onnx-go
onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library. (by owulveryck)
Gorgonia
Gorgonia is a library that helps facilitate machine learning in Go. (by gorgonia)
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| onnx-go | Gorgonia | |
|---|---|---|
| 3 | 21 | |
| 632 | 5,326 | |
| - | 0.8% | |
| 2.3 | 2.8 | |
| 6 months ago | 16 days 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.
onnx-go
Posts with mentions or reviews of onnx-go.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-23.
- Why isn’t Go used in AI/ML?
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Pre-trained object detection machine learning module for Golang? 🤔
You could always use something like https://github.com/owulveryck/onnx-go
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I need help deploying NLP model
If you can get a trained copy of the model in ONNX format, you can use https://github.com/owulveryck/onnx-go to work with it in Go
Gorgonia
Posts with mentions or reviews of Gorgonia.
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 en GO! 🤯
GitHub - gorgonia/gorgonia: Gorgonia is a library that helps facilitate machine learning in Go.
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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.
- Why isn’t Go used in AI/ML?
- GoLang AI/ML open source projects
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A systematic framework for technical documentation authoring
Perhaps it's a product of French culture, but because Gorgonia[0] has a number of French contributors, this was actually the way we structured our documentation.
But this is the first time I've heard of the name of the framework.
[0]: https://gorgonia.org
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[D] When was the last time you wrote a custom neural net?
Oh it's.Gorgonia
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Most Popular GoLang Frameworks
Website: https://gorgonia.org
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[D] What framework are you using?
I use Gorgonia.
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Why can't Go be popular for machine learning?
What you think about this https://github.com/gorgonia/gorgonia ? I also recall there is something else out there but can't find it at the moment...
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Neural networks in golang
Yep, all of them: https://github.com/gorgonia/gorgonia
What are some alternatives?
When comparing onnx-go and Gorgonia you can also consider the following projects:
GoLearn - Machine Learning for Go
NEUGO
tfgo - Tensorflow + Go, the gopher way
go-pr - Pattern recognition package in Go lang.
goml - On-line Machine Learning in Go (and so much more)
go-onnxruntime - Unofficial C binding for Onnxruntime in Golang.
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
goRecommend - Collaborative Filtering (CF) Algorithms in Go!
bayesian - Naive Bayesian Classification for Golang.
go-deep - Artificial Neural Network