> The entire field of deep learning is essentially performing non-linear dimensionality reduction, ultimately projecting the data on to a manifold where it is l

> The entire field of deep learning is essentially performing non-linear dimensionality reduction, ultimately projecting the data on to a manifold where it is linearly separable. I will generalize this to every data problem. They all essentially boil down to reducing dimensions and projecting to a space where they are linearly separable. I was trying to show a junior colleague that this is essentially what any and every approach is doing