You're reading from Natural Language Understanding with Python Combine natural language technology, deep learning, and large language models to create human-like language comprehension in computer systems

Product type Paperback

Published in Jun 2023

Publisher Packt

ISBN-13 9781804613429

Length 326 pages

Edition 1st Edition

Languages

Python

Tools

Combine

Concepts

Deep Learning

Author (1):

Deborah A. Dahl

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Table of Contents (21) Chapters

Preface

1. Part 1: Getting Started with Natural Language Understanding Technology

2. Chapter 1: Natural Language Understanding, Related Technologies, and Natural Language Applications FREE CHAPTER

3. Chapter 2: Identifying Practical Natural Language Understanding Problems

4. Part 2:Developing and Testing Natural Language Understanding Systems

5. Chapter 3: Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning

6. Chapter 4: Selecting Libraries and Tools for Natural Language Understanding

7. Chapter 5: Natural Language Data – Finding and Preparing Data

8. Chapter 6: Exploring and Visualizing Data

9. Chapter 7: Selecting Approaches and Representing Data

10. Chapter 8: Rule-Based Techniques

11. Chapter 9: Machine Learning Part 1 – Statistical Machine Learning

12. Chapter 10: Machine Learning Part 2 – Neural Networks and Deep Learning Techniques

13. Chapter 11: Machine Learning Part 3 – Transformers and Large Language Models

14. Chapter 12: Applying Unsupervised Learning Approaches

15. Chapter 13: How Well Does It Work? – Evaluation

16. Part 3: Systems in Action – Applying Natural Language Understanding at Scale

17. Chapter 14: What to Do If the System Isn’t Working

18. Chapter 15: Summary and Looking to the Future

19. Index

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20. Other Books You May Enjoy

To get the most out of this book

The code for this book is provided in the form of Jupyter Notebooks. To run the notebooks, you should have a comfortable understanding of coding in Python and be familiar with some basic libraries. Additionally, you’ll need to install the required packages.

The easiest way to install them is by using Pip, a great package manager for Python. If Pip is not yet installed on your system, you can find the installation instructions here: https://pypi.org/project/pip/.

Working knowledge of the Python programming language will assist with understanding the key concepts covered in this book. The examples in this book don’t require GPUs and can run on CPUs, although some of the more complex machine learning examples would run faster on a computer with a GPU.

The code for this book has only been tested on Windows 11 (64-bit).

Software/hardware used in the book	Operating system requirements
Basic platform tools
Python 3.9	Windows, macOS, or Linux
Jupyter Notebooks
pip
Natural Language Processing and Machine Learning
NLTK	Windows, macOS, or Linux
spaCy and displaCy
Keras
TensorFlow
Scikit-learn
Graphing and visualization
Matplotlib	Windows, macOS, or Linux
Seaborn

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Table of Contents (21) Chapters

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Table of Contents (21) Chapters

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