In batch normalization, we can drop the final step of shift and scaling. Because the aim of BN is just to reduce the internal covariate shift, and it has already been done by normalization of inputs of the unit.*FalseTrue

False. While it's true that the primary aim of Batch Normalization (BN) is to reduce internal covariate shift by normalizing the inputs, the shift and scaling step is also crucial. This step allows the model to learn the optimal scale and mean of the outputs. Without this step, the model might not be able to learn certain representations, especially if the optimal mean and variance are not 0 and 1 respectively. Therefore, we cannot drop the final step of shift and scaling in batch normalization.

The statement is False. Although the primary aim of Batch Normalization (BN) is indeed to reduce internal covariate shift by normalizing the inputs, the shift and scaling step is also crucial. This step allows the model to learn the appropriate scale and mean of the inputs. Without this step, the model may not be able to learn complex patterns in the data, which could negatively impact its performance. Therefore, we cannot drop the final step of shift and scaling in batch normalization.