APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
Bias function
|
|
|
Activation function
|
|
|
Trigger function
|
|
|
None of the above
|
Detailed explanation-1: -An Activation Function decides whether a neuron should be activated or not. This means that it will decide whether the neuron’s input to the network is important or not in the process of prediction using simpler mathematical operations.
Detailed explanation-2: -Hence, Beta is not an activation function.
Detailed explanation-3: -The choice is made by considering the performance of the model or convergence of the loss function. Start with the ReLU activation function and if you have a dying ReLU problem, try leaky ReLU. In MLP and CNN neural network models, ReLU is the default activation function for hidden layers.
Detailed explanation-4: -A neural network without an activation function is essentially just a linear regression model. Thus we use a non linear transformation to the inputs of the neuron and this non-linearity in the network is introduced by an activation function.