Questions tagged [standardisation]

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How to scale all positive continuous reward?

My RL project has all positive continuous rewards for every step and the goal is to have the maximum cumulative reward (episodic reward). The problem is that the rewards are too close and all between ...
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Why do you calculate the mean and standard deviation over the complete dataset before training rather than for every batch?

In most implementations of neural networks the features are scaled to make the optimization of the loss function as stable as possible. Mostly a min-max scaler is used. Alternatively, there is also a ...
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For image preprocessing, is it better to use normalization or standartization?

For a neural network model that classifies images, is it better to use normalization (dividing by 255.0) or using standardization (subtract mean and divide by STD)? When I started learning ...
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How to normalize images before training?

I have seen people normalize images by just dividing 255. But why? Why not use mean normalization or Z-score Normalization? I also came across this StackOverflow topic while searching but the answers ...
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Why does batch norm standardize with sample mean/variance, when it also learns parameters to scale the mean/variance?

Batch norm is a normalizing layer that is shown to help deep networks learn faster and with higher generalization accuracy. It normalizes the activations of the previous layer to a mean $\beta$ and ...
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Is it necessary to standardise the expected output

Normalisation transform data into a range: $$X_i = \dfrac{X_i - Min}{Max-Min}$$ Practically, I found out that the model doesn't generalise well when using normalisation of input data, instead of ...