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Classify inputs using a simple neural network with 1 hidden layer

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Given the simple neural network shown above and the input values in the table. a1,a2 are sigmoid activation. Fill in the output for each blank to classify each input as producing an output of either a 0 or a 1.Suppose that we have the following NN structure with one hidden layers. Each circle denotes a node. 'input'layer 'hidden' layer 'output' layer How many neurons (perceptron) are there in the NN? How many learnable parameters in total?.

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Given the simple neural network shown above and the input values in the table. a1,a2 are sigmoid activation. Fill in the output for each blank to classify each input as producing an output of either a 0 or a 1.Suppose that we have the following NN structure with one hidden layers. Each circle denotes a node. 'input'layer 'hidden' layer 'output' layer How many neurons (perceptron) are there in the NN? How many learnable parameters in total?

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Classify inputs using a simple neural network with 1 hidden layer

  • 1. Given the simple neural network shown above and the input values in the table. a1,a2 are sigmoid activation. Fill in the output for each blank to classify each input as producing an output of either a 0 or a 1.Suppose that we have the following NN structure with one hidden layers. Each circle denotes a node. 'input'layer 'hidden' layer 'output' layer How many neurons (perceptron) are there in the NN? How many learnable parameters in total?