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)

Evolution of large language models – purpose, value, and impact

The rise and development of LLMs stand as a testament to our relentless pursuit of more advanced algorithms. These giant computational linguistics models have come a long way from their initial incarnations, growing not only in size but also in capabilities. As we delve into the purpose, value, and impact of these formidable tools, it becomes clear that their evolution is closely intertwined with our aspiration to harness the true potential of machine-driven communication and cognition.

Purpose – why the push for bigger and better LLMs?

The rationale behind the development of LLMs revolves around the quest to bridge the gap between human and machine communication, where human language is to be fed into a machine for downstream processing. As the digital age began, the need for fluid, context-aware, and intelligent systems that could grasp human language with nuanced understanding became apparent. As...