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Springer Explainable AI - Interpreting Deep Learning Book

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Description

As intelligent systems take over autonomous decision-making, the need for human oversight becomes a priority. This Springer publication, part of the Lecture Notes in Computer Science series (11700), addresses the risks associated with giving control to machines. It focuses on the necessity of validating AI behavior before deployment to prevent improper or unsafe actions. For industries involving critical infrastructure, human well-being, or health, understanding how deep learning models reach conclusions is necessary. This book provides insights into interpreting, explaining, and visualizing these complex systems to ensure decisions are robust and safe for real-world applications.

Key Features

This Springer publication is part of the Lecture Notes in Computer Science series, volume 11700.

The text focuses on interpreting, explaining, and visualizing deep learning models for better transparency.

It addresses the risks of autonomous decision-making and the need for human oversight in AI technology.

The content covers methods to validate AI behavior to prevent unsafe or non-robust decisions in critical tasks.

This book is designed for those working with sensitive AI applications involving health or critical infrastructure.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
30 August 2019
Listed Since
11 July 2019

Barcode

No barcode data available

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