gorse
libsvm
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| gorse | libsvm | |
|---|---|---|
| 8 | 2 | |
| 8,012 | 4,470 | |
| 1.1% | - | |
| 7.1 | 4.5 | |
| 22 days ago | 9 months ago | |
| Go | Java | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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.
gorse
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How to Use AVX512 in Golang
Thanks, @gnabgib. Your comment is very insightful and reminds me of my mentor correcting my academic paper. The post introduces the basic idea of using AVX512 in Go by writing C codes. There are mistakes and many details are omitted. A complete example is https://github.com/gorse-io/gorse/tree/master/base/floats
- [P] Gorse: An open-source recommender system service
libsvm
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libsvm++ : Rewritten libsvm with newer C++
The most famous library for Support Vector Machine (SVM) algorithm is libsvm (https://github.com/cjlin1/libsvm/), but I felt that its code style is too old, I rewrote in newer C++ as a hobby project.
What are some alternatives?
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
sklearn - bits of sklearn ported to Go #golang
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
GoLearn - Machine Learning for Go
go-cluster - k-modes and k-prototypes clustering algorithms implementation in Go
libsvm - libsvm go version
goml - On-line Machine Learning in Go (and so much more)
bayesian - Naive Bayesian Classification for Golang.
go-deep - Artificial Neural Network
CloudForest - Ensembles of decision trees in go/golang.
goscore - Go Scoring API for PMML