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Techniques and educational content for interpreting model decisions.
Distinguishing note: Focuses on educational explainability rather than production monitoring tools.
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This project is an interactive educational textbook and comprehensive machine learning resource designed for deep learning education. It provides a structured curriculum that combines narrative prose with executable code, utilizing literate programming to create reproducible learning experiences within a collection of Jupyter Notebooks. The repository distinguishes itself by teaching machine learning through applied research and modular design. It demonstrates a callback-driven training loop, a declarative data-block pipeline, and a layered abstraction API that allows users to transition betw
Demonstrates class activation maps for interpreting model predictions.