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)

Demystifying Large Language Models: Theory, Design, and Langchain Implementation

In this chapter, we delve deep into the intricate world of large language models (LLMs) and the underpinning mathematical concepts that fuel their performance. The advent of these models has revolutionized the field of natural language processing (NLP), offering unparalleled proficiency in understanding, generating, and interacting with human language.

LLMs are a subset of artificial intelligence (AI) models that can understand and generate human-like text. They achieve this by being trained on a diverse range of internet text, thus learning an extensive array of facts about the world. They also learn to predict what comes next in a piece of text, which enables them to generate creative, fluent, and contextually coherent sentences.

As we explore the operations of LLMs, we will introduce the key metric of perplexity, a measurement of uncertainty that is pivotal in determining the performance of these...