Explainable AI for Practitioners - Designing and Implementing Explainable ML Solutions

Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability.

About the author

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.

Product details

• Authors: Parker Barnes, Michael Munn, Munn Michael, David Pitman, Pitman David
• Publisher: O'Reilly
• Languages: English
• Product format: Paperback / Softback
• Released: 30.11.2022
• EAN: 9781098119133
• ISBN: 978-1-09-811913-3
• Dimensions: 178 mm x 233 mm x 15 mm
• Weight: 304 g
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; machine learning, Computer Vision, pattern recognition, Natural language & machine translation, COMPUTERS / Data Science / Machine Learning, COMPUTERS / Data Science / Neural Networks, Natural language and machine translation, Neural networks and fuzzy systems, Neural networks & fuzzy systems