stanford-cs-229-machine-learning
pyod
| stanford-cs-229-machine-learning | pyod | |
|---|---|---|
| 1 | 7 | |
| 16,526 | 7,962 | |
| - | - | |
| 0.0 | 7.5 | |
| almost 4 years ago | 5 days ago | |
| Python | ||
| MIT License | BSD 2-clause "Simplified" License |
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stanford-cs-229-machine-learning
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?
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anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
MoneroAddressesCS - An infographic about Monero Keys and Addresses, their relations and scopes
stumpy - STUMPY is a powerful and scalable Python library for modern time series analysis