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)

Reviewing our use case – ML system design for NLP classification in a Jupyter Notebook

In this section, we will walk through a hands-on example. We will follow the steps we presented previously for articulating the problem, designing the solution, and evaluating the results. This section portrays the process that an ML developer goes through when working on a typical project in the industry. Refer to the notebook at https://colab.research.google.com/drive/1ZG4xN665le7X_HPcs52XSFbcd1OVaI9R?usp=sharing for more information.

The business objective

In this scenario, we are working for a financial news agency. Our objective is to publish news about companies and products in real time.

The technical objective

The CTO derives several technical objectives from the business objective. One objective is for the ML team: given a stream of financial tweets in real time, detect those tweets that discuss information about companies or products.

The pipeline

Let’s review...