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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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How LLMs stand out

LLMs, such as GPT-3 and GPT-4, are simply LMs that are trained on a very large amount of text and have a very large number of parameters. The larger the model (in terms of parameters and training data), the more capable it is of understanding and generating complex and varied texts. Here are some key ways in which LLMs differ from smaller LMs:

  • Data: LLMs are trained on vast amounts of data. This allows them to learn from a wide range of linguistic patterns, styles, and topics.
  • Parameters: LLMs have a huge number of parameters. Parameters in an ML model are the parts of the model that are learned from the training data. The more parameters a model has, the more complex patterns it can learn.
  • Performance: Because they’re trained on more data and have more parameters, LLMs generally perform better than smaller ones. They’re capable of generating more coherent and diverse texts, and they’re better at understanding context, making...

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