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)

Large datasets and their indelible mark on NLP and LLMs

The era of big data and the subsequent rise of NLP and LLMs are deeply linked. The transformation of NLP and LLMs into today’s powerful developments cannot be discussed without mentioning the vast datasets that became available. Let’s explore this relationship.

Purpose – training, benchmarking, and domain expertise

At its core, the emergence of large datasets has provided the raw material required to train increasingly sophisticated models. Typically, the larger the dataset, the more comprehensive and diverse the information the model can learn from.

Large datasets not only serve as training grounds but also provide benchmarks for evaluating model performance. This has led to standardized measures, giving researchers clear targets and allowing for apples-to-apples comparisons between models. There is a collection of benchmarks that are common and can be used for evaluating LLMs. One famous and very...