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)

Challenges of using GPT-3

Despite its impressive capabilities, GPT-3 also presents some challenges. Due to its large size, it requires substantial computational resources to train. It can sometimes generate incorrect or nonsensical responses, and it can reflect biases present in the training data. It also struggles with tasks that require a deep understanding of the world or common sense reasoning beyond what can be learned from text.

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

In this section, we are going to work on a real-world problem and see how we can use an NLP pipeline to solve it. The code for this part is shared as a Google Colab notebook at Ch6_Text_Classification_DL.ipynb.

The business objective

In this scenario, we are in the healthcare sector. Our objective is to develop a general medical knowledge engine that is very up to date with recent findings in the world of healthcare.

The technical objective

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