Update week43.do.txt
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@@ -30,6 +30,21 @@ DATE: today
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neural nets will be very large: impractical to write down gradient formula
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by hand for all parameters
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● backpropagation = recursive application of the chain rule along a
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computational graph to compute the gradients of all
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inputs/parameters/intermediates
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● implementations maintain a graph structure, where the nodes implement
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the forward() / backward() API
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● forward: compute result of an operation and save any intermediates
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needed for gradient computation in memory
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● backward: apply the chain rule to compute the gradient of the loss
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function with respect to the inputs
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!split
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===== CNNs in brief =====
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