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)

To get the most out of this book

All the code presented in this book is in the form of a Jupyter notebook. All the code was developed with Python 3.10.X and is expected to work on later versions as well.

Software/hardware covered in the book

Operating system requirements

Access to a Python environment via one of the following:

  • Accessing Google Colab, which is free and easy from any browser on any device (recommended)
  • A local/cloud development environment of Python with the ability to install public packages and access OpenAI’s API

Windows, macOS, or Linux

Sufficient computation resources, as follows:

  • The previously recommended free access to Google Colab includes a free GPU instance
  • If opting to avoid Google Colab, the local/cloud environment should have a GPU for several code examples

As the code examples in this book have a diversified set of use cases, for some of the advanced LLM solutions, you will need an OpenAI account, which will allow an API key.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.