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Natural Language Understanding with Python

You're reading from  Natural Language Understanding with Python

Product type Book
Published in Jun 2023
Publisher Packt
ISBN-13 9781804613429
Pages 326 pages
Edition 1st Edition
Languages
Author (1):
Deborah A. Dahl Deborah A. Dahl
Profile icon Deborah A. Dahl

Table of Contents (21) Chapters

Preface Part 1: Getting Started with Natural Language Understanding Technology
Chapter 1: Natural Language Understanding, Related Technologies, and Natural Language Applications Chapter 2: Identifying Practical Natural Language Understanding Problems Part 2:Developing and Testing Natural Language Understanding Systems
Chapter 3: Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning Chapter 4: Selecting Libraries and Tools for Natural Language Understanding Chapter 5: Natural Language Data – Finding and Preparing Data Chapter 6: Exploring and Visualizing Data Chapter 7: Selecting Approaches and Representing Data Chapter 8: Rule-Based Techniques Chapter 9: Machine Learning Part 1 – Statistical Machine Learning Chapter 10: Machine Learning Part 2 – Neural Networks and Deep Learning Techniques Chapter 11: Machine Learning Part 3 – Transformers and Large Language Models Chapter 12: Applying Unsupervised Learning Approaches Chapter 13: How Well Does It Work? – Evaluation Part 3: Systems in Action – Applying Natural Language Understanding at Scale
Chapter 14: What to Do If the System Isn’t Working Chapter 15: Summary and Looking to the Future Index Other Books You May Enjoy

Cloud-based LLMs

Recently, there have been a number of cloud-based pretrained large language models that have shown very impressive performance because they have been trained on very large amounts of data. In contrast to BERT, they are too large to be downloaded and used locally. In addition, some are closed and proprietary and can’t be downloaded for that reason. These newer models are based on the same principles as BERT, and they have shown a very impressive performance. This impressive performance is due to the fact that these models have been trained with much larger amounts of data than BERT. Because they cannot be downloaded, it is important to keep in mind that they aren’t appropriate for every application. Specifically, if there are any privacy or security concerns regarding the data, it may not be a good idea to send it to the cloud for processing. Some of these systems are GPT-2, GPT-3, GPT-4, ChatGPT, and OPT-175B, and new LLMs are being published on a frequent...

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