Book Image

Mastering NLP from Foundations to LLMs

By : Lior Gazit, Meysam Ghaffari
Book Image

Mastering NLP from Foundations to LLMs

By: Lior Gazit, Meysam Ghaffari

Overview of this book

Do you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you’ll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You’ll also explore general machine learning techniques and find out how they relate to NLP. Next, you’ll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You’ll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs’ theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You’ll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.
Table of Contents (14 chapters)

Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP

Natural language processing (NLP) and machine learning (ML) are two fields that have significantly benefited from mathematical concepts, particularly linear algebra and probability theory. These fundamental tools enable the analysis of the relationships between variables, forming the basis of many NLP and ML models. This chapter provides a comprehensive introduction to linear algebra and probability theory, including their practical applications in NLP and ML. The chapter commences with an overview of vectors and matrices and covers essential operations. Additionally, the basics of statistics, required for understanding the concepts and models in subsequent chapters, will be explained. Finally, the chapter introduces the fundamentals of optimization, which are critical for solving NLP problems and understanding the relationships between variables. By the end of this chapter, you will have a solid foundation...