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Why is it necessary in batch normalization to multiply and add a parameter to the result?


Paper: What's the difference between Layer Normalization, Recurrent Batch Normalization (2016), and Batch Normalized RNN (2015)?When to use (He or Glorot) normal initialization over uniform init? And what are its effects with Batch Normalization?Does Batch Normalization make sense for a ReLU activation function?Why is scale parameter on batch normalization not needed on ReLU?understanding batch normalizationBatch norm: why the initial normalization?Batch Normalization and Input Normalization in CNNWhy normalization kills my accuracyKeras multi-gpu batch normalizationTranspose-CNN with batch normalization













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How do we decide on which layer we want to add batch normalization. So if we have chosen a layer to apply batch norm to then why don't just normalize it why are we multiplying and scaling it by some value. What does multiplication and scaling does?









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    How do we decide on which layer we want to add batch normalization. So if we have chosen a layer to apply batch norm to then why don't just normalize it why are we multiplying and scaling it by some value. What does multiplication and scaling does?









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      How do we decide on which layer we want to add batch normalization. So if we have chosen a layer to apply batch norm to then why don't just normalize it why are we multiplying and scaling it by some value. What does multiplication and scaling does?









      share







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      How do we decide on which layer we want to add batch normalization. So if we have chosen a layer to apply batch norm to then why don't just normalize it why are we multiplying and scaling it by some value. What does multiplication and scaling does?







      neural-network deep-learning normalization feature-scaling batch-normalization





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