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Natural Language Understanding with Python

Natural Language Understanding with Python

By : Deborah A. Dahl
4.8 (13)
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Natural Language Understanding with Python

Natural Language Understanding with Python

4.8 (13)
By: Deborah A. Dahl

Overview of this book

Natural Language Understanding facilitates the organization and structuring of language allowing computer systems to effectively process textual information for various practical applications. Natural Language Understanding with Python will help you explore practical techniques for harnessing NLU to create diverse applications. with step-by-step explanations of essential concepts and practical examples, you’ll begin by learning about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also discover practical issues such as acquiring data, evaluating systems, and deploying NLU applications along with their solutions. The book is a comprehensive guide that’ll help you explore techniques and resources that can be used for different applications in the future. By the end of this book, you’ll be well-versed with the concepts of natural language understanding, deep learning, and large language models (LLMs) for building various AI-based applications.
Table of Contents (21 chapters)
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1
Part 1: Getting Started with Natural Language Understanding Technology
4
Part 2:Developing and Testing Natural Language Understanding Systems
16
Part 3: Systems in Action – Applying Natural Language Understanding at Scale

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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