Explainable Deep Learning AI: Methods and Challenges

Benois-Pineau, Jenny, Bourqui, Romain, Petkovic, Dragutin

  • 出版商: Academic Press
  • 出版日期: 2023-02-24
  • 售價: $4,380
  • 貴賓價: 9.5$4,161
  • 語言: 英文
  • 頁數: 346
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0323960987
  • ISBN-13: 9780323960984
  • 相關分類: DeepLearning
  • 海外代購書籍(需單獨結帳)

商品描述

Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI - deep learning, which become the necessary condition in various applications of artificial intelligence.

The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented.

商品描述(中文翻譯)

《可解釋的深度學習人工智慧:方法與挑戰》介紹了領先研究人員在可解釋人工智慧(XAI)領域的最新研究成果,提供了XAI領域的概述,以及解決深度學習人工智慧系統可解釋性挑戰的幾種新技術方法和應用。該書首先概述了XAI,然後介紹了一些特定的深度學習技術和方法,從一般的XAI方法到特定的XAI應用,最後介紹了以使用者為導向的評估方法。它還探討了可解釋人工智慧的主要類別-深度學習,這在各種人工智慧應用中成為必要條件。

該書解釋了一些方法的群組,如反向傳播和擾動法,並介紹了它們在各種數據分類中的應用。