Embeddings
Vector representations of data (usually text) that capture semantic meaning in high-dimensional space.
Detailed Explanation
Embeddings are a fundamental concept in modern machine learning, particularly in natural language processing. They transform discrete data (like words or sentences) into continuous vector spaces where similar items are positioned closer together.
The power of embeddings lies in their ability to capture semantic relationships. For example, in a well-trained embedding space, the vectors for "king" and "queen" would be close together, as would "cat" and "dog". More remarkably, vector arithmetic can capture analogies: vector("king") - vector("man") + vector("woman") ≈ vector("queen").
My Understanding
💭 Think of embeddings as coordinates in a multi-dimensional space. Just like GPS coordinates tell you where something is on Earth, embeddings tell you where a concept "lives" in meaning-space. Similar concepts are neighbors.
Common Usage
Etymology
From "embed" (to fix firmly in a surrounding mass) + "-ing", reflecting how discrete items are placed within a continuous space.