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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

Working with the reuter_50_50 dataset

In the previous chapters, when dealing with text data, we made use of data that had already been converted into a sequence of integers for developing deep network models. In this chapter, we will use text data that needs to be converted into a sequence of integers. We will start by reading the data that we will use to illustrate how to develop a text classification deep network model. We will also explore the dataset that we'll use so that we have a better understanding of it.

In this chapter, we will make use of the keras, deepviz, and readtext libraries, as shown in the following code:

# Libraries used
library(keras)
library(deepviz)
library(readtext)

For illustrating the steps involved in developing a convolutional recurrent network model, we will make use of the reuter_50_50 text dataset, which is available from the UCI Machine Learning...

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