We're going to learn how to use deep learning to convert an image into the style of an artist that we choose. We'll go over the history of computer generated art, then dive into the details of how this process works and why deep learning does it so well.
Coding challenge for this video:
https://github.com/llSourcell/How-to-Generate-Art-Demo
Itai's winning code:
https://github.com/etai83/lstm_stock_prediction
Andreas' runner up code:
https://github.com/AndysDeepAbstractions/How-to-Predict-Stock-Prices-Easily-Demo/blob/master/stockdemo.ipynb
More learning resources:
https://harishnarayanan.org/writing/artistic-style-transfer/
https://ml4a.github.io/ml4a/style_transfer/
http://genekogan.com/works/style-transfer/
https://arxiv.org/abs/1508.06576
https://jvns.ca/blog/2017/02/12/neural-style/
Style transfer apps:
http://www.pikazoapp.com/
http://deepart.io/
https://artisto.my.com/
https://prisma-ai.com/
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http://wizards.herokuapp.com/
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https://www.patreon.com/user?u=3191693
Song at the beginning is called Everyday by Carly Comando
jurassic park inception visualization is from http://www.pyimagesearch.com/2015/07/06/bat-country-an-extendible-lightweight-python-package-for-deep-dreaming-with-caffe-and-convolutional-neural-networks/
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