Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Natural Language Understanding with Python
  • Table Of Contents Toc
  • Feedback & Rating feedback
Natural Language Understanding with Python

Natural Language Understanding with Python

By : Deborah A. Dahl
4.8 (13)
close
close
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)
close
close
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

Pre-trained models

The most recent approach to NLU is based on the idea that much of the information required to understand natural language can be made available to many different applications by processing generic text (such as internet text) to create a baseline model for the language. Some of these models are very large and are based on tremendous amounts of data. To apply these models to a specific application, the generic model is adapted to the application through the use of application-specific training data, through a process called fine-tuning. Because the baseline model already contains a vast amount of general information about the language, the amount of training data can be considerably less than the training data required for some of the traditional approaches. These popular technologies include BERT and its many variations and Generative Pre-trained Transformers (GPTs) and their variations.

Pre-trained models will be discussed in detail in Chapter 11.

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY