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

Throughout this pivotal chapter, we have embarked on an in-depth exploration of the most recent and groundbreaking applications of LLMs, presented through comprehensive Python code examples. We began by unlocking advanced functionalities by using the RAG framework and LangChain, enhancing LLM performance for domain-specific tasks. The journey continued with advanced methods in chains for sophisticated formatting and processing, followed by the automation of information retrieval from diverse web sources. We also tackled the optimization of prompt engineering through prompt compression techniques, significantly reducing API costs. Finally, we ventured into the collaborative potential of LLMs by forming a team of models that work in concert to solve complex problems.

By mastering these topics, you have now acquired a robust set of skills, enabling you to harness the power of LLMs for a variety of applications. These newfound abilities not only prepare you to tackle current...