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)

Questions and answers

  1. What is natural language processing (NLP)?
    • Q: What defines NLP in the field of artificial intelligence?
    • A: NLP is a subfield of AI focused on enabling computers to understand, interpret, and generate human language in a way that is both natural and meaningful to human users.
  2. Initial strategies in machine processing of natural language.
    • Q: What is the importance of preprocessing in NLP?
    • A: Preprocessing, including tasks such as removing stop words and applying stemming or lemmatization, is crucial for cleaning and preparing text data, thereby improving the performance of machine learning algorithms on NLP tasks.
  3. The synergy of NLP and machine learning (ML).
    • Q: How does machine learning contribute to advancements in NLP?
    • A: ML, especially techniques such as statistical language modeling and deep learning, drives NLP forward by enabling algorithms to learn from data, predict word sequences, and perform tasks such as language understanding and sentiment analysis more effectively.
  4. Introduction to math and statistics in NLP
    • Q: Why are mathematical foundations important in NLP?
    • A: Mathematical foundations such as linear algebra, statistics, and probability are essential for understanding and developing the algorithms that underpin NLP techniques, from basic preprocessing to complex model training.
  5. Advancements in NLP – the role of pre-trained language models
    • Q: How have pre-trained models such as BERT and GPT influenced NLP?
    • A: Pre-trained models, trained on vast amounts of text data, can be fine-tuned for specific tasks such as sentiment analysis or language translation, significantly simplifying the development of NLP applications and enhancing task performance.
  6. Understanding transformers in language models
    • Q: Why are transformers considered a breakthrough in NLP?
    • A: Transformers process words in parallel and use attention mechanisms to understand word context within sentences, significantly improving a model’s ability to handle the complexities of human language.