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Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

You're reading from   Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide A practical guide to building neural networks using Microsoft's open source deep learning framework

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789802993
Length 208 pages
Edition 1st Edition
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Author (1):
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Willem Meints Willem Meints
Author Profile Icon Willem Meints
Willem Meints
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Table of Contents (9) Chapters Close

Preface 1. Getting Started with CNTK 2. Building Neural Networks with CNTK FREE CHAPTER 3. Getting Data into Your Neural Network 4. Validating Model Performance 5. Working with Images 6. Working with Time Series Data 7. Deploying Models to Production 8. Other Books You May Enjoy

Convolutional neural network architecture

In previous chapters, we've learned how to use regular feed-forward network architectures to build neural networks. In a feed-forward neural network, we assume that there are interactions between the different input features. But we don't make any assumptions about the nature of these interactions. This is, however, not always the right thing to do.

When you work with complex data such as images, a feed-forward neural network won't do a very good job. This comes from the fact that we assume that there's an interaction between the inputs of our network. But we don't account for the fact that they are organized in a spatial way. When you look at the pixels in an image, there's a horizontal and vertical relationship between them. There's also a relationship between the colors in an image and the position...

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