Suppose you are fitting a neural network with one hidden layer to a training set. You find that the cross validation error Jcv (0) is much larger than the training error Jtrain (0). Is increasing the number of hidden units likely to help? Oa. Yes, because it is currently suffering from high bias. Ob. Yes, because this increases the number of parameters and lets the network represent more complex functions. O No, because it is currently suffering from high variance, so adding hidden units is unlikely to help. No, because it is currently suffering from high bias, so adding hidden units is unlikely to help. Od.

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