Natural Language Understanding with Python: Building Human-Like Understanding with Large Language Models by Deborah A. Dahl, Deborah A. Dahl

Amazon download books online Natural Language Understanding with Python: Building Human-Like Understanding with Large Language Models by Deborah A. Dahl, Deborah A. Dahl

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Natural Language Understanding with Python: Building Human-Like Understanding with Large Language Models
Deborah A. Dahl, Deborah A. Dahl
Page: 309
Format: pdf, ePub, mobi, fb2
ISBN: 9781804613429
Publisher: Packt Publishing

Download Natural Language Understanding with Python: Building Human-Like Understanding with Large Language Models

Amazon download books online Natural Language Understanding with Python: Building Human-Like Understanding with Large Language Models by Deborah A. Dahl, Deborah A. Dahl

Overview

Unlock the full potential of Natural Language Understanding and create cutting-edge systems by mastering the art of data acquisition and technology selection with this comprehensive guide. How to decide if NLP techniques can help solve a problem How to select NLP techniques for a particular problem Selecting and developing corpora Natural language understanding is the technology that structures text and speech in a way that computers can further process it to perform useful applications. Natural language is ubiquitous and there are still many opportunities for exploring the value that it can add to applications. Developers working with natural language will be able to put their knowledge to work with this practical guide to solving natural language processing problems with Python. Managers will be able to identify areas where natural language understanding can be applied. Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will begin by learning about the kinds of problems that can be addressed with natural language technology tools. You'll learn what natural language understanding is and what kinds of applications it would help achieve. You'll then learn about the current natural language understanding techniques and the Python libraries that can be used in applications. By the end of the book, you will be able to identify natural language understanding problems, acquire data, select appropriate technology and develop natural language understanding systems. The most important skill that readers will acquire is not just HOW to apply natural language techniques, but WHY to select particular techniques. The book will also cover important practical considerations concerning acquiring real data and evaluating real system performance, not just performing textbook evaluations with pre-existing corpora. After reading this book and studying the code, readers will be equipped to build state of the art as well as practical natural language applications to solve real problems. How to develop and fine-tune an NLP application. Maintaining NLP applications after deployment. The target audience are NLP developers who are interested in learning about natural language understanding and applying natural language processing technology to real problems. This audience includes Computational Linguists, Linguists, Data Scientists, NLP Developers, Conversational AI who are interested in addressing natural language problems will find this book useful. Working knowledge in Python is needed to get the best from this guide. Natural Language Understanding,Related Technologies, and Natural Language Applications How to Identify a Practical Natural Language Understanding Problem? Approaches to natural language understanding: rule-based systems, machine learning and deep learning, and considerations for choosing technologies Tools:Selecting libraries and tools for natural language understanding in Python Natural language data: finding and preparing data Exploring and visualizing data Selecting approaches Rule-based techniques Machine Learning 1:Statistical Machine Learning Machine learning 2: neural nets and deep learning Machine learning 3:transformers and pretrained models Applications of unsupervised learning How well does it work:Evaluation What to do if the system isn't working Summary and looking to the future

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