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)

Summary

In this enlightening chapter, we embarked on a comprehensive exploration of DL and its remarkable application to text classification tasks through language models. We began with an overview of DL, revealing its profound ability to learn complex patterns from vast amounts of data and its indisputable role in advancing state-of-the-art NLP systems.

We then delved into the transformative world of transformer models, which have revolutionized NLP by providing an effective alternative to traditional RNNs and CNNs for processing sequence data. By unpacking the attention mechanism—a key feature in transformers—we highlighted its capacity to focus on different parts of the input sequence, hence facilitating a better understanding of context.

Our journey continued with an in-depth exploration of the BERT model. We detailed its architecture, emphasizing its pioneering use of bidirectional training to generate contextually rich word embeddings, and we highlighted its...