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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

Concept and hyperparameters

These pooling layers are a bit peculiar because they do not have any trainable parameters. Each neuron simply takes the values in its window (the receptive field) and returns a single output, computed from a predefined function. The two most common pooling methods are max-pooling and average-pooling. Max-pooling layers return only the maximum value at each depth of the pooled area (refer to Figure 3.5), and average-pooling layers compute the average at each depth of the pooled area (refer to Figure 3.6).

Pooling layers are commonly used with a stride value equal to the size of their window/kernel size, in order to apply the pooling function over non-overlapping patches. Their purpose is to reduce the spatial dimensionality of the data, cutting down the total number of parameters needed in the network, as well as its computation time. For instance, a pooling layer with a 2 × 2 window size and stride of 2 (that is, k = 2 and...

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