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)

Text Classification Reimagined: Delving Deep into Deep Learning Language Models

In this chapter, we delve into the realm of deep learning (DL) and its application in natural language processing (NLP), specifically focusing on the groundbreaking transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) and generative pretrained transformer (GPT). We begin by introducing the fundamentals of DL, elucidating its powerful capability to learn intricate patterns from large amounts of data, making it the cornerstone of state-of-the-art NLP systems.

Following this, we delve into transformers, a novel architecture that has revolutionized NLP by offering a more effective method of handling sequence data compared to traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs). We unpack the transformer’s unique characteristics, including its attention mechanisms, which allow it to focus on different parts of the input sequence...