Dropout (a) Explain why dropout can be used to mitigate the overfitting problem (b) Suppose we have a simple two layer neural network y W 2 ( W 1 x b 1 ) b 2 y W 2 ( W 1 x b 1 ) b 2 where x R 3 , W 1 R 2 3 x R 3 , W 1 R 2 3 , b 1 R 2 , W 2 R 2 b 1 R 2 , W 2 R 2 and b 2 R b 2 R How many possible dropout networks are there that are not disconnected

Question: Dropout. (a) Explain why dropout can be used to mitigate the overfitting problem. (b) Suppose we have a simple two-layer neural network y = W

Dropout. (a) Explain why dropout can be used to mitigate the overfitting problem. (b) Suppose we have a simple two-layer neural network $y = W_{2} (W_{1} x + b_{1}) + b_{2}$ where $x \in R^{3}, W_{1} \in R^{2 \times 3}$ , $b_{1} \in R^{2}, W_{2} \in R^{2}$ and $b_{2} \in R$ . How many possible dropout networks are there that are not disconnected?

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