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Practical Convolutional Neural Networks

You're reading from  Practical Convolutional Neural Networks

Product type Book
Published in Feb 2018
Publisher Packt
ISBN-13 9781788392303
Pages 218 pages
Edition 1st Edition
Languages
Authors (3):
Mohit Sewak Mohit Sewak
Profile icon Mohit Sewak
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
Pradeep Pujari Pradeep Pujari
Profile icon Pradeep Pujari
View More author details

Table of Contents (11) Chapters

Preface 1. Deep Neural Networks – Overview 2. Introduction to Convolutional Neural Networks 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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