While foundational network designs like CNNs and RNNs handle many tasks effectively, certain problem domains require more specialized architectures. This chapter focuses on implementing several advanced neural network models using PyTorch.

You will learn the structure and implementation details of key modern architectures. We will cover building Transformer models component by component, including attention mechanisms. We will work with Graph Neural Networks (GNNs) for graph-structured data, utilizing libraries like PyTorch Geometric. Additionally, the chapter introduces Normalizing Flows for generative tasks, Neural Ordinary Differential Equations (Neural ODEs) for continuous-depth modeling, and meta-learning approaches for few-shot scenarios. The emphasis is on understanding the building blocks and constructing these complex models in code.