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Mastering spaCy

You're reading from   Mastering spaCy An end-to-end practical guide to implementing NLP applications using the Python ecosystem

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781800563353
Length 356 pages
Edition 1st Edition
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Author (1):
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Duygu Altınok Duygu Altınok
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Duygu Altınok
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: Getting Started with spaCy
2. Chapter 1: Getting Started with spaCy FREE CHAPTER 3. Chapter 2: Core Operations with spaCy 4. Section 2: spaCy Features
5. Chapter 3: Linguistic Features 6. Chapter 4: Rule-Based Matching 7. Chapter 5: Working with Word Vectors and Semantic Similarity 8. Chapter 6: Putting Everything Together: Semantic Parsing with spaCy 9. Section 3: Machine Learning with spaCy
10. Chapter 7: Customizing spaCy Models 11. Chapter 8: Text Classification with spaCy 12. Chapter 9: spaCy and Transformers 13. Chapter 10: Putting Everything Together: Designing Your Chatbot with spaCy 14. Other Books You May Enjoy

References

Here are some references for this chapter:

On voice assistant products:

  • Alexa developer blog: https://developer.amazon.com/blogs/home/tag/Alexa
  • Alexa science blog: https://www.amazon.science/tag/alexa
  • Microsoft's publication on chatbots: https://academic.microsoft.com/search?q=chatbot
  • Google Assistant: https://assistant.google.com/

Keras layers and optimizers:

  • Keras layers: https://keras.io/api/layers/
  • Keras optimizers: https://keras.io/api/optimizers/
  • An overview of optimizers: https://ruder.io/optimizing-gradient-descent/
  • Adam optimizer: https://arxiv.org/abs/1412.6980

Datasets for conversational AI:

  • Taskmaster from Google Research: https://github.com/google-research-datasets/Taskmaster/tree/master/TM-1-2019
  • Simulated Dialogue dataset from Google Research: https://github.com/google-research-datasets/simulated-dialogue
  • Dialog Challenge dataset from Microsoft: https://github.com/xiul-msr/e2e_dialog_challenge...
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