Answer (Choose 2 answers)
A. (1)
B. (II)
C. (III)
D. (IV)
Which of the following statements are true? Check all that apply.
(1) Suppose you have a three layer network with parameters (1)(controlling the function mapping from the inputs to the hidden units)and (2)
(controlling the mapping from the hidden units to the outputs). If we set all the elements of to be 0, and all the elements of (2) to be 1, then this suffices for symmetry breaking,since the neurons are no longer all computing the
same function of
(III) Suppose you are training a neural network using gradient descent.Depending on your random initialization, your algorithm may converge to different local optima (i.e., if you run the algorithm twice with different random initializations,gradient descent may converge to two different solutions).
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