name: "Tokenization"
number: 11
description: "Text processing systems that convert raw text into numerical sequences for language models"
learning_objectives:
  - "Implement character-level tokenization with special token handling"
  - "Build BPE (Byte Pair Encoding) tokenizer for subword units"
  - "Understand tokenization trade-offs: vocabulary size vs sequence length"
  - "Optimize tokenization performance for production systems"
  - "Analyze how tokenization affects model memory and training efficiency"

prerequisites:
  - "02_tensor"

exports:
  - "CharTokenizer"
  - "BPETokenizer" 
  - "TokenizationProfiler"
  - "OptimizedTokenizer"

systems_concepts:
  - "Memory efficiency of token representations"
  - "Vocabulary size vs model size tradeoffs"
  - "Tokenization throughput optimization" 
  - "String processing performance"
  - "Cache-friendly text processing patterns"

ml_systems_focus: "Text processing pipelines, tokenization throughput, memory-efficient vocabulary management"

estimated_time: "4-5 hours"

next_modules:
  - "12_embeddings"