May 2020
Intermediate to advanced
576 pages
26h 55m
French
Content preview from Deep Learning avec Keras et TensorFlow
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516
Annexe B Différentiation automatique
vers la sortie) pour calculer la valeur de chaque nœud. Elle procède ensuite à une
seconde passe, cette fois-ci en sens inverse (de la sortie vers les entrées) pour calculer
toutes les dérivées partielles. Le « mode inverse » du nom de cette différentiation
automatique vient de cette seconde passe sur le graphe, dans laquelle les gradients
vont dans le sens inverse. La gureB.3 illustre cette seconde phase. Au cours de la
première, toutes les valeurs des nœuds ont été calculées, en partant de x=3 et y=4.
Ces valeurs sont données en partie inférieure droite de chaque nœud (par exemple,
x×x = 9). ...
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