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)

Technical requirements

To successfully navigate through this chapter, certain technical prerequisites are necessary, as follows:

  • Programming knowledge: A strong understanding of Python is essential, as it’s the primary language used for most DL and NLP libraries.
  • Machine learning fundamentals: A good grasp of basic ML concepts such as training/testing data, overfitting, underfitting, accuracy, precision, recall, and F1 score will be valuable.
  • DL basics: Familiarity with DL concepts and architectures, including neural networks, backpropagation, activation functions, and loss functions, will be essential. Knowledge of RNNs and CNNs would be advantageous but not strictly necessary as we will focus more on transformer architectures.
  • NLP basics: Some understanding of basic NLP concepts such as tokenization, stemming, lemmatization, and word embeddings (such as Word2Vec or GloVe) would be beneficial.
  • Libraries and frameworks: Experience with libraries such...