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)

Understanding deep learning basics

In this part, we explain what neural network and deep neural networks are, what is the motivation for using them, and the different types (architectures) of deep learning models.

What is a neural network?

Neural networks are a subfield of artificial intelligence (AI) and ML that focuses on algorithms inspired by the structure and function of the brain. It is also known as “deep” learning because these neural networks often consist of many repetitive layers, creating a deep architecture.

These DL models are capable of “learning” from large volumes of complex, high-dimensional, and unstructured data. The term “learning” refers to the ability of the model to automatically learn and improve from experience without being explicitly programmed to do so for any one particular task of the tasks it learns.

DL can be supervised, semi-supervised, or unsupervised. It’s used in numerous applications...