What is the difference between taking a transformation of a response variable to then apply linear regression and a GLM? [duplicate]

From what I've studied so far, GLM's are to be used when the error term of a response variable is not assumed to be normally distributed. However, I also read that sometimes a transformation of a response variable is used (after which regular linear regression is applied) to "normalize" it? If so, then what is the difference between those two modelling approaches?