Fully functional code for every chapter, from basic GANs to advanced models like CycleGAN.
# Define the GAN model def gan_model(generator, discriminator): discriminator.trainable = False model = keras.Sequential() model.add(generator) model.add(discriminator) return model gans in action pdf github
The world of Generative AI has exploded in recent years. From creating hyper-realistic human faces to generating art and enhancing medical images, Generative Adversarial Networks (GANs) are at the heart of this revolution. However, for many learners, the mathematical complexity of GANs presents a steep barrier. This is where GANs in Action by Jakub Langr and Vladimir Bok steps in. Fully functional code for every chapter, from basic
. The GAN architectures covered (DCGAN, cGAN, WGAN-GP, Pix2Pix, CycleGAN) remain industry baselines. However, modern advances (StyleGAN3, Diffusion Models) are not included. Consider this a foundational book – after finishing, you can read StyleGAN papers and adapt the code. However, for many learners, the mathematical complexity of
: Deep Convolutional GANs (DCGAN), Semi-Supervised GANs, and Conditional GANs.
