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Anomaly Detection Learning Resources is a curated open-source repository that collects educational materials, tools, and academic references related to anomaly detection and outlier analysis in data science. The project serves as a centralized index for researchers and practitioners who want to explore algorithms, datasets, and publications associated with detecting unusual patterns in data. The repository organizes resources into structured categories such as books, tutorials, academic papers, datasets, benchmark frameworks, and open-source toolkits. It includes materials covering a wide range of anomaly detection domains, including time series data, graph data, tabular datasets, and real-time monitoring systems. By compiling resources from multiple programming ecosystems such as Python, R, and other machine learning platforms, the repository allows users to discover both research papers and practical implementations.
Features
- Curated list of anomaly detection books, papers, and tutorials
- Collections of datasets and benchmarking frameworks for experiments
- Catalog of open-source anomaly detection libraries and toolkits
- Coverage of multiple domains such as time series, graphs, and tabular data
- Links to conferences, journals, and academic workshops in the field
- Continuously updated resources reflecting new research trends
Project Samples
