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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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Table of Contents (20) Chapters Close

Preface 1. Section 1: Revisiting Deep Learning Basics FREE CHAPTER
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 LSTM networks for developing a movie review sentiment classification model. One of the problems faced by recurrent neural networks that we used in the previous chapter is that it involves difficulty in capturing long-term dependency that may exist between two words/integers in a sequence of words or integers. Long Short-Term Memory (LSTM) networks are designed to artificially retain long-term memories that are important when dealing with long sentences or a long sequence of integers.

In the next chapter, we will continue to work with text data and explore the use of Convolutional Recurrent Neural Networks (CRNNs), which combine the benefits of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) into a single network. We will illustrate the use of this type of network with the help of an interesting and publicly...

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