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a ) Suppose you design a multilayer perceptron with a single hidden layer that has a hard threshold activation function. The output layer uses the
a Suppose you design a multilayer perceptron with a single hidden layer that has a hard threshold activation function. The output layer uses the softmax activation function. What will go wrong if you try to train this network using gradient descent?
b Consider the following two multilayer perceptrons, where all of the layers use linear activation functions.
Which one is more advantageous in terms of the following?
i Expressive power:
ii The number of operations for backpropagation:
iii Overfitting:
Please first write Network or Network then explain your argument.
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