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

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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