| Description |
1 online resource (viii, 202 pages) : illustrations (some color) |
| Contents |
The Landscape -- Explainable AI: needs, opportunities and challenges -- Intrinsic Explainable Models -- 3.1. Loss Function -- Model-agnostic methods for XAI -- Explaining Deep Learning Models -- Making science with Machine Learning and XAI -- Adversarial Machine Learning and Explainability --A proposal for a sustainable model of Explainable AI |
| Summary |
This book provides a full presentation of the current concepts and available techniques to make "machine learning" systems more explainable. The approaches presented can be applied to almost all the current "machine learning" models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others. Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed May 17, 2021) |
| Subject |
Machine learning.
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Artificial intelligence.
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Neural networks (Computer science)
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Python (Computer program language)
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Artificial Intelligence
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Neural Networks, Computer
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Machine Learning
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artificial intelligence.
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Artificial intelligence
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Machine learning
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Neural networks (Computer science)
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Python (Computer program language)
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| Form |
Electronic book
|
| Author |
Di Cecco, Antonio, author
|
| ISBN |
9783030686406 |
|
303068640X |
|
