Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks (Record no. 696011)

MARC details
000 -LEADER
fixed length control field 02049 a2200193 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240712153415.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240712b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781484285466
040 ## - CATALOGING SOURCE
Language of cataloging English
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31/MIS
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Mishra, Pradeepta
245 ## - TITLE STATEMENT
Title Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Apress
Date of publication, distribution, etc 2024
300 ## - PHYSICAL DESCRIPTION
Extent xviii, 344p
520 ## - SUMMARY, ETC.
Summary, etc This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers.<br/>You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision.<br/>Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data and natural language processing–related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks.<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AI model interpretable and explainable, Examine the biasness and good ethical practices of AI models, Quantify, visualize, and estimate reliability of AI models, Design frameworks to unbox the black-box models, Assess the fairness of AI models, Understand the building blocks of trust in AI models, Increase the level of AI adoption.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Cost, replacement price Price effective from Koha item type Public note
    Dewey Decimal Classification     Symbiosis Institute of Computer Studies and Research Symbiosis Institute of Computer Studies and Research Python 12/07/2024 The Word Book Shop 1099.00   006.31/MIS SICSR-B-19672 12/07/2024 1099.00 12/07/2024 Books AI model interpretable and explainable, Examine the biasness and good ethical practices of AI models, Quantify, visualize, and estimate reliability of AI models, Design frameworks to unbox the black-box models, Assess the fairness of AI models, Understand the building blocks of trust in AI models, Increase the level of AI adoption.