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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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Retrieving information from various web sources automatically

In this example, we will review how simple it is to leverage LLMs to access the web and extract information. We may wish to research a particular topic, and so we would like to consolidate all the information from a few web pages, several YouTube videos that present that topic, and so on. Such an endeavor can take a while, as the content may be massive. For instance, several YouTube videos can sometimes take hours to review. Often, one doesn’t know how useful the video is until one has watched a significant portion of it.

Another use case is when looking to track various trends in real time. This may include tracking news sources, YouTube videos, and so on. Here, speed is crucial. Unlike the previous example where speed was important to save us personal time, here, speed is necessary for getting our algorithm to be relevant for identifying real-time emerging trends.

In this section, we put together a very simple...

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