Feature normalization on inputs to hidden layers is not as simple as feature normalization on the original input. This is because at layer 𝑖, the input depends on the previous 𝑖−1 layers, and the weights will change as the weights are updated.Group of answer choicesTrueFalse

Layer normalization is used to normalize inputs across the batch dimension.Group of answer choicesTrueFalse

Batch normalization can only be applied to convolutional layers.Group of answer choicesTrueFalse

Batch Normalization is helpful because.Question 17Select one:A.It returns the normalized mean and standard deviation of weights.B.It normalizes (changes) all the input before sending it to the next layer.C.None of theseD.It is a very efficient backpropagation technique.

In deep learning, the ______________ technique is used to normalize inputs to each layer to improve training stability.Group of answer choicesL2 RegularizationGradient ClippingDropoutBatch Normalization

Question 1What task does Batch normalization do?1 pointWe normalize the input layer by adjusting and scaling the activations Reducing Internal Covariate Shift