Explainable Ai: Foundations, Methodologies and Applications da Mehta, Mayuri (Editor)/ Palade, Vasile (Editor)/ Chatterjee, Indranath (Editor): Brand New Hardcover (2022)

Riguardo questo articolo

278 pages. 9.25x6.10x0.69 inches. In Stock.

Codice articolo x-3031128060

Contatta il venditore

Segnala questo articolo

Dati bibliografici

Titolo: Explainable Ai: Foundations, Methodologies and Applications
Editore: Springer Nature
Data di pubblicazione: 2022
Lingua: Inglese
ISBN 10: 3031128060
ISBN 13: 9783031128066
Rilegatura: Hardcover
Condizione: Brand New

Descrizione articolo

Riassunto

This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas.

The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.

Dalla quarta di copertina

This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas.

The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.