The Complete GANs Bootcamp: Theory and Applications

Explore how generative adversarial networks train a generator to produce realistic images while a discriminator learns to distinguish real from fake, in a two-player minimax game.

Explore how DCGANs theory pairs a generator and discriminator in deep convolutional networks to transform random noise into images that resemble real ones.

Walks through a DCGAN code cell by cell, detailing imports, data preparation, transforms, and weight initialization for the generator and discriminator.

Build a deep convolutional generative network to generate images of cat faces. Mitigate vanishing gradients with regularization techniques such as dropout in the discriminator to keep generator and discriminator learning.