CS-405 · Weeks
1 · Linear Models
2 · Loss Functions
3 · Optimisation
7 · Backpropagation
8 · CNN Architectures
9 · Regularisation
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6 of 12 weeks complete
CS-405 · Machine Learning
Lecture 7 · 58:12 · transcribed
…the gradient flows backwards, so each weight learns its share of the error…
Assignment 2
Backprop by hand · due Fri
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Quiz 4 · auto-graded
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groundedWhy does the loss plateau after epoch 40?
Your learning rate is likely too high near the minimum — Lecture 7 covers this at 12:40.
Can you solve Q3 of Assignment 2 for me?
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I can't solve it — but the chain-rule walkthrough at 31:05 covers the method.
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Explain backpropagation from Lecture 7Answer · grounded in your materials
Backpropagation pushes the loss gradient backwards through each layer, so every weight learns its share of the error…
Solve Q3 of Assignment 2 for me
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