GoLearn
randomforest
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| GoLearn | randomforest | |
|---|---|---|
| 3 | 2 | |
| 8,908 | 30 | |
| - | - | |
| 0.0 | 0.0 | |
| 3 months ago | 11 months ago | |
| Go | Go | |
| MIT License | Apache License 2.0 |
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GoLearn
randomforest
-
Machine Learning
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?
Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
sklearn - bits of sklearn ported to Go #golang
goml - On-line Machine Learning in Go (and so much more)
neural-go - A multilayer perceptron network implemented in Go, with training via backpropagation.
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
regommend - Recommendation engine for Go
tfgo - Tensorflow + Go, the gopher way
onnx-go - onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library.
go-fann - Go bindings for FANN, library for artificial neural networks
bayesian - Naive Bayesian Classification for Golang.
golinear - liblinear bindings for Go
go-deep - Artificial Neural Network