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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

Using convolutional neural network ensembles to improve accuracy

In machine learning, one of the most robust classifiers is, in fact, a meta-classifier, known as an ensemble. An ensemble is comprised of what's known as weak classifiers, predictive models just a tad better than random guessing. However, when combined, they result in a rather robust algorithm, especially against high variance (overfitting). Some of the most famous examples of ensembles we may encounter include Random Forest and Gradient Boosting Machines.

The good news is that we can leverage the same principle when it comes to neural networks, thus creating a whole that's more than the sum of its parts. Do you want to learn how? Keep reading!

Getting ready

This recipe depends on Pillow and tensorflow_docs, which can be easily installed like this:

$> pip install Pillow git+https://github.com/tensorflow/docs

We'll also be using the famous Caltech 101 dataset, available here: http://www...

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