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TensorFlow 2.0 Computer Vision Cookbook

You're reading from   TensorFlow 2.0 Computer Vision Cookbook Implement machine learning solutions to overcome various computer vision challenges

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781838829131
Length 542 pages
Edition 1st Edition
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Author (1):
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Jesús Martínez Jesús Martínez
Author Profile Icon Jesús Martínez
Jesús Martínez
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Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Getting Started with TensorFlow 2.x for Computer Vision 2. Chapter 2: Performing Image Classification FREE CHAPTER 3. Chapter 3: Harnessing the Power of Pre-Trained Networks with Transfer Learning 4. Chapter 4: Enhancing and Styling Images with DeepDream, Neural Style Transfer, and Image Super-Resolution 5. Chapter 5: Reducing Noise with Autoencoders 6. Chapter 6: Generative Models and Adversarial Attacks 7. Chapter 7: Captioning Images with CNNs and RNNs 8. Chapter 8: Fine-Grained Understanding of Images through Segmentation 9. Chapter 9: Localizing Elements in Images with Object Detection 10. Chapter 10: Applying the Power of Deep Learning to Videos 11. Chapter 11: Streamlining Network Implementation with AutoML 12. Chapter 12: Boosting Performance 13. Other Books You May Enjoy

Chapter 4: Enhancing and Styling Images with DeepDream, Neural Style Transfer, and Image Super-Resolution

Although deep neural networks excel in traditional computer vision tasks for purely practical applications, they have a fun side too! As we'll discover in this chapter, we can unlock the artistic side of deep learning with the help of a little bit of cleverness and math, of course!

We'll start this chapter by covering DeepDream, an algorithm used to make neural networks produce dream-like images. Next, we'll seize the power of transfer learning to apply the style of famous paintings to our own images (this is known as Neural Style Transfer). Finally, we'll close with Image Super-Resolution, a deep learning approach that's used to improve the quality of an image.

In this chapter, we will cover the following recipes:

  • Implementing DeepDream
  • Generating your own dreamy images
  • Implementing Neural Style Transfer
  • Applying style transfer...
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