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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks with Python

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Length 320 pages
Edition 1st Edition
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Authors (4):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Fabrizio Milo Fabrizio Milo
Author Profile Icon Fabrizio Milo
Fabrizio Milo
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning FREE CHAPTER 2. First Look at TensorFlow 3. Using TensorFlow on a Feed-Forward Neural Network 4. TensorFlow on a Convolutional Neural Network 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. GPU Computing 8. Advanced TensorFlow Programming 9. Advanced Multimedia Programming with TensorFlow 10. Reinforcement Learning

Introducing CNNs

In recent years, Deep Neural Networks (DNNs) have contributed a new impetus to research as well as industry and are therefore been used increasingly. A special type of a DNN is a Convolutional Neural Network (CNN), which has been used with great success in image classification problems.

Before diving into the implementation of an image classifier based on CNN, we'll introduce some basic concepts in image recognition, such as feature detection and convolution.

It's 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 5x5 grid of pixels:

Black and white image

Each element of the grid corresponds to a pixel and, in the case of a black and white image, it assumes either a value of 1, which is associated with black color or the value 0, which is associated with...

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