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)

Basic probability for machine learning

Probability provides information about the likelihood of an event occurring. In this field, there are several key terms that are important to understand:

  • Trial or experiment: An action that results in a certain outcome with a certain likelihood
  • Sample space: This encompasses all potential outcomes of a given experiment
  • Event: This denotes a non-empty portion of the sample space

Therefore, in technical terms, probability is a measure of the likelihood of an event occurring when an experiment is conducted.

In this very simple case, the probability of event A with one outcome is equal to the chance of event A divided by the chance of all possible events. For example, in flipping a fair coin, there are two outcomes with the same chance: heads and tails. The chance of having heads will be 1/(1+1) = ½.

In order to calculate the probability, given an event, A, with n outcomes and a sample space, S, the probability of...