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Do you normalize the output variable?
If your problem is a regression problem, then the output will be a real value. This is best modeled with a linear activation function. If the distribution of the value is normal, then you can standardize the output variable. Otherwise, the output variable can be normalized.
Should output be normalized?
Similarly, the expected output data should be within normalized values, which _may be mapped to actual values. In most of supervised neural networks, the normalization is required. This is because in these NNs a transfer function is used (in the forward calculation) which gives outputs in a specific range.
Why should I normalize also the output data?
I’m new to data science and Neural Networks in general. Looking around many people say it is better to normalize the data between doing anything with the NN. I understand how normalizing the input data can be useful. However I really don’t see how normalizing the output data can help.
How to normalize data before training a neural network?
All the variables have roughly normal distributions. I consider different options to scale the data before training. One option is to scale the input (independent) and output (dependent) variables to [0, 1] by computing cumulative distribution function using the mean and standard deviation values of each variable, independently.
Why do neural networks normalize the input vector?
The reason lies in the fact that, in the case of linear activation functions, a change of scale of the input vector can be undone by choosing appropriate values of the vector . If the training algorithm of the network is sufficiently efficient, it should theoretically find the optimal weights without the need for data normalization.
How is batch normalization used in deep learning?
Batch Normalization Another technique widely used in deep learning is batch normalization. Instead of normalizing only once before applying the neural network, the output of each level is normalized and used as input of the next level. This speeds up the convergence of the training process.