Gorgonia
nn
| Gorgonia | nn | |
|---|---|---|
| 21 | 26 | |
| 5,333 | 48,004 | |
| 0.5% | 3.7% | |
| 2.8 | 7.7 | |
| 29 days ago | about 1 month ago | |
| Go | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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Gorgonia
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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
nn
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Can't remember name of website that has explanations side-by-side with code
Hey are you talking about https://nn.labml.ai/ ?
- [D] Recent ML papers to implement from scratch
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[P] GPT-NeoX inference with LLM.int8() on 24GB GPU
Implementation & LM Eval Harness Results
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[P] Fine-tuned the GPT-Neox Model to Generate Quotes
Github: https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/neox
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Best resources to learn recent transformer papers and stay updated [D]
Regarding implementations this helps me: https://nn.labml.ai/
- Introductory papers to implement
- How to convert research papers to code?
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[D] How to convert papers to code?
Dunno if this is directly helpful, but this website has implementation with the math side by side https://nn.labml.ai/
- [D] Looking for open source projects to contribute
- Resource for papers explanation
What are some alternatives?
onnx-go - onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library.
GFPGAN-for-Video-SR - A colab notebook for video super resolution using GFPGAN
GoLearn - Machine Learning for Go
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
tfgo - Tensorflow + Go, the gopher way
functorch - functorch is JAX-like composable function transforms for PyTorch.
goml - On-line Machine Learning in Go (and so much more)
ZoeDepth - Metric depth estimation from a single image
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
onnx-simplifier - Simplify your onnx model
bayesian - Naive Bayesian Classification for Golang.
Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.