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Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs

By : Gazit, Meysam Ghaffari
4.9 (24)
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Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs

4.9 (24)
By: 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)
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Motivations for developing and using LLMs

The motivation to develop and use LLMs arises from several factors related to the capabilities of these models, and the potential benefits they can bring in diverse applications. The following subsections detail a few of these key motivations.

Improved performance

LLMs, when trained with sufficient data, generally demonstrate better performance compared to smaller models. They are more capable of understanding context, identifying nuances, and generating coherent and contextually relevant responses. This performance gain applies to a wide range of tasks in NLP, including text classification, named entity recognition, sentiment analysis, machine translation, question answering, and text generation. As shown in Table 7.1, the performance of BERT – one of the first well-known LLMs – and GPT is compared to the previous models on the General Language Understanding Evaluation (GLUE) benchmark. The GLUE benchmark is a collection...

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