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)

Introduction to linear algebra

Let’s start by first understanding scalars, vectors, and matrices:

  • Scalars: A scalar is a single numerical value that usually comes from the real domain in most ML applications. Examples of scalars in NLP include the frequency of a word in a text corpus.
  • Vectors: A vector is a collection of numerical elements. Each of these elements can be termed as an entry, component, or dimension, and the count of these components defines the vector’s dimensionality. Within NLP, a vector could hold components related to elements such as word frequency, sentiment ranking, and more. NLP and ML are two domains that have reaped substantial benefits from mathematical disciplines, particularly linear algebra and probability theory. These foundational tools aid in evaluating the correlation between variables and are at the heart of numerous NLP and ML models. This segment presents a detailed primer on linear algebra and probability theory, along...