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Natural Language Understanding with Python
Natural Language Understanding with Python

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

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Profile Icon Deborah A. Dahl
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Can$44.99 Can$50.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (13 Ratings)
eBook Jun 2023 326 pages 1st Edition
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Can$44.99 Can$50.99
Paperback
Can$63.99
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Arrow left icon
Profile Icon Deborah A. Dahl
Arrow right icon
Can$44.99 Can$50.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (13 Ratings)
eBook Jun 2023 326 pages 1st Edition
eBook
Can$44.99 Can$50.99
Paperback
Can$63.99
Subscription
Free Trial
eBook
Can$44.99 Can$50.99
Paperback
Can$63.99
Subscription
Free Trial

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Natural Language Understanding with Python

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Key benefits

  • Master NLU concepts from basic text processing to advanced deep learning techniques
  • Explore practical NLU applications like chatbots, sentiment analysis, and language translation
  • Gain a deeper understanding of large language models like ChatGPT

Description

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.

Who is this book for?

This book is for python developers, computational linguists, linguists, data scientists, NLP developers, conversational AI developers, and students looking to learn about natural language understanding (NLU) and applying natural language processing (NLP) technology to real problems. Anyone interested in addressing natural language problems will find this book useful. Working knowledge in Python is a must.

What you will learn

  • Explore the uses and applications of different NLP techniques
  • Understand practical data acquisition and system evaluation workflows
  • Build cutting-edge and practical NLP applications to solve problems
  • Master NLP development from selecting an application to deployment
  • Optimize NLP application maintenance after deployment
  • Build a strong foundation in neural networks and deep learning for NLU

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 30, 2023
Length: 326 pages
Edition : 1st
Language : English
ISBN-13 : 9781804612996
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Product Details

Publication date : Jun 30, 2023
Length: 326 pages
Edition : 1st
Language : English
ISBN-13 : 9781804612996
Category :

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Table of Contents

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

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(13 Ratings)
5 star 76.9%
4 star 23.1%
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Kam F Siu Jan 30, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
Mohamed El-Refaey Aug 18, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Natural language understanding with python is a well-written and organized book, it took you from the basics of NLP to some of the more trendy advanced topics of LLMs and transformers based solutions. In an easy to ready and comprehend way and the code is simple enough to get what it does, given also that the author didn’t leave a chance without giving a thorough explanation for each piece of code.What I also liked about the book, is that the author introduced decent advices and a framework to think about what works/fits and doesn’t fit well in the nlu space, it gives the reader or the nlp practitioner an organized thinking framework before they start solving an NLU problem. I would advice people who are interested to get into this field to read this book in order to get the best value.
Amazon Verified review Amazon
Amazon Customer Aug 01, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book provides a solid foundation in the fundamentals of NLU, including text processing, tokenization, part-of-speech tagging, syntactic parsing, and semantic analysis.What sets this book apart is its emphasis on implementation usingPython. The authors seamlessly integrate theory with hands-on coding examples, allowing readers to apply what they learn in real-world scenarios.The code snippets are well-structured, and the explanations accompanying them are thorough, making it easy for readers to follow along and implement the concepts presented.Additionally, the book covers a wide range of NLU techniques, including sentiment analysis, named entity recognition, topic modeling, and machine translation. Each topic is discussed in detail, with practical examples and step-by-stepinstructions. The authors also provide guidance on selecting the right tools and libraries, such as NLTK and spaCy, to accomplish specific NLU tasks effectively.The combination of clear explanations, practical examples, and Python implementation makes it a truly comprehensive guide. I highly recommend it to anyone looking to unlock the power of natural language understanding.
Amazon Verified review Amazon
Om S Jul 17, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"Natural Language Understanding with Python" is an indispensable resource for those eager to delve into the realm of natural language processing (NLP) and unlock the power of NLU. From foundational concepts to advanced deep learning techniques, this book equips readers with the skills needed to master NLU and apply it to a range of practical applications.The book begins by providing step-by-step explanations of essential NLU concepts and their real-world applications. It covers a wide spectrum of NLU techniques, introducing readers to the most useful Python libraries in the process. Beyond the basics, the book delves into acquiring data, evaluating system performance, and deploying NLU applications effectively, offering practical solutions to common challenges.What sets this book apart is its emphasis on understanding the reasoning behind technique selection, as well as practical considerations for real-world data acquisition and evaluation. Readers will not only learn how to apply natural language techniques but also gain insight into why certain techniques are chosen.By the end of the book, readers will have a strong grasp of NLU, deep learning, and large language models. Armed with this knowledge, they will be well-prepared to independently develop state-of-the-art NLU applications and address real-world natural language problems.
Amazon Verified review Amazon
Jeff Weakley Mar 13, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As an Emmy-winning ad writer, I’ve been interested in NLP (Natural Language Processing) from jump – like ten years ago. My vision is that ad creatives (especially writers) mine data themselves and create directly from it instead of having it filtered through a 3rd party: a planner, a researcher, an account person, etc.While NLP is primarily about grammar, structure, and syntax, NLU is more about understanding context, intent, and meaning. In other words, how exactly do ChatGPT, Chatbots, et al. understand what the hell you’re talking about?“Natural Language Understanding with Python” by Deborah Dahl takes you from the most basic stuff to the most advanced stuff without having to spend a decade figuring it out on your own. (From stuff like Classification, Sentiment analysis, Spam detection, and Fake news to advanced uses of Transformers models.) Along the way, you can easily see how you can use it to amplify pretty much whatever career path you’ve chosen. It’s also written in an easy-to-understand way – which makes sense, well, given the title.If you were to use NLU to run Sentiment Analysis on my review, you would see that it’s muy, muy positive. Highly recommend.
Amazon Verified review Amazon
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