Explainable AI with Python /

Explainable AI with Python /

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Bibliographic Details
Author / Creator:	Gianfagna, Leonida, author.
Imprint:	Cham, Switzerland : Springer, [2021]
Description:	1 online resource (viii, 202 pages) : illustrations (some color)
Language:	English
Subject:	Machine learning. Artificial intelligence. Neural networks (Computer science) Python (Computer program language) Artificial intelligence. Machine learning. Neural networks (Computer science) Python (Computer program language) Electronic books.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12612702

Hidden Bibliographic Details
Other authors / contributors:	Di Cecco, Antonio, author.
ISBN:	9783030686406 303068640X 3030686396 9783030686390
Notes:	Includes bibliographical references and index. Online resource; title from PDF title page (SpringerLink, viewed May 17, 2021).
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.
Other form:	Print version: 9783030686390
Standard no.:	10.1007/978-3-030-68640-6

Table of 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.