SeaLion
pyod
| SeaLion | pyod | |
|---|---|---|
| 4 | 7 | |
| 332 | 8,029 | |
| - | - | |
| 5.2 | 7.5 | |
| 7 months ago | 6 days ago | |
| Python | Python | |
| Apache License 2.0 | BSD 2-clause "Simplified" License |
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SeaLion
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Cython, why should I care?
I was searching reddit earlier, for certain machine learning topics, and I came across a topic which looks like a showcase. By following the links leading to the repo, I found some .pyx and .pxd hybrid modules. I was always skeptical about taking the trouble of writing modules in this weird syntax, expecting promising performance gains. By searching cython projects on github, I found many others. It looks like some people found it interesting to adopt in their projects. What can you consider as valid use case(s) for cython? I mean if you're really that worried about performance, which you can't get using python, wouldn't it be wiser to use optimized C/C++ with possibly a python API?
- A Python ML framework that encourages learning ML concepts
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Machine Learning Library by 14-year old : SeaLion
We do that already inside of the source code. The ensemble learning classifier has a method in which you can train multiple models all at once in parallel and then get the best classifier on the dataset. You can check out the ensemble learning tutorials here : ensemble learning tutorial
pyod
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A Comprehensive Guide for Building Rag-Based LLM Applications
This is a feature in many commercial products already, as well as open source libraries like PyOD. https://github.com/yzhao062/pyod
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Analyze defects and errors in the created images
PyOD
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Multivariate Outlier Detection in Python
Check out the algorithms and documentation in this toolkit. It’ll give you a list of methods to read up on to understand their mechanisms. https://github.com/yzhao062/pyod
- Pyod – A Comprehensive and Scalable Python Library for Outlier Detection
- Predictive Maintenance and Anomaly Detection Resources
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[D] Unsupervised Outlier Detection - Advise Requested
The source code and documentaion of PyOD is the best survey about OOD. Besides, the normalized flow and VQVAE are also feasible.
- PyOD: ~50 anomaly detection algorithms in one framework.
What are some alternatives?
scikit-learn - scikit-learn: machine learning in Python
tods - TODS: An Automated Time-series Outlier Detection System
mlcourse.ai - Open Machine Learning Course
isolation-forest - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.
alibi-detect - Algorithms for outlier, adversarial and drift detection
pycaret - An open-source, low-code machine learning library in Python
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
stumpy - STUMPY is a powerful and scalable Python library for modern time series analysis
pymiere - Python for Premiere pro
loglizer - A machine learning toolkit for log-based anomaly detection [ISSRE'16]
kafkaml-anomaly-detection - Project for real-time anomaly detection using Kafka and python
deep_learning_and_the_game_of_go - Code and other material for the book "Deep Learning and the Game of Go"