The Future of Deep Learning Research

Back-propagation is fundamental to deep learning. Hinton (the inventor) recently said we should "throw it all away and start over". What should we do? I'll describe how back-propagation works, how its used in deep learning, then give 7 interesting research directions that could overtake back-propagation in the near term. Code for this video: https://github.com/llSourcell/7_Research_Directions_Deep_Learning Please Subscribe! And like. And comment. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.youtube.com/watch?v=q555kfIFUCM https://www.youtube.com/watch?v=h3l4qz76JhQ https://www.youtube.com/watch?v=vOppzHpvTiQ https://deeplearning4j.org/deepautoencoder https://deeplearning4j.org/glossary https://www.reddit.com/r/MachineLearning/comments/70e4ex/n_hinton_says_we_should_scrap_back_propagation/ https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ http://kvfrans.com/generative-adversial-networks-explained/ 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/