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The Deep Learning with PyTorch Workshop

You're reading from The Deep Learning with PyTorch Workshop

Product type Book

Published in Jul 2020

Publisher Packt

ISBN-13 9781838989217

Pages 330 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Hyatt Saleh

Table of Contents (8) Chapters

Preface

1. Introduction to Deep Learning and PyTorch

2. Building Blocks of Neural Networks

3. A Classification Problem Using DNN

4. Convolutional Neural Networks

5. Style Transfer

6. Analyzing the Sequence of Data with RNNs

Appendix

Implementation of Style Transfer Using the VGG-19 Network Architecture

VGG-19 is a CNN consisting of 19 layers. It was trained using millions of images from the ImageNet database. The network is capable of classifying images into 1,000 different class labels, including a vast number of animals and different tools.

Note

To explore the ImageNet database, go to the following URL: http://www.image-net.org/.

Considering its depth, the network is able to identify complex features from a wide variety of images, which makes it particularly good for style transfer problems, where feature extraction is crucial at different stages and for different purposes.

This section will focus on how to use the pretrained VGG-19 model to perform style transfer. The end goal of this chapter will be to take an image of an animal or a landscape (as the content image) and one of a painting from a well-known artist (as the style image) to create a new image of a regular object with an artistic...

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Authors (1)

Hyatt Saleh

Hyatt Saleh discovered the importance of data analysis for understanding and solving real-life problems after graduating from college as a business administrator. Since then, as a self-taught person, she not only works as a machine learning freelancer for many companies globally, but has also founded an artificial intelligence company that aims to optimize everyday processes. She has also authored Machine Learning Fundamentals, by Packt Publishing.

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