The main ideas behind Backpropagation are super simple, but there are tons of details when it comes time to implementing it. This video shows how to optimize three parameters in a Neural Network simultaneously and introduces some Fancy Notation. NOTE: This StatQuest assumes that you already know the main ideas behind Backpropagation: https://www.youtube.com/watch?v=IN2XmBhILt4 ...and that also means you should be familiar with... Neural Networks: https://www.youtube.com/watch?v=CqOfi41LfDw The Chain Rule: https://www.youtube.com/watch?v=wl1myxrtQHQ Gradient Descent: https://www.youtube.com/watch?v=sDv4f4s2SB8 LAST NOTE: When I was researching this 'Quest, I found this page by Sebastian Raschka to be helpful: https://sebastianraschka.com/faq/docs/backprop-arbitrary.html For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Buying my book, The StatQuest Illustrated Guide to Machine Learning: PDF - https://statquest.gumroad.com/l/wvtmc Paperback - https://www.amazon.com/dp/B09ZCKR4H6 Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: null ...a cool StatQuest t-shirt or sweatshirt: null ...buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer 0:00 Awesome song and introduction 3:01 Derivatives do not change when we optimize multiple parameters 6:28 Fancy Notation 10:51 Derivatives with respect to two different weights 15:02 Gradient Descent for three parameters 17:19 Fancy Gradient Descent Animation #StatQuest #NeuralNetworks #Backpropagation