L2 norm regularization : Make the weights closer to zero prevent overfitting. L1 Norm regularization : Make the weights closer to zero and also induce sparsity in weights. Less common form of regularization Dropout regularization : Ensure some of the hidden units are dropped out at random to ensure the network does not overfit by…
Increase number of units in the hidden layer Increase number of hidden layers Increase data set size Change activation function to tanh Try all of the above The answer is d. When I use a linear activation function, the deep neural network is realizing a linear combination of linear functions which leads to modeling only…
High bias means the data is being underfit. The decision boundary is not usually complex enough. High variance happens due to over fitting, the decision boundary is more complex than what it should be. High bias high variance happens when you fit a complex decision boundary that is also not fitting the training set…