This book systematically explains the fundamentals of natural language processing (NLP) and advanced knowledge related to advanced NLP model applications. The book consists of 11 chapters: Chapters 1-5 cover the basics of NLP, and Chapters 6-11 apply NLP knowledge to practical scenarios. The book primarily introduces pre-trained models, text classification, machine reading comprehension, named entity recognition, text generation, model distillation and pruning, loss functions, and so on. The book contains numerous application examples, enabling readers not only to grasp theoretical knowledge but also to apply it flexibly. The examples in the book are developed based on the Linux and PyTorch environments, so readers can learn PyTorch framework techniques while studying NLP concepts. With comprehensive content and clear steps, it provides engineered solutions. This book can serve as a primer for readers with a certain foundation in deep learning. It is also a valuable reference for technicians working in NLP algorithm development and training institutions.