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)

References

  • Alter O, Brown PO, Botstein D. (2000) Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A, 97, 10101-6.
  • Golub, G.H., and Van Loan, C.F. (1989) Matrix Computations, 2nd ed. (Baltimore: Johns Hopkins University Press).
  • Greenberg, M. (2001) Differential equations & Linear algebra (Upper Saddle River, N.J. : Prentice Hall).
  • Strang, G. (1998) Introduction to linear algebra (Wellesley, MA : Wellesley-Cambridge Press).
  • Lax, Peter D. Linear algebra and its applications. Vol. 78. John Wiley & Sons, 2007.
  • Dangeti, Pratap. Statistics for machine learning. Packt Publishing Ltd, 2017.
  • DasGupta, Anirban. Probability for statistics and machine learning: fundamentals and advanced topics. New York: Springer, 2011.