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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.
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.
sklearn
Posts with mentions or reviews of sklearn.
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning sklearn yet.
Tracking mentions began in Dec 2020.
neat
Posts with mentions or reviews of neat.
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning neat yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
When comparing sklearn and neat you can also consider the following projects:
GoLearn - Machine Learning for Go
tfgo - Tensorflow + Go, the gopher way
go-fann - Go bindings for FANN, library for artificial neural networks
libsvm - libsvm go version
godist - Probability distributions and associated methods in Go
Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
Goptuna - A hyperparameter optimization framework, inspired by Optuna.
gorse - Gorse open source recommender system engine
randomforest - Random Forest implementation in golang
Varis - Golang Neural Network