What is self-attention in the context of neural networks, particularly in transformer models? Explain how self-attention mechanisms work, including the key components and computations involved. How does self-attention facilitate learning long-range dependencies in sequences, and what advantages does it offer over traditional recurrent or convolutional architectures? Additionally, discuss any limitations or computational complexities associated with self-attention.

Self-attention is a fundamental mechanism used in neural networks, particularly prominent in transformer models, allowing them to effectively process sequential data. It enables the model to weigh different words or elements within a sequence differently, focusing more...