This chapter focuses on the practical implementation of sophisticated model architectures using TensorFlow. You'll apply the techniques learned previously to construct components of models prevalent in current research and applications.

We will examine the building blocks of architectures like Transformers, including attention mechanisms and encoder structures. You will also learn the fundamentals of Generative Adversarial Networks (GANs) and implement a basic version. Additionally, we cover Graph Neural Networks (GNNs) for processing graph-structured data and introduce the TF-Agents library for reinforcement learning applications. The emphasis is on understanding the core TensorFlow code required to build these advanced components.