Michael Munn, David Pitman

Explainable AI for Practitioners (eBook, ePUB)

• Format: ePub

Produktbeschreibung

Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does.Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow.This essential book provides:A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needsTips and best practices for implementing these techniquesA guide to interacting with explainability and how to avoid common pitfallsThe knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systemsAdvice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text dataExample implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace

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Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 278
• Erscheinungstermin: 31. Oktober 2022
• Englisch
• ISBN-13: 9781098119096
• Artikelnr.: 72389886

Autorenporträt

Michael Munn is a research software engineer at Google. His work focuses on better understanding the mathematical foundations of machine learning and how those insights can be used to improve machine learning models at Google. Previously, he worked in the Google Cloud Advanced Solutions Lab helping customers design, implement, and deploy machine learning models at scale. Michael has a PhD in mathematics from the City University of New York. Before joining Google, he worked as a research professor.