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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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Colour book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Frequently bought together

Causal Inference and Discovery in Python

€40.99

Natural Language Understanding with Python

€37.99

Modern Generative AI with ChatGPT and OpenAI Models

€37.99

Total € 116.97

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Amazon verified reviews

Kam F Siu Jan 30, 2024

Feefo Verified review

Amazon Customer Aug 01, 2023

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

Om S Jul 17, 2023

"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.

Jeff Weakley Mar 13, 2024

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.

Yiqiao Yin Jul 26, 2023

"Natural Language Understanding with Python" is an insightful and comprehensive guide that extensively elucidates the field of Natural Language Understanding (NLU). It presents a seamless blend of theoretical expositions and practical techniques, making it an indispensable resource for both academics and professionals seeking to understand and apply NLU in real-world scenarios.At the heart of this magnum opus is a desire to equip readers with an in-depth comprehension of NLU and its manifold applications. The book unfolds like a well-scripted narrative, introducing the readers to the fundamental theories of NLU, leading them on an exploration of a wide spectrum of contemporary NLU techniques, and ultimately steering them towards their practical implementation using Python libraries. In this journey, the reader is continually engaged with comprehensive explanations of complex concepts and stimulated by their practical application.One of the salient features of this text is its multi-layered approach to teaching. Not satisfied with only providing the "how", the authors delve into the 'why' behind each technique and tool. This approach gives the readers a discerning understanding of the subject matter, equipping them with the judgment to select appropriate techniques based on unique circumstances and requirements.This book also sets itself apart by addressing practical issues often omitted in academic literature. For instance, it lays significant emphasis on the acquisition of real data and evaluation of real system performance, in contrast to textbook evaluations based on pre-existing corpora. This practical outlook enhances the book’s value significantly, making it a trusted companion for those venturing into the development, fine-tuning, and maintenance of NLU applications.In addition, the book serves as a launchpad into deep learning and large language models (LLMs). By the time the reader concludes the book, they are not only well-versed in the foundational principles of NLU, but also equipped with the skills to work effectively with deep learning techniques and LLMs, further widening their scope in the discipline.Ultimately, "Natural Language Understanding with Python" is a remarkable contribution to the field of NLU. Its real strength lies in its ability to make complex NLU concepts accessible and practical, providing the reader with not only a robust theoretical foundation but also the ability to apply this knowledge to solve real-world problems. In its commitment to presenting a comprehensive, practical, and theoretically rigorous guide to NLU, this book has indeed raised the bar for future works in the field.

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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