Channel: StatQuest with Josh Starmer

Category: Education

Tags: josh starmerdata scienceneural networksbackpropagationmachine learningstatqueststatistics

Description: 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: youtu.be/IN2XmBhILt4 ...and that also means you should be familiar with... Neural Networks: youtu.be/CqOfi41LfDw The Chain Rule: youtu.be/wl1myxrtQHQ Gradient Descent: youtu.be/sDv4f4s2SB8 LAST NOTE: When I was researching this 'Quest, I found this page by Sebastian Raschka to be helpful: sebastianraschka.com/faq/docs/backprop-arbitrary.html For a complete index of all the StatQuest videos, check out: statquest.org/video-index If you'd like to support StatQuest, please consider... Buying my book, The StatQuest Illustrated Guide to Machine Learning: PDF - statquest.gumroad.com/l/wvtmc Paperback - amazon.com/dp/B09ZCKR4H6 Kindle eBook - amazon.com/dp/B09ZG79HXC Patreon: patreon.com/statquest ...or... YouTube Membership: youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...a cool StatQuest t-shirt or sweatshirt: shop.spreadshirt.com/statquest-with-josh-starmer ...buying one or two of my songs (or go large and get a whole album!) joshuastarmer.bandcamp.com ...or just donating to StatQuest! paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: 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