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Dévoiler la boîte noire: Apprentissage profond pratique et IA explicable
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ISBN10: 6208169941
ISBN13: 9786208169947
Publisher: Editions Notre Savoir
Published: Dec 16 2024
Pages: 148
Weight: 0.50
Height: 0.34 Width: 6.00 Depth: 9.00
Language: French
ISBN13: 9786208169947
Publisher: Editions Notre Savoir
Published: Dec 16 2024
Pages: 148
Weight: 0.50
Height: 0.34 Width: 6.00 Depth: 9.00
Language: French
Unveiling the Black Box: Practical Deep Learning and Explainable AI offre un aperçu complet des techniques d'IA explicable (XAI) et de leur importance pour garantir la transparence et la confiance dans les modèles d'IA complexes. Les applications de l'IA couvrant les soins de santé, la finance et les systèmes autonomes, l'opacité des modèles d'apprentissage profond soulève souvent des problèmes éthiques, juridiques et de fiabilité. Ce guide explore les structures fondamentales des modèles d'IA, tels que les réseaux de neurones à propagation directe (FNN), les réseaux de neurones convolutionnels (CNN) et les réseaux de neurones récurrents (RNN), en mettant en évidence leur architecture, leurs fonctionnalités et leurs applications dans le monde réel. Pour améliorer l'interprétabilité, le texte présente les principales méthodes XAI telles que les explications locales interprétables indépendantes du modèle (LIME) et les explications additives SHAPley (SHAP), qui permettent aux utilisateurs de comprendre les prédictions du modèle. Des techniques avancées, notamment l'apprentissage par transfert et les mécanismes d'attention, sont abordées pour illustrer leur impact sur l'adaptabilité et les performances des réseaux neuronaux. Les défis liés à la réalisation d'une IA interprétable, tels que la gestion des biais, l'équilibre entre la précision et la garantie de la confidentialité, sont également abordés.
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