In parts 1 and 2, we learned a lot about the “modeling” part of the modern NLP, including word embeddings, RNNs, CNNs, and the Transformer. However, you still need to learn how to effectively train, serve, deploy, and interpret those models for building robust and practical NLP applications.
Chapter 10 touches upon important machine learning techniques and best practices when developing NLP applications, including batching and padding, regularization, and hyperparameter optimization.
Finally, if chapters 1 to 10 are about building NLP models, chapter 11 covers everything that happens outside NLP models. The chapter covers how to deploy, serve, explain, and interpret NLP models.