Synapses
m2cgen
Our great sponsors
| Synapses | m2cgen | |
|---|---|---|
| 1 | 8 | |
| 68 | 2,707 | |
| - | 0.6% | |
| 0.0 | 0.0 | |
| over 2 years ago | 6 months ago | |
| Elixir | Python | |
| MIT License | MIT License |
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Synapses
m2cgen
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How to use python ML script in tauri?
Check out: https://github.com/BayesWitnesses/m2cgen
- EleutherAI announces it has become a non-profit
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Redis as a Database — Data Migration With RedisOM, RedisGears and Redlock
Notice that I’m using random values to populate the Sentiment field. You might compute the values for your fields based on other fields or actually use an ML model to perform the transformation. E.g. you could make use of m2cgen to transform trained models to pure python code and load them in **RedisGears **to be executed in a *GearsBuilder *instance. Another option is to pull out the big guns and go straight to RedisAI.
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Why isn’t Go used in AI/ML?
I wish that it was more common for model outputs to be converted the way bayeswitness does with mc2gen https://github.com/BayesWitnesses/m2cgen
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Use your decision tree model in your Javascript project today with m2cgen
And that’s it! All the magic in just two lines of code. I would like to thank the authors of the m2cgen library and encourage you to try it out.
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We use Rust for an opensource malware detection engine. It's great at detecting ransomwares and we want to share results and ideas with you.
I forgot to update the README. We just replaced RNN with xgboost that has a better f1 and is very quick, as the decision trees are translated to plain rust using m2cgen.
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Is data science/engineering in Rust practical, does it provide any benefit over Python, and what are the best crates?
Probably, as many frameworks come with a Rust support (or there are wrappers). Some models, like decision tree, can also be automatically translated to plain Rust (in my company we use m2cgen to translate xgboost models to plain rust code).
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Flutter Machine Learning App
These repositories on GitHub are good start I think: https://github.com/BayesWitnesses/m2cgen and https://github.com/vickylance/dart_nn
What are some alternatives?
ML.NET - ML.NET is an open source and cross-platform machine learning framework for .NET.
TensorFlow.NET - .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
Brain.js - Simple feed-forward neural network in JavaScript
R Provider - Access R packages from F#
Keras.js - Run Keras models in the browser, with GPU support using WebGL
gorse - Gorse open source recommender system engine
Breeze - Breeze is a numerical processing library for Scala.
randomforest - Random Forest implementation in golang
Dannjs - Easy to use Deep Neural Network Library for JavaScript.
gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
neurojs - A JavaScript deep learning and reinforcement learning library.
go-fann - Go bindings for FANN, library for artificial neural networks