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SKU/Artículo: AMZ-0128240202
Adversarial Robustness for Machine Learning
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.65 kg
0.65 kg
Devolución:
No
No
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and verification. Sections cover adversarial attack, verification and defense, mainly focusing on image classification applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in the area, which can be a good reference for conducting future research. In addition, the book can also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems. Summarizes the whole field of adversarial robustness for Machine learning modelsProvides a clearly explained, self-contained referenceIntroduces formulations, algorithms and intuitionsIncludes applications based on adversarial robustness
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U$S 188,93
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