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)

Summary

In this chapter, we embarked on a comprehensive journey through the key trends shaping the world of AI, with a particular emphasis on LLMs. At the very heart of these models lies computational power, which acts as the driving engine, enabling breakthroughs and amplifying their potential. With advancements in computational capabilities, we’re not only progressing faster but also unlocking new efficiencies that redefine the realm of possibilities.

Complementing this computational prowess are vast datasets, casting an indelible mark on NLP and LLMs. We have covered their significance in this chapter and learned that they serve pivotal roles. As we look ahead, the future of data availability in NLP promises to be a dynamic landscape, constantly evolving in response to these challenges.

LLMs themselves have undergone significant evolution; each iteration aimed at achieving greater scale and capability. We reviewed the impact these models possess and learned that they...