how-embeddings-work-ml.md

How Embeddings Work in Machine Learning (Discrete Data → Dense Vectors → Model)

flowchart TD
    A[Raw Input<br/>word / item / image / user ID] --> B[Tokenization or Indexing]
    B --> C[Embedding Layer<br/>lookup table]
    C --> D["Dense Vector<br/>e.g. [0.21, -0.44, 0.98, ...]"]
    D --> E[Neural Network / ML Model]
    E --> F[Output<br/>classification / similarity / recommendation]

    C -. learned during training .-> G[(Embedding Matrix Parameters)]

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Description:
Embeddings convert symbolic or categorical data into dense numeric vectors that machine learning models can understand.

Flow:
Input → Index → Embedding → Vector → Model → Output

Key Benefits:

• Compact representation
• Captures semantic similarity
• Trainable parameters
• More efficient than one-hot encoding