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)

What are LLMs and how are they different from LMs?

An LM is a type of ML model that is trained to predict the next word (or character or subword, depending on the granularity of the model) in a sequence, given the words that came before it (or in some models, the surrounding words). It’s a probabilistic model that is capable of generating text that follows a certain linguistic style or pattern.

Before the advent of Transformer-based models such as generative pretrained Transformers (GPTs) and Bidirectional Encoder Representations from Transformers (BERT), there were several other types of LMs widely used in NLP tasks. The following subsections discuss a few of them.

n-gram models

These are some of the simplest LMs. An n-gram model uses the (n-1) previous words to predict the nth word in a sentence. For example, in a bigram (2-gram) model, we would use the previous word to predict the next word. These models are easy to implement and computationally efficient, but they...