Neural Networks / Deep Learning - Backpropagation Details Pt. 2: Going bonkers with The Chain Rule

This StatQuest picks up right here Part 1 left off, and this time we're going to go totally bonkers with The Chain Rule and optimize every single parameter in this simple Neural Network. BAM!!! NOTE: This StatQuest assumes that you already know the main ideas behind Backpropagation: https://youtu.be/IN2XmBhILt4 ...and that also means you should be familiar with... Neural Networks: https://youtu.be/CqOfi41LfDw The Chain Rule: https://youtu.be/wl1myxrtQHQ Gradient Descent: https://youtu.be/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... Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...buying one of my books, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... https://statquest.org/statquest-store/ ...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 1:28 The derivative of the weight W1 5:58 The derivative of the bias b1 7:39 The derivatives of W2 and b2 9:21 Gradient Descent for all parameters 11:18 Fancy Gradient Descent Animation #StatQuest #NeuralNetworks #Backpropagation