How to Generate Images - Intro to Deep Learning #14

We're going to build a variational autoencoder capable of generating novel images after being trained on a collection of images. We'll be using handwritten digit images as training data. Then we'll both generate new digits and plot out the learned embeddings. And I introduce Bayesian theory for the first time in this series :) Code for this video: https://github.com/llSourcell/how_to_generate_images Mike's Winning Code: https://github.com/xkortex/how_to_win_slot_machines/blob/master/WallStBandits.ipynb SG's Runner up Code: https://github.com/esha-sg/Intro-DeepLearning-Siraj-Week13 Please subscribe! And like. And comment. That's what keeps me going. 2 things -The embedding visualization at the end would be more spread out if i trained it for more epochs (50 is recommended) but i just used 5. -The code in the video doesn't fully implement the reparameterization trick (to save space) but check the GitHub repo for details on that. More Learning resources: https://jaan.io/what-is-variational-autoencoder-vae-tutorial/ http://kvfrans.com/variational-autoencoders-explained/ http://blog.fastforwardlabs.com/2016/08/12/introducing-variational-autoencoders-in-prose-and.html http://blog.fastforwardlabs.com/2016/08/22/under-the-hood-of-the-variational-autoencoder-in.html http://blog.evjang.com/2016/11/tutorial-categorical-variational.html https://jmetzen.github.io/2015-11-27/vae.html Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 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/