January 2019
Intermediate to advanced
316 pages
8h 16m
English
Content preview from Generative Adversarial Networks Projects
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What this book covers
Chapter 1, Introduction to Generative Adversarial Networks, starts with the concepts of GANs. Readers will learn what a discriminator is, what a generator is, and what Game Theory is. The next few topics will cover the architecture of a generator, the architecture of a discriminator, objective functions for generators and discriminators, training algorithms for GANs, Kullback–Leibler and Jensen–Shannon Divergence, evaluation matrices for GANs, different problems with GANs, the problems of vanishing and exploding gradients, Nash equilibrium, batch normalization, and regularization in GANs.
Chapter 2, 3D-GAN – Generating Shapes Using GANs, starts with a short introduction to 3D-GANs and various architectural details. In ...
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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.