How to Generate Art - Intro to Deep Learning #8

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/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: 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/ Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/