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Hands-On Computer Vision with TensorFlow 2

You're reading from   Hands-On Computer Vision with TensorFlow 2 Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788830645
Length 372 pages
Edition 1st Edition
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Authors (2):
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Eliot Andres Eliot Andres
Author Profile Icon Eliot Andres
Eliot Andres
Benjamin Planche Benjamin Planche
Author Profile Icon Benjamin Planche
Benjamin Planche
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Table of Contents (16) Chapters Close

Preface 1. Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision FREE CHAPTER
2. Computer Vision and Neural Networks 3. TensorFlow Basics and Training a Model 4. Modern Neural Networks 5. Section 2: State-of-the-Art Solutions for Classic Recognition Problems
6. Influential Classification Tools 7. Object Detection Models 8. Enhancing and Segmenting Images 9. Section 3: Advanced Concepts and New Frontiers of Computer Vision
10. Training on Complex and Scarce Datasets 11. Video and Recurrent Neural Networks 12. Optimizing Models and Deploying on Mobile Devices 13. Migrating from TensorFlow 1 to TensorFlow 2 14. Assessments 15. Other Books You May Enjoy

Definition

Introduced in Dropout: A Simple Way to Prevent Neural Networks from Overfitting (JMLR, 2014) by Hinton and his team (who made numerous contributions to deep learning), dropout consists of randomly disconnecting (dropping out) some neurons of target layers at every training iteration. This method thus takes a hyperparameter ratio, , which represents the probability that neurons are being turned off at each training step (usually set between 0.1 and 0.5). The concept is illustrated in Figure 3.13:

Figure 3.13: Dropout represented on a simple neural network (note that dropped-out neurons of layers are randomly chosen in each iteration)

By artificially and randomly impairing the network, this method forces the learning of robust and concurrent features. For instance, as dropout may deactivate the neurons responsible for a key feature, the network has to figure out other significant features in order to reach the same prediction. This has the effect of developing...

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