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The Transformer architecture replaces recurrence with self-attention, allowing parallel processing of entire sequences. Each layer computes: Attention(Q, K, V) = softmax(QKᵀ / √dₖ) · V where Q, K, V are learned linear projections of the input. Multi-head attention runs this in parallel across different subspaces, capturing diverse relationships. Key advantages over RNNs: • O(1) path length between any two positions • Fully parallelizable training • Better gradient flow for long sequences

Self-attention is the core mechanism that allows transformers to process sequences without recurrence. For each token, it computes relevance scores against every other token. The process: 1. Project input into Q, K, V matrices 2. Compute attention weights: softmax(QKᵀ/√d) 3. Apply weights to values V Multi-head attention splits this across parallel heads, each learning different relationship patterns. Combined with positional encodings and feed-forward layers, this creates a powerful sequence model.