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, Ainsi, l'application de ces règles permet au modèle de converger vers le minimum de la fonction de coût

, Avec les informations précédentes, on peut organiser l'apprentissage du modèle en différentes étapes: 1. Initialisation aléatoire des paramètres w du modèle 2. Calcul de la sortie du modèle avec les paramètres précédemment fixés 3. Calcul de la fonction de coût (

, jusqu'à atteindre le minimum ? de la fonction de coût

C. , en utilisant l'algorithme de Levendberg-Marquart et en diminuant la fonction de coût que l'ont obtient un modèle optimal. Cependant, comme nous allons le voir dans la partie suivante, l'apprentissage devra suivre certaines règles pour éviter que le modèle n'effectue un surapprentissage des données à sa disposition

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