Which activation function is commonly used in the hidden layers of a neural network to introduce non-linearity?Group of answer choicesSigmoidLinearSoftmaxReLU

Which activation function is commonly used in the hidden layers of a neural network to introduce non-linearity?

Which layer type is responsible for introducing non-linearity in an RNN?Question 22Answera.Activation layerb.Input layerc.Hidden layerd.Output layer

The ReLU activation function can introduce non-linearity to the model.Group of answer choicesTrueFalse

Why are nonlinear activation functions preferable?1 pointNonlinear activation functions are NOT preferable to linear ones, as they lose information in systems with high variance.Nonlinear activation functions increase the functional capacity of the neural network by allowing the representation of nonlinear relationships between features in input.Nonlinear activation functions are preferable because they have been used historically.Nonlinear activation functions are preferable because they are used in generalized linear models in statistics.

What is the purpose of the activation function in a neural network?Review LaterTo determine the learning rateTo normalize the input dataTo introduce non-linearityTo calculate the loss function