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)

Enhancing LLM performance with RAG and LangChain – a dive into advanced functionalities

The retrieval-augmented generation (RAG) framework has become instrumental in tailoring large language models (LLMs) for specific domains or tasks, bridging the gap between the simplicity of prompt engineering and the complexity of model fine-tuning.

Prompt engineering stands as the initial, most accessible technique for customizing LLMs. It leverages the model’s capacity to interpret and respond to queries based on the input prompt. For example, to inquire if Nvidia surpassed earnings expectations in its latest announcement, directly providing the earnings call content within the prompt can compensate for the LLM’s lack of immediate, up-to-date context. This approach, while straightforward, hinges on the model’s ability to digest and analyze the provided information within a single or a series of carefully crafted prompts.

When the scope of inquiry exceeds what...