name: "Attention"
number: 13
description: "Scaled dot-product and multi-head attention mechanisms that enable transformer architectures"
learning_objectives:
  - "Implement scaled dot-product attention with proper masking and numerical stability"
  - "Build multi-head attention with parallel head processing and output projection"
  - "Design KV-cache systems for efficient autoregressive generation"
  - "Understand attention's O(N²) scaling and memory optimization techniques"
  - "Analyze attention performance bottlenecks and production optimization strategies"

prerequisites:
  - "02_tensor"
  - "12_embeddings"

exports:
  - "ScaledDotProductAttention"
  - "MultiHeadAttention"
  - "KVCache"
  - "AttentionProfiler"

systems_concepts:
  - "Quadratic memory scaling O(N²) with sequence length"
  - "Memory-bandwidth bound attention computation"
  - "KV-cache optimization for autoregressive generation"
  - "Multi-head parallelization and hardware optimization"
  - "Attention masking patterns and causal dependencies"

ml_systems_focus: "Attention memory scaling, generation efficiency optimization, sequence length limitations"

estimated_time: "5-6 hours"

next_modules:
  - "14_transformers"