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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks and build intelligent systems with Python

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788831109
Length 484 pages
Edition 2nd Edition
Languages
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Authors (2):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. A First Look at TensorFlow FREE CHAPTER 3. Feed-Forward Neural Networks with TensorFlow 4. Convolutional Neural Networks 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. Heterogeneous and Distributed Computing 8. Advanced TensorFlow Programming 9. Recommendation Systems Using Factorization Machines 10. Reinforcement Learning Other Books You May Enjoy Index

Main concepts of CNNs


Recently, Deep Neural Networks (DNNs) have given fresh impetus to research and therefore they are being used widely. CNNs are a special type of DNN, and they have been used with great success in image classification problems. Before diving into the implementation of an image classifier based on CNNs, we'll introduce some basic concepts in image recognition, such as feature detection and convolution.

In computer vision, it is well known that a real image is associated with a grid composed of a high number of small squares called pixels. The following figure represents a black and white image related to a 5×5 grid of pixels:

Figure 1: Pixel view of a black and white image.

Each element in the grid corresponds to a pixel. In the case of a black and white image, a value of 1 is associated with black and a value of 0 is associated with white. Alternatively, for a grayscale image, the allowed values for each grid element are in the range [0, 255], where 0 is associated with...

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