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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

Deep Neural Networks for Multi-Class Classification

When developing prediction and classification models, depending on the type of response or target variable, we come across two potential type of problems: the target variable is of categorical type (this is a classification type of problem) or the target variable is of a numeric type (this is a regression type of problem). It has been observed that about 70% of the data belongs to problems arising from classification categories and the remaining 30% are regression problems (here is the reference: https://www.topcoder.com/role-of-statistics-in-data-science/). In this chapter, we will provide steps for applying deep learning neural networks for classification problems. The steps are illustrated using the fetal cardiotocograms, or CTGs.

In this chapter, we will cover the following topics:

  • A brief understanding of the fetal cardiotocogram...
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