emlearn
miceforest
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| emlearn | miceforest | |
|---|---|---|
| 5 | 6 | |
| 374 | 294 | |
| 7.2% | - | |
| 9.2 | 5.7 | |
| 20 days ago | 5 months ago | |
| Python | Python | |
| MIT License | MIT License |
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.
emlearn
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EleutherAI announces it has become a non-profit
> My big gripe, and for obvious reasons, is that we need to step away from cloud-based inference, and it doesn't seem like anyone's working on that.
I think there are steps being taken in this direction (check out [1] and [2] for interesting lightweight transpile / ad-hoc training projects) but there is a lack of centralized community for these constrained problems.
[1] https://github.com/emlearn/emlearn
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Simple and embedded friendly C code for Machine Learning inference algorithms
Examples: Gaussian Mixture Models (GMM) for anomaly detection or clustering Mahalanobis distance (EllipticEnvelope) for anomaly detection Decision trees and tree ensembles (Random Forest, ExtraTrees) Feed-forward Neural Networks (Multilayer Perceptron, MLP) for classification Gaussian Naive Bayes for classification
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[D] Drop your best open source Deep learning related Project
https://github.com/emlearn/emlearn is a ML inference engine for microcontrollers and embedded systems, allowing to deploy models to any platform with a C99 compiler. Has also been used for network traffic analysis as a Linux kernel module, and embedded in Android apps.
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Regression with the C64
The C64 has 64 kB of RAM. That is more than many contemporary microcontrollers. Using something like https://github.com/emlearn/emlearn allows to generate portable C code of ML models for such targets. Should be able to classify digits (MNIST) no problem on such hardware. Assuming there is a workable C compiler available.
Disclosure: Maintainer of emlearn project.
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Ask HN: What are some tools / libraries you built yourself?
I built emlearn, a Machine Learning inference engine for microcontrollers and embedded systems. It allows converting traditional ML models to simple and portable C99, following best practices in embedded software (no dynamic allocations etc). https://github.com/emlearn/emlearn
miceforest
- Ask HN: What is the most impactful thing you've ever built?
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Cold Showers
Wow, very interesting, thanks for this. Daily batch predictions is all we do. I’m the maintainer of miceforest[1], do you think this would integrate well into the package at a brief glance? I’m always looking for ways to make this package faster.
[1] https://github.com/AnotherSamWilson/miceforest
- Miceforest: Fast, Memory Efficient, Multiple Imputation by Chained Equations
- Show HN: Multiple Imputation with Lightgbm
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Multiple Imputation with lightgbm
I am the maintainer of miceforest. I've just released a major update that a lot of you might find useful.
- R markdown README.rmd Equivalent in Python?
What are some alternatives?
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