2.2) Which of the following statement(s) is/are true?

By adding one or more layers to perceptron network with activation functions, non-

linear separable cases can be handled.

For a non-linearly separable dataset, if activation function applied on the hidden layer(s)

of the Multi-layer Perceptron network is a linear function, the model will converge to an

optimal solution.

For a linearly separable dataset, applying non-linear activation function such as sigmoid

or tanh on hidden layers of a MLP network, can converge to a good solution.

All of the above