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)

Eigenvalues and eigenvectors

A vector x, belonging to a d × d matrix A, is an eigenvector if it satisfies the equation Ax = λx, where λ represents the eigenvalue associated with the matrix. This relationship delineates the link between matrix A and its corresponding eigenvector x, which can be perceived as the “stretching direction” of the matrix. In the case where A is a matrix that can be diagonalized, it can be deconstructed into a d × d invertible matrix, V, and a diagonal d × d matrix, Δ, such that

<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math" display="block"><mml:mi mathvariant="bold">A</mml:mi><mml:mi mathvariant="bold"> </mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant="bold"> </mml:mi><mml:mi mathvariant="bold">V</mml:mi><mml:mi mathvariant="bold"> </mml:mi><mml:mi mathvariant="bold">Δ</mml:mi><mml:mi mathvariant="bold"> </mml:mi><mml:msup><mml:mrow><mml:mi mathvariant="bold">V</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:math>

The columns of V encompass d eigenvectors, while the diagonal entries of Δ house the corresponding eigenvalues. The linear transformation Ax can be visually understood through a sequence of three operations. Initially, the multiplication of x by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:msup><mml:mrow><mml:mi mathvariant="bold">V</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:math> calculates x’s co-ordinates in a non-orthogonal basis associated with V’s columns. Subsequently, the multiplication of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:msup><mml:mrow><mml:mi mathvariant="bold">V</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:math> x by Δ scales these co-ordinates using...