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Automated Machine Learning with AutoKeras

You're reading from   Automated Machine Learning with AutoKeras Deep learning made accessible for everyone with just few lines of coding

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Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781800567641
Length 194 pages
Edition 1st Edition
Languages
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Author (1):
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Luis Sobrecueva Luis Sobrecueva
Author Profile Icon Luis Sobrecueva
Luis Sobrecueva
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Table of Contents (15) Chapters Close

Preface 1. Section 1: AutoML Fundamentals
2. Chapter 1: Introduction to Automated Machine Learning FREE CHAPTER 3. Chapter 2: Getting Started with AutoKeras 4. Chapter 3: Automating the Machine Learning Pipeline with AutoKeras 5. Section 2: AutoKeras in Practice
6. Chapter 4: Image Classification and Regression Using AutoKeras 7. Chapter 5: Text Classification and Regression Using AutoKeras 8. Chapter 6: Working with Structured Data Using AutoKeras 9. Chapter 7: Sentiment Analysis Using AutoKeras 10. Chapter 8: Topic Classification Using AutoKeras 11. Section 3: Advanced AutoKeras
12. Chapter 9: Working with Multimodal and Multitasking Data 13. Chapter 10: Exporting and Visualizing the Models 14. Other Books You May Enjoy

Chapter 5: Text Classification and Regression Using AutoKeras

In this chapter, we will focus on the use of AutoKeras to work with text (a sequence of words).

In the previous chapter, we saw that there was a specialized type of network suitable for image processing, called a convolutional neural network (CNN). In this chapter, we will see what recurrent neural networks (RNNs) are and how they work. An RNN is a type of neural network that is very suited to working with text.

We will also use a classifier and a regressor to solve text-based tasks. By the end of the chapter, you will have learned how to use AutoKeras to solve a wide variety of problems that are text-based, such as extracting emotions from tweets, detecting spam in emails, and so on.

In this chapter, we will cover the following topics:

  • Working with text data
  • Understanding RNNs—what are these neural networks and how do they work?
  • One-dimensional CNNs (Conv1D)
  • Creating an email spam detector...
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