emlearn
sklearn-project-template
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| emlearn | sklearn-project-template | |
|---|---|---|
| 5 | 2 | |
| 374 | 24 | |
| 7.2% | - | |
| 9.2 | 3.2 | |
| 20 days ago | over 2 years 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
sklearn-project-template
What are some alternatives?
miceforest - Multiple Imputation with LightGBM in Python
playground - A Streamlit application to play with machine learning models directly from the browser
cppflow - Run TensorFlow models in C++ without installation and without Bazel
Machine-Learning - Implementation of different ML Algorithms from scratch, written in Python 3.x
fselect - Find files with SQL-like queries
igel - a delightful machine learning tool that allows you to train, test, and use models without writing code
pico-wake-word - MicroSpeech Wake Word example on the Raspberry Pi Pico. This is a port of the example on the TensorFlow repository.
experta - Expert Systems for Python
gutenberg - A fast static site generator in a single binary with everything built-in. https://www.getzola.org
multithread-serial-logger - Simple multi-threaded logger with Qt5 GUI for embedded devices such as Arduino, ESP, STM32, etc, using standard UART.
GoJS, a JavaScript Library for HTML Diagrams - JavaScript diagramming library for interactive flowcharts, org charts, design tools, planning tools, visual languages.
Zip Foundation - Effortless ZIP Handling in Swift