Artificial Intelligence & Machine Learning
Embedding
Definition
In machine learning, an embedding is a learned representation for discrete data, like words or user IDs, where items are mapped to a dense vector of real numbers. Items with similar meanings or properties are positioned close to each other in this high-dimensional vector space.
Why It Matters
Embeddings allow machine learning models to work with categorical data. They capture the semantic relationships between items, which is far more powerful than just treating them as arbitrary IDs.
Contextual Example
A word embedding model like Word2Vec learns vector representations for words. In this vector space, the vector for "king" might be close to the vector for "queen," and the relationship between them might be similar to the relationship between "man" and "woman."
Common Misunderstandings
- Embeddings are not manually designed; they are learned from data during the training process.
- They are a fundamental component of modern NLP models and recommendation systems.