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Neural Networks with Keras Cookbook

You're reading from   Neural Networks with Keras Cookbook Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789346640
Length 568 pages
Edition 1st Edition
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Authors (2):
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V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
Srinivas Pradeep Srinivas Pradeep
Author Profile Icon Srinivas Pradeep
Srinivas Pradeep
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Toc

Table of Contents (18) Chapters Close

Preface 1. Building a Feedforward Neural Network 2. Building a Deep Feedforward Neural Network FREE CHAPTER 3. Applications of Deep Feedforward Neural Networks 4. Building a Deep Convolutional Neural Network 5. Transfer Learning 6. Detecting and Localizing Objects in Images 7. Image Analysis Applications in Self-Driving Cars 8. Image Generation 9. Encoding Inputs 10. Text Analysis Using Word Vectors 11. Building a Recurrent Neural Network 12. Applications of a Many-to-One Architecture RNN 13. Sequence-to-Sequence Learning 14. End-to-End Learning 15. Audio Analysis 16. Reinforcement Learning 17. Other Books You May Enjoy

Introduction

In the previous chapters, we identified the optimal weights that result in classifying an image into the right class. The output class of an image can be changed by varying the following:

  • The weights connecting the input to the output layer, while the input pixels remain constant
  • The input pixel values, while the weights remain constant

In this chapter, we will employ these two techniques to generate images.

In the case studies of an adversarial attack, the neural style transfer and DeepDream will leverage the technique of changing the input pixel values. In the techniques involving a Generative Adversarial Network (GAN), we will leverage the technique of changing certain weights that connect input pixel values to the output.

The first three case studies in this chapter will leverage the technique of changing the input pixel values, while the rest leverage a change...

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