What is the practical difference between convolution and cross correlation? Good morning,

I am coming from learning machine learning convolution for neural nets and was wondering about cross-correlation vs convolution.

I referenced this answer here: What&#39;s the difference between convolution and crosscorrelation?

But I fail to understand the practical difference that a mirrored 'filter' (not sure if that is the correct term in this context) produces when using convolution rather than cross-correlation. It seems that either method contains different representations of the same data. Whether (as in the link above) it is X+Y or Y-X, they both contain similar, albeit opposite, data.

Is this simply used to adjust the direction of the vector, as seemingly the magnitude would remain unchanged? Or am I missing some subtleties?

Thank you!
From more research I performed, per a Coursera course in which I am enrolled (DeepLearning.Ai), in "signal processing or in certain branches of mathematics, doing the flipping in the definition of convolution causes the convolution operator to enjoy [the associativity property]" - Professor Andrew Ng

The 'flipping' referring to using convolution rather than cross-validation, both terms used in the mathematical context.
From more research I performed, per a Coursera course in which I am enrolled (DeepLearning.Ai), in "signal processing or in certain branches of mathematics, doing the flipping in the definition of convolution causes the convolution operator to enjoy [the associativity property]" - Professor Andrew Ng

The 'flipping' referring to using convolution rather than cross-validation, both terms used in the mathematical context.