توضیحات
Attacks, Defenses and Testing for Deep Learning provides a comprehensive guide to understanding security and reliability challenges in deep learning systems. It covers adversarial attacks, data poisoning, model evasion techniques, and corresponding defense strategies such as robust training and anomaly detection. The book also explores testing methodologies to evaluate model performance and resilience under adversarial conditions. Designed for researchers and practitioners, it combines theoretical foundations with practical examples to ensure the safe and reliable deployment of deep learning applications.
نقد و بررسیها
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