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Mastering NLP from Foundations to LLMs
Mastering NLP from Foundations to LLMs

Mastering NLP from Foundations to LLMs: Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python

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Profile Icon Gazit Profile Icon Meysam Ghaffari
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Mastering NLP from Foundations to LLMs

Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP

Natural language processing (NLP) and machine learning (ML) are two fields that have significantly benefited from mathematical concepts, particularly linear algebra and probability theory. These fundamental tools enable the analysis of the relationships between variables, forming the basis of many NLP and ML models. This chapter provides a comprehensive introduction to linear algebra and probability theory, including their practical applications in NLP and ML. The chapter commences with an overview of vectors and matrices and covers essential operations. Additionally, the basics of statistics, required for understanding the concepts and models in subsequent chapters, will be explained. Finally, the chapter introduces the fundamentals of optimization, which are critical for solving NLP problems and understanding the relationships between variables. By the end of this chapter, you will have a solid foundation...

Introduction to linear algebra

Let’s start by first understanding scalars, vectors, and matrices:

  • Scalars: A scalar is a single numerical value that usually comes from the real domain in most ML applications. Examples of scalars in NLP include the frequency of a word in a text corpus.
  • Vectors: A vector is a collection of numerical elements. Each of these elements can be termed as an entry, component, or dimension, and the count of these components defines the vector’s dimensionality. Within NLP, a vector could hold components related to elements such as word frequency, sentiment ranking, and more. NLP and ML are two domains that have reaped substantial benefits from mathematical disciplines, particularly linear algebra and probability theory. These foundational tools aid in evaluating the correlation between variables and are at the heart of numerous NLP and ML models. This segment presents a detailed primer on linear algebra and probability theory, along...

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...

Basic probability for machine learning

Probability provides information about the likelihood of an event occurring. In this field, there are several key terms that are important to understand:

  • Trial or experiment: An action that results in a certain outcome with a certain likelihood
  • Sample space: This encompasses all potential outcomes of a given experiment
  • Event: This denotes a non-empty portion of the sample space

Therefore, in technical terms, probability is a measure of the likelihood of an event occurring when an experiment is conducted.

In this very simple case, the probability of event A with one outcome is equal to the chance of event A divided by the chance of all possible events. For example, in flipping a fair coin, there are two outcomes with the same chance: heads and tails. The chance of having heads will be 1/(1+1) = ½.

In order to calculate the probability, given an event, A, with n outcomes and a sample space, S, the probability of...

Summary

This chapter was about linear algebra and probability for ML, and it covers the fundamental mathematical concepts that are essential to understanding many machine learning algorithms. The chapter began with a review of linear algebra, covering topics such as matrix multiplication, determinants, eigenvectors, and eigenvalues. It then moved on to discuss probability theory, introducing the basic concepts of random variables and probability distributions. We also covered key concepts in statistical inference, such as maximum likelihood estimation and Bayesian inference.

In the next chapter, we will cover the fundamentals of machine learning for NLP, including topics such as data exploration, feature engineering, selection methods, and model training and validation.

Further reading

Please find the additional reading content as follows:

  • Householder reflection matrix: A Householder reflection matrix, or Householder matrix, is a type of linear transformation utilized in numerical linear algebra due to its computational effectiveness and numerical stability. This matrix is used to perform reflections of a given vector about a plane or hyperplane, transforming the vector so that it only has non-0 components in one specific dimension. The Householder matrix (H) is defined by

<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">H</mml:mi><mml:mi mathvariant="bold"> </mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant="bold"> </mml:mi><mml:mi mathvariant="bold">I</mml:mi><mml:mi mathvariant="bold"> </mml:mi><mml:mo>-</mml:mo><mml:mi mathvariant="bold"> </mml:mi><mml:mn>2</mml:mn><mml:mi mathvariant="bold"> </mml:mi><mml:mi mathvariant="bold">u</mml:mi><mml:mi mathvariant="bold"> </mml:mi><mml:msup><mml:mrow><mml:mi mathvariant="bold">u</mml:mi></mml:mrow><mml:mrow><mml:mi>T</mml:mi></mml:mrow></mml:msup></mml:math>

Here, I is the identity matrix, and u is a unit vector defining the reflection plane.

The main purpose of Householder transformations is to perform QR factorization and to reduce matrices to a tridiagonal or Hessenberg form. The properties of being symmetric and orthogonal make the Householder matrix computationally efficient and numerically stable.

  • Diagonalizable: A matrix is said to be diagonalizable if it can be written in the form <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><mi mathvariant="bold">D</mi><mo>=</mo><msup><mi mathvariant="bold">P</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mi mathvariant="bold">A</mi><mi mathvariant="bold">P</mi></mrow></mrow></math><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><mi mathvariant="bold">D</mi><mo>=</mo><msup><mi mathvariant="bold">P</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup><mi mathvariant="bold">A</mi><mi mathvariant="bold">P</mi></mrow></mrow></math>, where A is the...

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.
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Key benefits

  • Learn how to build Python-driven solutions with a focus on NLP, LLMs, RAGs, and GPT
  • Master embedding techniques and machine learning principles for real-world applications
  • Understand the mathematical foundations of NLP and deep learning designs
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

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.

Who is this book for?

This book is for deep learning and machine learning researchers, NLP practitioners, ML/NLP educators, and STEM students. Professionals working with text data as part of their projects will also find plenty of useful information in this book. Beginner-level familiarity with machine learning and a basic working knowledge of Python will help you get the best out of this book.

What you will learn

  • Master the mathematical foundations of machine learning and NLP Implement advanced techniques for preprocessing text data and analysis Design ML-NLP systems in Python
  • Model and classify text using traditional machine learning and deep learning methods
  • Understand the theory and design of LLMs and their implementation for various applications in AI
  • Explore NLP insights, trends, and expert opinions on its future direction and potential
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Table of Contents

13 Chapters
Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction Chevron down icon Chevron up icon
Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP Chevron down icon Chevron up icon
Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing Chevron down icon Chevron up icon
Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance Chevron down icon Chevron up icon
Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques Chevron down icon Chevron up icon
Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models Chevron down icon Chevron up icon
Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation Chevron down icon Chevron up icon
Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG Chevron down icon Chevron up icon
Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs Chevron down icon Chevron up icon
Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI Chevron down icon Chevron up icon
Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Fon Marile May 23, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Mastering NLP is an invaluable resource for anyone in the NLP field. It provides a thorough grounding in essential math and programming, along with advanced techniques for text classification and LLM system design. With practical python examples and expert insights, its a must-read for NLP mastery and when seeking a reliable guide when tackling real world problems.
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Petar Dimov Jun 21, 2024
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🎉 This comprehensive guide has proved itself to be an invaluable resource!💡 The book captivated me from the first chapter itself. It brilliantly paves the way for everyone from budding learners to industry veterans in the space of NLP and LLMs, and its concise yet comprehensive coverage aligns perfectly with the needs of a wide array of readers.🧠 Gazit and Ghaffari have meticulously crafted this book as a powerful compendium of knowledge. They have intelligently structured the content to segue from fundamental understanding of topics like linear algebra, probability, and statistics to more advanced ML techniques, making it an essential manual of NLP-related learnings.✏️ What sets this book apart is not merely the actionable insight it provides, but how it beautifully combines these insights with real-life applications. The transition from text pre-processing techniques to deep learning models, discussed from Chapter 4 through 6, is skillfully supplemented with Python-based case studies, creating a truly immersive learning experience.🛠 For those fascinated by Large Language Models (LLMs), Chapters 7 to 9 delve into everything from theory and design to sophisticated applications like prompt engineering and RAGs, all of which are brought to life through practical code implementations.🧩 The final chapters (10 and 11) critically analyse the current trends influenced by AI and LLMs, while also making well-researched predictions about the future of this industry, preparing readers to stay ahead of the curve.✨ In summary, "Mastering NLP from Foundations to LLMs" is more than just a book; it is a well-rounded guide for anyone keen to tap into the potential of NLP and LLMs. This guide offers an impressive mix of foundational theories, actionable insights, and future forecasts, all while ensuring reader-friendly delivery. Whether you're a beginner or a professional, this book is a brilliant tool to navigate the complex yet exciting world of AI and LLMs.
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Andrew May 26, 2024
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As a business leader this is a good source for me to refer to. It simplifies complex NLP concepts and also provides practical Python examples to just run and play with.This book empowers leaders by letting better understand what actually happens which feeds into strategic decision-making and driving innovation.Ideally, this would help be competitive in an AI-driven market.
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yifat May 29, 2024
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This NLP book is a powerful resource for job candidates. It directly prepares you for Machine Learning Engineer interviews by covering essential system design concepts, advanced NLP techniques, and practical Python examples.You get the insight you need, so it's worth keeping at your desk.
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Sarbjit Singh Hanjra Oct 18, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"Mastering NLP from Foundations to LLMs", serves as that crucial guide, helping readers chart their course through the expansive field of Natural Language Processing (NLP).From the basics of NLP and machine learning to advanced topics like neural networks, transformers, and LLMs, this book lays out the landscape clearly. It begins by grounding readers in essential mathematical concepts, including linear algebra and probability, before guiding them through data exploration and machine learning models. The book’s strength lies in its structured approach, making even the most complex topics—like text preprocessing, hyperparameter tuning, and ensemble models—easy to digest.The sections dedicated to LLMs are like detailed flight maps for advanced NLP techniques. From designing and integrating LLMs with LangChain and RAG to practical applications using Hugging Face, the book ensures you're well-equipped to navigate these models effectively. Highly recommended for those looking to master NLP, this guide is the perfect roadmap to understanding and leveraging the power of LLMs.
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  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela