Moving from theory to practice, this chapter concentrates on the engineering aspects of constructing and refining advanced GANs. Successfully implementing models like StyleGAN or BigGAN requires careful attention to detail beyond the core algorithms.

We will examine practical considerations such as selecting between TensorFlow and PyTorch for GAN development, utilizing advanced optimizers like AdamW and Lookahead, and establishing effective hyperparameter tuning procedures. Proper weight initialization, important for stability, will be discussed alongside techniques for diagnosing and resolving common training issues. Additionally, we'll cover methods for accelerating training, including mixed precision ($FP16$) computation and distributed training strategies for handling large-scale models. Finally, you'll learn about profiling tools and performance optimization techniques to make your GAN implementations efficient.