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.

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Table of Contents (14 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Reviews
Share Your Thoughts
Download a free PDF copy of this book
Free Chapter
1
Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction
Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction
Who this book is for
What is natural language processing?
Initial strategies in the machine processing of natural language
A winning synergy – the coming together of NLP and ML
Introduction to math and statistics in NLP
Summary
Questions and answers
2
Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP
Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP
Introduction to linear algebra
Eigenvalues and eigenvectors
Basic probability for machine learning
Summary
Further reading
References
3
Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing
Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing
Technical requirements
Data exploration
Common machine learning models
Model underfitting and overfitting
Splitting data
Hyperparameter tuning
Ensemble models
Handling imbalanced data
Dealing with correlated data
Summary
References
4
Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance
Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance
Technical requirements
Lowercasing in NLP
Removing special characters and punctuation
NER
POS tagging
Explaining the preprocessing pipeline
Summary
5
Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques
Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques
Technical requirements
Types of text classification
Text classification using TF-IDF
Text classification using Word2Vec
Topic modeling – a particular use case of unsupervised text classification
Reviewing our use case – ML system design for NLP classification in a Jupyter Notebook
Summary
6
Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models
Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models
Technical requirements
Understanding deep learning basics
The architecture of different neural networks
The challenges of training neural networks
Language models
Understanding transformers
Learning more about large language models
The challenges of training language models
Challenges of using GPT-3
Summary
7
Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation
Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation
Technical requirements
What are LLMs and how are they different from LMs?
How LLMs stand out
Motivations for developing and using LLMs
Challenges in developing LLMs
Different types of LLMs
Example designs of state-of-the-art LLMs
Summary
References
8
Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG
Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG
Technical requirements
Setting up an LLM application – API-based closed source models
Prompt engineering and priming GPT
Setting up an LLM application – local open source models
Employing LLMs from Hugging Face via Python
Exploring advanced system design – RAG and LangChain
Reviewing a simple LangChain setup in a Jupyter notebook
LLMs in the cloud
Summary
9
Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs
Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs
Technical requirements
Enhancing LLM performance with RAG and LangChain – a dive into advanced functionalities
Advanced methods with chains
Retrieving information from various web sources automatically
Prompt compression and API cost reduction
Multiple agents – forming a team of LLMs that collaborate
Summary
10
Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI
Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI
Key technical trends around LLMs and AI
Large datasets and their indelible mark on NLP and LLMs
Evolution of large language models – purpose, value, and impact
NLP and LLMs in the business world
Behavioral trends induced by AI and LLMs – the social aspect
Summary
11
Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts
Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts
Overview of our experts
Nitzan Mekel-Bobrov, PhD
David Sontag, PhD
John D. Halamka, M.D., M.S.
Xavier Amatriain, PhD
Melanie Garson, PhD
Our questions and the experts’ answers
Summary
12
Index
Index
Why subscribe?
13
Other Books You May Enjoy
Other Books You May Enjoy
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Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Now, we add a feature for achieving the syntax. We define the output_parser variable, and we use a different function for generating the output, predict_and_parse().”

A block of code is set as follows:

import pandas as pd
import matplotlib.pyplot as plt
# Load the record dict from URL
import requests
import pickle

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

qa_engineer (to manager_0):
exitcode: 0 (execution succeeded)
Code output:
Figure(640x480)
programmer (to manager_0):
TERMINATE

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “While we chose one particular database, you can refer to the Vector Store page to read more about the different choices.”

Tips or important notes

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