GoLearn
Machine Learning for Go (by sjwhitworth)
randomforest
Random Forest implementation in golang (by malaschitz)
| GoLearn | randomforest | |
|---|---|---|
| 3 | 2 | |
| 9,373 | 53 | |
| 0.0% | - | |
| 0.0 | 2.6 | |
| over 1 year ago | over 1 year ago | |
| Go | Go | |
| MIT 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.
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.
GoLearn
Posts with mentions or reviews of GoLearn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-06.
- Machine Learning
- GoLang AI/ML open source projects
-
Multi-layer neural network from scratch in Go
If you're curious about a more robust machine learning library in Go, https://github.com/sjwhitworth/golearn is a quite feature-rich option.
randomforest
Posts with mentions or reviews of randomforest.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-06.
-
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.
- Boruta algorithm added to Random Forest library
What are some alternatives?
When comparing GoLearn and randomforest you can also consider the following projects:
Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
sklearn - bits of sklearn ported to Go #golang
goml - On-line Machine Learning in Go (and so much more)