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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
Author Profile Icon Duygu Altınok
Duygu Altınok
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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

Text classification with spaCy and Keras

In this section, we will learn about methods for blending spaCy with neural networks using another very popular Python deep learning library, TensorFlow, and its high-level API, Keras.

Deep learning is a broad family of machine learning algorithms that are based on neural networks. Neural networks are human brain-inspired algorithms that contain connected layers, which are made from neurons. Each neuron is a mathematical operation that takes its input, multiplies it by its weights, and then passes the sum through the activation function to the other neurons. The following diagram shows a neural network architecture with three layers -- the input layer, hidden layer, and output layer:

Figure 8.8 – A neural network architecture with three layers

TensorFlow is an end-to-end open source platform for machine learning. TensorFlow might be the most popular deep learning library among research engineers and scientists...

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