Question 1

What is the mathematical intuition behind attention?

Accepted Answer

Attention is a soft selection mechanism: compute relevance scores between queries and keys (dot-product), normalize (softmax), and aggregate values. It allows models to dynamically focus on relevant context.

Question 2

Why is scaling by sqrt(d_k) important in attention?

Accepted Answer

Prevents softmax from collapsing into near-zero gradients when dimension d_k is large. Without scaling, logits grow too fast, and gradients vanish.

Question 3

What is the difference between self-attention and cross-attention?

Accepted Answer

Self-attention attends within the same sequence (query, key, value from same source). Cross-attention attends from one sequence (query) to another (key, value). Used in seq2seq and encoder-decoder models.

Question 4

How does multi-head attention improve learning?

Accepted Answer

Different heads learn different attention patterns (positions, semantic features, syntax). Parallel heads increase representational capacity without proportionally increasing parameters.

Question 5

What is causal masking?

Accepted Answer

In autoregressive models, mask future positions so the model can't cheat by looking ahead. Applied as a -inf mask in the softmax step.

Question 6

Why is Flash Attention faster?

Accepted Answer

Reduces memory reads/writes by computing attention in tiles and recomputing rather than materializing the full attention matrix. 2-3x speedup on GPUs.

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UK	£100k	£165k	£265k
EU	€90k	€150k	€240k
CANADA	C$135k	C$225k	C$360k

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