{"product_id":"9783031922282","title":"Explainable AI with Python","description":"\u003ch1\u003eExplainable AI with Python\u003c\/h1\u003e \u003ch2\u003eDi Cecco, Antonio; Gianfagna, Leonida\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eThis comprehensive book on Explainable Artificial Intelligence has been updated and expanded to reflect the latest advancements in the field of XAI, enriching the existing literature with new research, case studies, and practical techniques. \u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eThe Second Edition expands on its predecessor by addressing advancements in AI, including large language models and multimodal systems that integrate text, visual, auditory, and sensor data. It emphasizes making complex systems interpretable without sacrificing performance and provides an enhanced focus on additive models for improved interpretability. Balancing technical rigor with accessibility, the book combines theory and practical application to equip readers with the skills needed to apply explainable AI (XAI) methods effectively in real-world contexts.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eFeatures:\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eExpansion of the \"Intrinsic Explainable Models\" chapter to delve deeper into generalized additive models and other intrinsic techniques, enriching the chapter with new examples and use cases for a better understanding of intrinsic XAI models.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eFurther details in \"Model-Agnostic Methods for XAI\" focused on how explanations differ between the training set and the test set, including a new model to illustrate these differences more clearly and effectively.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eNew section in \"Making Science with Machine Learning and XAI\" presenting a visual approach to learning the basic functions in XAI, making the concept more accessible to readers through an interactive and engaging interface.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eRevision in \"Adversarial Machine Learning and Explainability\" that includes a code review to enhance understanding and effectiveness of the concepts discussed, ensuring that code examples are up-to-date and optimized for current best practices.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eNew chapter on \"Generative Models and Large Language Models (LLM)\" chapter dedicated to generative models and large language models, exploring their role in XAI and how they can be used to create richer, more interactive explanations. This chapter also covers the explainability of transformer models and privacy through generative models.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eNew \"Artificial General Intelligence and XAI\" mini-chapter dedicated to exploring the implications of Artificial General Intelligence (AGI) for XAI, discussing how advancements towards AGI systems influence strategies and methodologies for XAI.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal; mso-pagination: widow-orphan;\"\u003e\u003cspan style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-ligatures: none; mso-fareast-language: EN-US;\"\u003eEnhancements in \"Explaining Deep Learning Models\" features new methodologies in explaining deep learning models, further enriching the chapter with cutting-edge techniques and insights for deeper understanding.\u003c\/span\u003e\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2025-08-06\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9783031922282\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-031-92229-9\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 324\u003c\/p\u003e ","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":44309896626316,"sku":"9783031922282","price":53.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783031922282.jpg?v=1776097805","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783031922282","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}