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)

Learning more about large language models

Large language models are a class of ML models that have been trained on a broad range of internet text.

The term “large” in “large language models” refers to the number of parameters that these models have. For example, GPT-3 has 175 billion parameters. These models are trained using self-supervised learning on a large corpus of text, which means they predict the next word in a sentence (such as GPT) or a word based on surrounding words (such as BERT, which is also trained to predict whether a pair of sentences is sequential). Because they are exposed to such a large amount of text, these models learn grammar, facts about the world, reasoning abilities, and also biases in the data they’re trained on.

These models are transformer-based, meaning they leverage the transformer architecture, which uses self-attention mechanisms to weigh the importance of words in input data. This architecture allows these...