Gorgonia
mapstructure
Gorgonia | mapstructure | |
---|---|---|
21 | 16 | |
5,350 | 7,677 | |
0.9% | - | |
2.5 | 0.0 | |
about 1 month ago | about 1 month ago | |
Go | Go | |
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
mapstructure
- How do I marshal a JSON array into a map?
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Is there any equivalent to pydantic, serde, etc?
Maybe https://github.com/mitchellh/mapstructure can do what you want? It has some options for Remainder Values and Omit Empty
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Struggling to get JSON response data into usable struct
I've tried using mapstructure to then marshal the map fields into a struct which mostly works (it struggles with times and custom time types which requires a workaround for each case), but this doesn't feel very idiomatic and requires two passes at marshaling.
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Return unstructed db rows to struct
Although some orders may have more records maybe a superset can be indentified that you can actually create a struct of it and after gathereing first all values into a map then convert it to a struct maybe using a library like https://github.com/mitchellh/mapstructure . this way you can at least isolate the non structured data only on the data extraction part and the rest of your application can work with well formed structs.
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Trying to print JSON data from a file
Alternatively, you could try https://github.com/mitchellh/mapstructure if you don't know what your incoming structure is
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How to ensure required fields in struct consistently?
I'm doing it by validating a map[string]any first then putting it into a structure using mapstructure. It covers most use-cases and offers the most flexibility, at the expense of a bit of performance.
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Question about Unmarshalling
That said, it is possible to do this with JSON using something like https://github.com/tidwall/gjson or if you are fine with the switch statement but don't want to marshal and unmarshal again: https://github.com/mitchellh/mapstructure
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What type of software do you write at your workplace?
https://github.com/mitchellh/mapstructure because we have JSON documents which contain rugged arrays ;-)
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Help with mapstructure.Decode()
I've been using mapstructure.Decode to great effect, but currently can't figure out why a given mapping doesn't work. I'd appreciate it if someone could point out wtf I'm doing wrong or at least in the right direction:
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map[string]interface{} decoder
What do you mean by "decode"? I've used https://github.com/mitchellh/mapstructure but that doesn't quite look like what you're doing.
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.
jsoniter - A high-performance 100% compatible drop-in replacement of "encoding/json"
GoLearn - Machine Learning for Go
viper - Go configuration with fangs
tfgo - Tensorflow + Go, the gopher way
goprotobuf - Go support for Google's protocol buffers
goml - On-line Machine Learning in Go (and so much more)
gogoprotobuf - [Deprecated] Protocol Buffers for Go with Gadgets
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
structomap - Easily and dynamically generate maps from Go static structures
bayesian - Naive Bayesian Classification for Golang.
go-capnproto - Cap'n Proto library and parser for go. This is go-capnproto-1.0, and does not have rpc. See https://github.com/zombiezen/go-capnproto2 for 2.0 which has rpc and capabilities.