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|MIT License||Apache License 2.0|
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8 projects | /r/golang | 6 Feb 2023
I did end up writing and using a custom library for Random Forest (it's also in AwesomGo) in one real-world project (detecting Alzheimer's and Parkinson's from speech from a mobile app) - https://github.com/malaschitz/randomForest I had better results than the team who used TensorFlow and most importantly I didn't have to use any other technology than Go. For NN's it's probably best to use https://gorgonia.org/ - but it's not exactly a user friendly library. But there is a whole book on it - Hands-On Deep Learning with Go.
What are some alternatives?
GoLearn - Machine Learning for Go
libsvm - libsvm go version
go-fann - Go bindings for FANN, library for artificial neural networks
Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
gorse - Gorse open source recommender system engine
go-cluster - k-modes and k-prototypes clustering algorithms implementation in Go
CloudForest - Ensembles of decision trees in go/golang.
goml - On-line Machine Learning in Go (and so much more)