name: "Embeddings"
number: 12
description: "Dense vector representations that convert discrete tokens into continuous semantic spaces"
learning_objectives:
  - "Implement embedding layers with efficient lookup operations"
  - "Build sinusoidal and learned positional encoding systems"
  - "Understand embedding memory scaling and optimization techniques"
  - "Analyze how embedding choices affect model capacity and performance"
  - "Design embedding systems for production language model deployment"

prerequisites:
  - "02_tensor"
  - "11_tokenization"

exports:
  - "Embedding"
  - "PositionalEncoding"
  - "LearnedPositionalEmbedding"
  - "EmbeddingProfiler"

systems_concepts:
  - "Embedding table memory scaling O(vocab_size × embed_dim)"
  - "Memory-bandwidth bound lookup operations"
  - "Cache-friendly embedding access patterns"
  - "Position encoding trade-offs and extrapolation"
  - "Distributed embedding table management"

ml_systems_focus: "Memory-efficient embedding lookup, position encoding scalability, large-scale parameter management"

estimated_time: "4-5 hours"

next_modules:
  - "13_attention"