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Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning
Hardcover
General ComputersProbability & Statistics
ISBN13: 9783030833558
Publisher: Springer
Published: Dec 16 2021
Pages: 310
Weight: 1.42
Height: 0.75 Width: 6.14 Depth: 9.21
Language: English
--Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYU
This is a wonderful book! I'm pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I've seen that has up-to-date and well-rounded coverage. Thank you to the authors!
--Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics
Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level.
Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist.
Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder ofExplainable AI-XAI Group
1 different editions
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