Natural Language Processing

Natural Language Processing (NLP) is often regarded as a cornerstone of artificial intelligence. The advent of deep learning has revolutionized NLP techniques, particularly with the rise of large language models in recent years, which have established a new paradigm for NLP research. This book introduces fundamental concepts in natural language processing and deep learning while emphasizing modern NLP techniques driven by pre-trained and large language models.

The book is structured into three main sections:

• Foundational Knowledge: This section covers the essential principles of natural language processing and deep learning, including key toolsets and commonly used datasets.
• Pre-trained Language Models: This section explores language models, pre-trained word embeddings, methodologies for implementing pre-trained models, and their applications.
• Large Language Models: This section begins with an overview of pre-training methods for large language models, followed by discussions on adaptation, application, and evaluation strategies. It also examines extension techniques inspired by pre-trained models and concludes with an illustrative example using the DeepSeek series of models, showcasing the latest advancements in large language models.

Beyond theoretical foundations, this book incorporates practical case studies with accompanying PyTorch code implementations, enabling readers to deepen their understanding while seamlessly applying NLP models in practice-bridging theory with hands-on experience.

Designed for college students, academic researchers, and computer engineers with a basic knowledge of machine learning, this book serves as a valuable resource for those seeking to advance their expertise in NLP algorithms. It is also an essential reference for students and professionals interested in artificial intelligence, deep learning, large language models, and natural language processing, as well as individuals aspiring to enter the field of AI applications.