> The reason this works well for images is because you can say, ok, this output number represents a tag (dog, cat, etc) for each discrete value, and the input n

> The reason this works well for images is because you can say, ok, this output number represents a tag (dog, cat, etc) for each discrete value, and the input numbers are just an array of the raw pixel data. > because a traditional function implementation of f(x) that takes a I of dimension say, 65535, and produces an output of dimension 1 is a hard problem Nitpick maybe, but you do not represent each class by a number. This would create a bias to consider subsequent categories as similar (why would aardvark=0 be more similar to antilope=1 than cat=2?). Instead you have one dimension per possible-value (aka one-hot encoding) and the value in that dimension represents how likely the photo belongs to that class (with values summing to 1, softmax, if it's a one-class problem, or no such guarantee if it's a multiclass problem).