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)

Language models

A language model is a statistical model in NLP that is designed to learn and understand the structure of human language. More specifically, it is a probabilistic model that is trained to estimate the likelihood of words when provided with a given word scenario. For instance, a language model could be trained to predict the next word in a sentence, given the previous words.

Language models are fundamental to many NLP tasks. They are used in machine translation, speech recognition, part-of-speech tagging, and named entity recognition, among other things. More recently, they have been used to create conversational AI models such as chatbots and personal assistants and to generate human-like text.

Traditional language models were often based on explicitly statistical methods, such as n-gram models, which consider only the previous n words when predicting the next word, or hidden Markov models (HMMs).

More recently, neural networks have become popular for creating...