With the subtitle, "Interpreting, Explaining and Visualizing Deep Learning" the 22 chapters in this book provide a snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

<ASIN:3030289532>

Authors/Editors: Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus-Robert Muller
Publisher: Springer
Date: August 2019
Pages: 452
ISBN: 978-3030289539
Print: 3030289532
Kindle: B07XQ4KSB9
Audience: developers interested in AI
Level: Intermediate
Category: Artificial Intelligence

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