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Why is it advantageous for inputs/targets to most ML algorithms like neural nets to be normally distributed? I am not talking about mean normalization, but in some cases of skewed data, people perform log transform to make it more normal. Example: https://medium.com/ml-byte/rare-feature-engineering-techniques-for-machine-learning-competitions-de36c7bb418f here the last tip says to convert targets by log(1+target) to do so. Why? Why does it become easier to fit or improve accuracy?

P.S I know from studying linear models that those models model how variances in the input affect the variance in the output. So the mean and variances that we normalize by are just shift and scaling factors that act on the true relationship between the variables. But i'm not sure of log transforms and such and why it easier here.

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