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

As the world of NLP and LLMs continues to grow rapidly, so do the various practices of system design. In this chapter, we reviewed the design process of LLM applications and pipelines. We discussed the components of these approaches, touching on both API-based closed source and local open source solutions. We then gave you hands-on experience with code.

We later delved deeper into the system design process and introduced LangChain. We reviewed what LangChain comprises and experimented with an example pipeline in code.

To complement the system design process, we surveyed leading cloud services that allow you to experiment, develop, and deploy LLM-based solutions.

In the next chapter, we’ll focus on particular practical use cases, accompanied with code.