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Mastering NLP from Foundations to LLMs

You're reading from   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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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781804619186
Length 340 pages
Edition 1st Edition
Languages
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Authors (2):
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Meysam Ghaffari Meysam Ghaffari
Author Profile Icon Meysam Ghaffari
Meysam Ghaffari
Lior Gazit Lior Gazit
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Lior Gazit
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Toc

Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction 2. Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP FREE CHAPTER 3. Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing 4. Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance 5. Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques 6. Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models 7. Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation 8. Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG 9. Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs 10. Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI 11. Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts 12. Index 13. Other Books You May Enjoy

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

Appear like this.

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