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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

Implementing Neural Style Transfer

Creativity and artistic expression are not traits that we tend to associate with deep neural networks and AI in general. However, did you know that with the right tweaks, we can turn pre-trained networks into impressive artists, capable of applying the distinctive style of famous painters such as Monet, Picasso, and Van Gogh to our mundane pictures?

This is exactly what Neural Style Transfer does. By the end of this recipe, we'll have the artistic prowess of any painter at our disposal!

Getting ready

We don't need to install any libraries or bring in extra resources to implement Neural Style Transfer. However, because this is a hands-on recipe, we won't detail the inner workings of our solution extensively. If you're interested in the ins and outs of Neural Style Transfer, I recommend that you read the original paper here: https://arxiv.org/abs/1508.06576.

I hope you're ready because we are about to begin!

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