tfgo
Tensorflow + Go, the gopher way (by galeone)
CloudForest
Ensembles of decision trees in go/golang. (by ryanbressler)
Our great sponsors
| tfgo | CloudForest | |
|---|---|---|
| 6 | 4 | |
| 2,378 | 735 | |
| - | - | |
| 1.5 | 0.0 | |
| about 1 month ago | about 2 years ago | |
| Go | Go | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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.
tfgo
Posts with mentions or reviews of tfgo.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-27.
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Show HN: Carton – Run any ML model from any programming language
eh, awesome! Seems this one, right? https://github.com/galeone/tfgo. Quite many stars.
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Introducing GoFaceRec: A Go-based Face Recognition Tool Using Deep Learning
I'm excited to share a project I've been working on: [GoFaceRec](https://github.com/modanesh/GoFaceRec). This is a face recognition tool built in Go, leveraging the power of MTCNN for face detection and QMagFace for face recognition. The project was born out of a desire to bring the power of deep learning models to the Go community. After much effort, I concluded that the best approach was to convert models to TensorFlow and then work with tfgo, a Go binding to TensorFlow's C API. In GoFaceRec, the input image is first processed, and then its embeddings are compared against the ones already computed from our dataset. If the distance between embeddings falls below a specific threshold, then the face is considered as unknown. Otherwise, the proper label will be printed. The project is tested using Go 1.17 on Ubuntu 20.04. For gocv, the version of OpenCV installed is 4.7. And for tfgo, I installed [this version](https://github.com/galeone/tfgo) instead of the official one. You can install this package by running the following command in your project: > go get github.com/modanesh/[email protected] You can find more detailed instructions on how to use the tool in the [GitHub repository](https://github.com/modanesh/GoFaceRec). I welcome any feedback, suggestions, or contributions to the project. I'm looking forward to seeing how the community uses GoFaceRec and hope it can be a valuable tool for those working on face recognition tasks. Happy coding! 🚀
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Why can't Go be popular for machine learning?
Paolo Galeone has improved bindings (tfgo) that can be used for training and deployment.
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How to train a model for object detection in Golang?
https://github.com/galeone/tfgo here is a very good tutorial. I would suggest starting there.
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What libraries from other languages do you wish were ported over into go?
Tensorflow is actually written in C++, and the python package is just bindings to tensorflow. There are Tensorflow Go bindings: https://github.com/galeone/tfgo.
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Using Time series to make predictions
have you tried your hands at [galeone/tfgo](https://github.com/galeone/tfgo); I've just hello-world it... so can't vouch on efficiency
CloudForest
Posts with mentions or reviews of CloudForest.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-12.
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Trinary Decision Trees for missing value handling
I implemented something like this in a [pre xgboost boosting framework](https://github.com/ryanbressler/CloudForest) ~10 years ago and it worked well.
It isn't even that much of a speed hit using the classical sorting CART implementation. However xgboost and ligthgbm use histogram based approximate sorting which might be harder to adapt in a performant way. And certainly the code will be a lot messier.
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Future of Golang
Personally, my Go-to ML tool for tabular data is here: https://github.com/ryanbressler/CloudForest
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[D] Best methods for imbalanced multi-class classification with high dimensional, sparse predictors
The best method i've seen for dealing with this bias is to create "artificial contrasts" by including possibly many permutated copies of each feature and then doing a statistical test of the random forest importance values for each feature vs its shuffled contrasts. This method is described here: https://www.jmlr.org/papers/volume10/tuv09a/tuv09a.pdf and there is an implementation here: https://github.com/ryanbressler/CloudForest
What are some alternatives?
When comparing tfgo and CloudForest you can also consider the following projects:
Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
libsvm - libsvm go version
GoLearn - Machine Learning for Go
gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
neat
gobrain - Neural Networks written in go
go-deep - Artificial Neural Network
go-galib - Genetic Algorithms library written in Go / golang
shield - Bayesian text classifier with flexible tokenizers and storage backends for Go
Varis - Golang Neural Network
goga - Golang Genetic Algorithm