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Advanced Deep Learning with R

You're reading from  Advanced Deep Learning with R

Product type Book
Published in Dec 2019
Publisher Packt
ISBN-13 9781789538779
Pages 352 pages
Edition 1st Edition
Languages
Author (1):
Bharatendra Rai Bharatendra Rai
Profile icon Bharatendra Rai

Table of Contents (20) Chapters

Preface 1. Section 1: Revisiting Deep Learning Basics
2. Revisiting Deep Learning Architecture and Techniques 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

Summary

In this chapter, we illustrated the use of the recurrent neural network model for text sentiment classification using IMDb movie review data. Compared to a regular densely connected network, recurrent neural networks are better suited to deal with data that has sequences in it. Text data is one such example that we worked with in this chapter.

In general, deep networks involve many factors or variables, and this calls for some amount of experimentation involving making changes to the levels for such factors before arriving at a useful model. In this chapter, we also developed five different movie review sentiment classification models.

A variant of recurrent neural networks that has become popular is Long Short-Term Memory (LSTM) networks. LSTM networks are capable of learning long-term dependencies and help recurrent networks remember inputs for a longer time.

In the...

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