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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

Mathematical model

Inspired by its biological counterpart (represented in Figure 1.11), the artificial neuron takes several inputs (each a number), sums them together, and finally applies an activation function to obtain the output signal, which can be passed to the following neurons in the network (this can be seen as a directed graph):

Figure 1.11: On the left, we can see a simplified biological neuron. On the right, we can see its artificial counterpart

The summation of the inputs is usually done in a weighted way. Each input is scaled up or down, depending on a weight specific to this particular input. These weights are the parameters that are adjusted during the training phase of the network in order for the neuron to react to the correct features. Often, another parameter is also trained and used for this summation process—the neuron's bias. Its value is simply added to the weighted sum as an offset.

Let's quickly formalize this process mathematically. Suppose...

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