Isbn: 9781779568878 - fundamentals of robust machine learning (13 risultati)

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Taschenbuch. Condizione: Neu. Fundamentals of Robust Machine Learning | Bechoo Lal | Taschenbuch | Englisch | 2026 | Toronto Academic Press | EAN 9781779568878 | Verantwortliche Person für die EU: Mare Nostrum Group B.V., Doelen 72, 4831 GR BREDA, NIEDERLANDE, gpsr[at]mare-nostrum[dot]co[dot]uk | Anbieter: preigu.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The reliability and stability of machine learning systems are crucial in real-world applications, where data variability, noise, and uncertainty can significantly affect model performance. Building models that remain effective under such challenges defines the essence of robust AI. Fundamentals of Robust Machine Learning explores the theoretical and practical approaches to designing resilient learning algorithms. The book discusses adversarial robustness, data augmentation, uncertainty quantification, and generalization techniques. It also covers robust optimization and fairness in model evaluation. Combining mathematical rigor with applied examples, it provides students, researchers, and engineers with tools to build dependable machine learning systems capable of handling complex, imperfect, and evolving data environments. 484 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The reliability and stability of machine learning systems are crucial in real-world applications, where data variability, noise, and uncertainty can significantly affect model performance. Building models that remain effective under such challenges defines the essence of robust AI. Fundamentals of Robust Machine Learning explores the theoretical and practical approaches to designing resilient learning algorithms. The book discusses adversarial robustness, data augmentation, uncertainty quantification, and generalization techniques. It also covers robust optimization and fairness in model evaluation. Combining mathematical rigor with applied examples, it provides students, researchers, and engineers with tools to build dependable machine learning systems capable of handling complex, imperfect, and evolving data environments.…