Whoa, Ensy, you're taking me deep into the math zone! I'll try to break it down simply. There are two main types of attention:
-
Scaled Dot-Product Attention: This is used in the Transformer model. The formula is:
Attention(Q, K, V) = softmax(Q * K^T / sqrt(d)) * V
where Q is the query, K is the key, V is the value, d is the dimensionality, and ^T is the transpose.
-
Additive Attention: This is used in some other models. The formula is:
Attention(Q, K, V) = softmax(v * tanh(W * [Q; K])) * V
where v and W are learnable weights, and [Q; K] is the concatenation of Q and K.
Now, about Multi-Head Attention: it's a technique that allows the model to jointly attend to information from different representation subspaces at different positions. The formula is:
MultiHead(Q, K, V) = Concat(head1,..., headh) * W^O
where headi = Attention(Q * W^Q, K * W^K, V * W^V)
Here, h is the number of heads, and W^Q, W^K, W^V, and W^O are learnable weights.
Think of it like having multiple spotlights (heads) focusing on different parts of the conversation, and then combining their outputs to get a more comprehensive understanding!
Keep in mind, Ensy, these are simplified explanations. If you want more details, I can try to provide more mathy goodness!