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: [ Ссылка ]
The Chain Rule: [ Ссылка ]
Gradient Descent: [ Ссылка ]
LAST NOTE: When I was researching this 'Quest, I found this page by Sebastian Raschka to be helpful: [ Ссылка ]
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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
