow
24 100
48,142 160,967
0.8% 0.5%
9.9 10.0
3 days ago 5 days ago
Python C++
BSD 3-clause "New" or "Revised" License 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.


Posts with mentions or reviews of scikit-learn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-13.


Posts with mentions or reviews of tensorflow. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-15.

What are some alternatives?

When comparing scikit-learn and tensorflow you can also consider the following projects:

PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)

Keras - Deep Learning for humans

Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Surprise - A Python scikit for building and analyzing recommender systems


xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

gensim - Topic Modelling for Humans

LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration