scikit-learn VS CNTK

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

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scikit-learn CNTK
24 0
48,142 17,123
0.8% 0.1%
9.9 0.0
1 day ago 3 months ago
Python C++
BSD 3-clause "New" or "Revised" License GNU General Public License v3.0 or later
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.

scikit-learn

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.

CNTK

Posts with mentions or reviews of CNTK. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning CNTK yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

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

Keras - Deep Learning for humans

Surprise - A Python scikit for building and analyzing recommender systems

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

tensorflow - An Open Source Machine Learning Framework for Everyone

gensim - Topic Modelling for Humans

PyBrain

TFLearn - Deep learning library featuring a higher-level API for TensorFlow.

MLflow - Open source platform for the machine learning lifecycle

seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)

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

H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.