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)

Topic modeling – a particular use case of unsupervised text classification

Topic modeling is an unsupervised ML technique that’s used to discover abstract topics or themes within a large collection of documents. It assumes that each document can be represented as a mixture of topics, and each topic is represented as a distribution over words. The goal of topic modeling is to find the underlying topics and their word distributions, as well as the topic proportions for each document.

There are several topic modeling algorithms, but one of the most popular and widely used is LDA. We will discuss LDA in detail, including its mathematical formulation.

LDA

LDA is a generative probabilistic model that assumes the following generative process for each document:

  1. Choose the number of words in the document.
  2. Choose a topic distribution (θ) for the document from a Dirichlet distribution with parameter α.
  3. For each word in the document, do the following...