Neural Networks Pt. 2: Backpropagation Main Ideas

Backpropagation is the method we use to optimize parameters in a Neural Network. The ideas behind backpropagation are quite simple, but there are tons of details. This StatQuest focuses on explaining the main ideas in a way that is easy to understand. NOTE: This StatQuest assumes that you already know the main ideas behind... 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 3:55 Fitting the Neural Network to the data 6:04 The Sum of the Squared Residuals 7:23 Testing different values for a parameter 8:38 Using the Chain Rule to calculate a derivative 13:28 Using Gradient Descent 16:05 Summary #StatQuest #NeuralNetworks #Backpropagation