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Hands-On Natural Language Processing with PyTorch 1.x

You're reading from   Hands-On Natural Language Processing with PyTorch 1.x Build smart, AI-driven linguistic applications using deep learning and NLP techniques

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781789802740
Length 276 pages
Edition 1st Edition
Languages
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Author (1):
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Thomas Dop Thomas Dop
Author Profile Icon Thomas Dop
Thomas Dop
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Toc

Table of Contents (14) Chapters Close

Preface 1. Section 1: Essentials of PyTorch 1.x for NLP
2. Chapter 1: Fundamentals of Machine Learning and Deep Learning FREE CHAPTER 3. Chapter 2: Getting Started with PyTorch 1.x for NLP 4. Section 2: Fundamentals of Natural Language Processing
5. Chapter 3: NLP and Text Embeddings 6. Chapter 4: Text Preprocessing, Stemming, and Lemmatization 7. Section 3: Real-World NLP Applications Using PyTorch 1.x
8. Chapter 5: Recurrent Neural Networks and Sentiment Analysis 9. Chapter 6: Convolutional Neural Networks for Text Classification 10. Chapter 7: Text Translation Using Sequence-to-Sequence Neural Networks 11. Chapter 8: Building a Chatbot Using Attention-Based Neural Networks 12. Chapter 9: The Road Ahead 13. Other Books You May Enjoy

Chapter 8: Building a Chatbot Using Attention-Based Neural Networks

If you have ever watched any futuristic sci-fi movies, chances are you will have seen a human talk to a robot. Machine-based intelligence has been a long-standing feature in works of fiction; however, thanks to recent advances in NLP and deep learning, conversations with a computer are no longer a fantasy. While we may be many years away from true intelligence, where computers are able to understand the meaning of language in the same way that humans do, machines are at least capable of holding a basic conversation and giving a rudimentary impression of intelligence.

In the previous chapter, we looked at how to construct sequence-to-sequence models to translate sentences from one language into another. A conversational chatbot that is capable of basic interactions works in much the same way. When we talk to a chatbot, our sentence becomes the input to the model. The output is whatever the chatbot chooses to reply...

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