[D] - Why do Attention layers work so well? Don't weights in DNNs already tell the network how much weight/attention to give to a specific input? (High weight = lots of attention, low weight = little attention)

So an attention layer has a Q, K, and V vector My understanding is the goal is to say for a given query q, how relevant is the value v.

From this the network learns which data is relevant to focus on for a given input.

But what I don't get is why this is effective. Don't DNNs already do this with weights? A neuron in a hidden layer can be set off by any arbitrary combination of inputs, so …

attention low machinelearning network work