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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 Long Short-Term Memory Network

In the previous chapter, we used a recurrent neural network to develop a movie review sentiment classification model for text data that are characterized by a sequence of words. Long Short-Term Memory (LSTM) neural networks are a special type of Recurrent Neural Networks (RNNs) that are useful with data involving sequences and provide advantages that we will discuss in the next section. This chapter illustrates the steps for using an LSTM neural network for sentiment classification. The steps involved in applying an LSTM network to a business problem may include text data preparation, creating the LSTM model, training the model, and assessing the model performance.

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

  • Why do we use LSTM networks?
  • Preparing text data for model building
  • Creating a long short...
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