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La question de la traduction automatique s'est posée dès la naissance de l'informatique. Elle semblait alors accessible, mais quiconque, aujourd'hui, utilise les traducteurs automatiques disponibles sur Internet sait que, malgré les remarquables progrès effectués, on est encore loin d'une traduction toujours fidèle. La complexité du langage naturel et ses ambiguïtés sont bien faites pour dérouter les algorithmes pleinement rationnels de nos ordinateurs. Les "réseaux de neurones" qui pratiquent l'"apprentissage profond" sont la dernière en date des multiples stratégies déployées pour parler avec la machine... et s'en faire comprendre.
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"Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race"--
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"An advanced book for researchers and graduate students working in machine learning and statistics that reflects the influence of deep learning"--
Machine learning. --- Probabilities. --- Apprentissage automatique. --- Probabilités. --- Machine learning --- Probabilities
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