February 2024
Intermediate to advanced
456 pages
12h 6m
French
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CHAPITRE 4 Réseaux antagonistes génératifs
Objectifs du chapitre :
• Découvrir la conception architecturale d’un réseau antagoniste génératif (GAN).
• Construire et entraîner un GAN convolutif profond (DCGAN) en partant de zéro avec Keras.
• Utiliser un DCGAN pour générer de nouvelles images.
• Comprendre certains des problèmes courants rencontrés lors de l’entraînement d’un DCGAN.
• Découvrir comment l’architecture Wasserstein GAN (WGAN) résout ces problèmes.
• Comprendre les améliorations qui peuvent être apportées au WGAN, telles que l’incorporation d’un terme de pénalité de gradient (GP) dans la fonction de perte.
• Construire un WGAN-GP en partant de zéro avec Keras.
• Utiliser un WGAN-GP pour générer des visages.
• Découvrir comment un GAN ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.