Show HN: A path through time-series anomaly detection, z-scores to deep learning (github.com)

A new project on GitHub introduces a comprehensive tutorial series for time-series anomaly detection, guiding users from simple z-score methods to advanced deep learning techniques. The initiative emphasizes not only the implementation of various algorithms but also the crucial aspect of evaluating their performance honestly. The series features runnable notebooks that are designed to be executed on CPUs in minutes, with clear documentation on each method's strengths and weaknesses, using a consistent and accessible visual language throughout. This resource is significant for the AI/ML community because it addresses a common gap in anomaly detection training: the lack of a structured learning path that combines practical execution with thorough evaluation. Each notebook is self-contained, yet part of a recommended reading order, allowing learners to progress systematically. With an emphasis on reproducibility and clarity, this project not only builds on the foundations laid in earlier tutorials but also reflects a decade of advancements in the field, making it a valuable tool for practitioners and researchers alike. Users are encouraged to contribute to the project's visibility by starring the repository if they find it helpful.