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Practical Convolutional Neural Networks

You're reading from   Practical Convolutional Neural Networks Implement advanced deep learning models using Python

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788392303
Length 218 pages
Edition 1st Edition
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Authors (3):
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Mohit Sewak Mohit Sewak
Author Profile Icon Mohit Sewak
Mohit Sewak
Md. Rezaul Karim Md. Rezaul Karim
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Md. Rezaul Karim
Pradeep Pujari Pradeep Pujari
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Pradeep Pujari
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Table of Contents (11) Chapters Close

Preface 1. Deep Neural Networks – Overview 2. Introduction to Convolutional Neural Networks FREE CHAPTER 3. Build Your First CNN and Performance Optimization 4. Popular CNN Model Architectures 5. Transfer Learning 6. Autoencoders for CNN 7. Object Detection and Instance Segmentation with CNN 8. GAN: Generating New Images with CNN 9. Attention Mechanism for CNN and Visual Models 10. Other Books You May Enjoy

Building, training, and evaluating our first CNN

In the next section, we will look at how to classify and distinguish between dogs from cats based on their raw images. We will also look at how to implement our first CNN model to deal with the raw and color image having three channels. This network design and implementation are not straightforward; TensorFlow low-level APIs will be used for this. However, do not worry; later in this chapter, we will see another example of implementing a CNN using TensorFlow's high-level contrib API. Before we formally start, a short description of the dataset is a mandate.

Dataset description

For this example, we will use the dog versus cat dataset from Kaggle that was provided for...

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