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)

NLP and LLMs in the business world

NLP and LLMs are proving themselves to be transformative in the business domain. From improving efficiencies to enabling new business models, NLP’s capabilities have been harnessed to automate mundane tasks, derive insights from data, and provide advanced customer support.

Initially, NLP was mostly restricted to academia and specialized sectors. However, with the rise of digitalization, the explosion of data, and advancements in open source ML, businesses began to recognize its potential. The affordability of computing power and accessibility to vast datasets made the implementation of LLMs feasible for enterprises, allowing for more sophisticated NLP applications. We observed that this transition of NLP into the business world took place from 2018–2019. First, the combination of NLP and traditional ML models for the purpose of limited tasks, such as text classification, began to infiltrate business operations and analytics. In 2019...