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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

Preparing the data

We start by changing the name of the BostonHousing data to simply data for ease of use. Independent variables that are of the factor type are then converted to the numeric type using the lapply function.

Note that for this data, the only factor variable is chas; however, for any other dataset with more factor variables, this code will work fine.

Take a look at the following code:

# Converting factor variables to numeric
data <- BostonHousing
data %>% lapply(function(x) as.numeric(as.character(x)))
data <- data.frame(data)

In the preceding code, after converting factor variables to the numeric type, we also change the format of data to data.frame.

Visualizing the neural network

To visualize a neural...

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