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Sarah Jenkins

Sarah Jenkins

Student • 2h ago

#Help#Module 3

Question about Module 3: Neural Networks

I'm having a bit of trouble understanding the backpropagation math. Can anyone explain it simply? I understand the forward pass concept, but when it comes to calculating gradients, especially with the chain rule involved in deeper layers, I get a bit lost. Is there a visual resource or a simpler analogy that helped you understand this unique concept?

Discussion (2)

David Chen
David Chen• 1h ago

Highly recommend 3Blue1Brown's video on this! It visualizes the gradient descent as a ball rolling down a hill.

Dr. Elena Rossi
Dr. Elena RossiInstructor• 30m ago

Great question Sarah! Think of backprop as 'assigning blame' to each weight for the final error. The higher the 'blame' (gradient), the more we need to adjust that weight.