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)

Setting up an LLM application – local open source models

Now, we shall touch on the complementary approach to a closed source implementation, that is, an open source, local implementation.

We will see how you can achieve a similar functional outcome to the one we reviewed in the previous section, without having to register for an account, pay, or share prompts that contain possibly sensitive information with a third-party vendor, such as OpenAI.

About the different aspects that distinguish between open source and closed source

When selecting between open source LLMs, such as LLaMA and GPT-J, and closed source, API-based models such as OpenAI’s GPT, several critical factors must be considered.

Firstly, cost is a major factor. Open source LLMs often have no licensing fees, but they require significant computational resources for training and inference, which can be expensive. Closed source models, while potentially carrying a subscription or pay-per-use fee...