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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

Creating New Images Using Generative Adversarial Networks

This chapter illustrates the application of generative adversarial networks (GANs) for generating new images using a practical example. So far in this book, using image data, we have illustrated the use of deep networks for image classification tasks. However, in this chapter, we will explore an interesting and popular approach that helps create new images. Generative adversarial networks have been applied for generating new images, improving image quality, and generating new text and new music. Another interesting application of GANs is in the area of anomaly detection. Here, a GAN is trained to generate data that is considered normal. When this network is used for reconstructing data that is considered not normal or anomalous, the differences in results can help us detect the presence of an anomaly. We will look at an...

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