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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
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 Regression

In the previous chapter, we worked with a dataset that had a categorical target variable, and we went over the steps for developing a classification model using Keras. In situations where the response variable is numeric, supervised learning problems are categorized as regression problems. In this chapter, we will develop a prediction model for numeric response variables. To illustrate the process of developing the prediction model, we will make use of the Boston Housing dataset, which is available within the mlbench package.

In this chapter, we will cover the following topics:

  • Understanding the Boston Housing dataset
  • Preparing the data
  • Creating and fitting a deep neural network model for regression
  • Model evaluation and prediction
  • Performance optimization tips and best practices
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