Keras 3

11.4    Summary

In this chapter, we explored GenAI through three key architectures. We began with autoencoders, learning how their encoder-decoder structure compresses data into meaningful latent representations and reconstructs outputs, with variations such as denoising and sparse autoencoders addressing specific challenges. We then advanced to Variational Autoencoders (VAEs), which introduced probabilistic encoding through the reparameterization trick and KL divergence loss, enabling controlled generation by sampling from learned latent distributions. Finally, we tackled Generative Adversarial Networks (GANs), implementing a complete ACGAN that combines adversarial training between generator and discriminator networks with class conditioning ...