國立高雄大學圖資館 |

Language: English

Back

Explainable AI with Python

Di Cecco, Antonio.

Explainable AI with Python

Record Type:	Electronic resources : Monograph/item
Title/Author:	Explainable AI with Pythonby Leonida Gianfagna, Antonio Di Cecco.
Author:	Gianfagna, Leonida.
other author:	Di Cecco, Antonio.
Published:	Cham :Springer International Publishing :2021.
Description:	viii, 202 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-68640-6
ISBN:	9783030686406$q(electronic bk.)

Explainable AI with Python
Gianfagna, Leonida.

Explainable AI with Python[electronic resource] /by Leonida Gianfagna, Antonio Di Cecco. - Cham :Springer International Publishing :2021. - viii, 202 p. :ill. (some col.), digital ;24 cm.

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.

ISBN: 9783030686406$q(electronic bk.)

Standard No.: 10.1007/978-3-030-68640-6doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .G53 2021

Dewey Class. No.: 006.31

Explainable AI with Python
LDR:01880nmm a2200313 a 4500 001 597594
003 DE-He213
005 20210428212822.0
006 m d
007 cr nn 008maaau
008 211019s2021 sz s 0 eng d
020 $a 9783030686406$q(electronic bk.)
020 $a 9783030686390$q(paper)
024 7 $a 10.1007/978-3-030-68640-6 $2 doi
035 $a 978-3-030-68640-6
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .G53 2021
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .G433 2021
100 1 $a Gianfagna, Leonida. $3 890953
245 1 0 $a Explainable AI with Python $h [electronic resource] / $c by Leonida Gianfagna, Antonio Di Cecco.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a viii, 202 p. : $b ill. (some col.), digital ; $c 24 cm.
520 $a 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.
650 0 $a Machine learning. $3 188639
650 0 $a Artificial intelligence. $3 194058
650 0 $a Neural networks (Computer science) $3 181982
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Python. $3 763308
700 1 $a Di Cecco, Antonio. $3 890954
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-68640-6
950 $a Computer Science (SpringerNature-11645)