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Advanced Deep Learning with R

You're reading from   Advanced Deep Learning with R Become an expert at designing, building, and improving advanced neural network models using R

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789538779
Length 352 pages
Edition 1st Edition
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Author (1):
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Bharatendra Rai Bharatendra Rai
Author Profile Icon Bharatendra Rai
Bharatendra Rai
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Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Revisiting Deep Learning Basics
2. Revisiting Deep Learning Architecture and Techniques FREE CHAPTER 3. Section 2: Deep Learning for Prediction and Classification
4. Deep Neural Networks for Multi-Class Classification 5. Deep Neural Networks for Regression 6. Section 3: Deep Learning for Computer Vision
7. Image Classification and Recognition 8. Image Classification Using Convolutional Neural Networks 9. Applying Autoencoder Neural Networks Using Keras 10. Image Classification for Small Data Using Transfer Learning 11. Creating New Images Using Generative Adversarial Networks 12. Section 4: Deep Learning for Natural Language Processing
13. Deep Networks for Text Classification 14. Text Classification Using Recurrent Neural Networks 15. Text classification Using Long Short-Term Memory Network 16. Text Classification Using Convolutional Recurrent Neural Networks 17. Section 5: The Road Ahead
18. Tips, Tricks, and the Road Ahead 19. Other Books You May Enjoy

Text Classification Using Recurrent Neural Networks

Recurrent neural networks are useful for solving problems where data involves sequences. Some examples of applications involving sequences are seen in text classification, time series prediction, the sequence of frames in videos, DNA sequences, and speech recognition.

In this chapter, we will develop a sentiment (positive or negative) classification model using a recurrent neural network. We will begin by preparing the data for developing the text classification model, followed by developing a sequential model, compiling the model, fitting the model, evaluating the model, prediction, and model performance assessment using a confusion matrix. We will also review some tips for sentiment classification performance optimization.

More specifically, in this chapter, we will cover the following topics:

  • Preparing data for model building...
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