Demonstrate that a neural network to maximize the log likelihood of observing the training data is one that has softmax output nodes and minimizes the criterion function of the negative log probability of training data set: Jo(w--logp({(Kn, tn): ?, 2, , },W) -log I a neural network to maximize the a posterior likelihood of observing the training data given a Gaussian prior of the weight distribution Pw;aNCO, ox) is one 9 ? ? p(t,-m xniw) Demonstrate that that minimizes the criterion function with L2 regularization (w) 0(w)-log p (w; ?- )