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 concluding chapter of our exploration into the dynamic world of NLP and LLMs, we have had the privilege of engaging with experts across various fields. Their insightful discussions have illuminated intricate developments, legal considerations, operational approaches, regulatory influences, and emerging capabilities of LLMs. Through their expert lenses, we delved into pressing issues such as creating equitable datasets, advancing NLP technologies, navigating privacy protections in research, restructuring organizations around AI, and anticipating breakthroughs in learning paradigms.

The dialogue with these luminaries has underscored a common theme: the intersection of technological innovation with ethical, legal, and organizational considerations. As we ponder strategies to mitigate biases in datasets, envision the future of hybrid learning paradigms, and assess the impact of foundation models on data ownership, it becomes clear that the evolution of NLP and LLMs is...