Short Definition
Embeddings represent entities as dense vectors.
Definition
Embeddings map discrete or high-dimensional entities into continuous vector spaces where semantic similarity is preserved. They are foundational in NLP, vision, and recommendation systems.
Why It Matters
Embeddings enable neural networks to work with language, categories, and relationships efficiently.
How It Works (Conceptually)
- Discrete inputs mapped to vectors
- Similar entities have similar vectors
- Learned during training
Minimal Python Example
embedding = [0.12, -0.8, 1.3]
Common Pitfalls
- Treating embeddings as fixed truths
- Ignoring embedding drift
- Using pretrained embeddings blindly
Related Concepts
- Representation Learning
- Transformers
- Tokenization
- Transfer Learning
Neural Network Lexicon 