Embeddings

Explore how AI maps words, images, and concepts into high-dimensional vector spaces where similar items cluster together

// Visualization

Selected Word
Similar Words
Semantic Cluster

// Vector Arithmetic

Explore semantic relationships through vector operations

− +
Result will appear here...

How it works

Word embeddings capture semantic relationships as geometric patterns in vector space. Vector arithmetic allows us to perform analogies and explore conceptual relationships.

// Similarity Search

Find words most similar to your selection

Selected Word: None
Dimensions: 300
Nearest Neighbors: 0

Cosine Similarity

Similarity is measured using cosine similarity, which computes the angle between word vectors. Values range from -1 (opposite) to 1 (identical).

// Understanding Embeddings

What are Embeddings?

Embeddings are dense vector representations that map discrete objects (like words, images, or users) into continuous vector spaces. These representations capture semantic meaning, allowing machines to understand that "king" and "queen" are more related than "king" and "bicycle".

Key Properties

  • Dimensionality Reduction: Maps high-dimensional one-hot vectors into dense, lower-dimensional spaces (typically 50-300 dimensions)
  • Semantic Similarity: Similar items have similar vectors (measured by cosine similarity or Euclidean distance)
  • Learned Representations: Trained from data to capture meaningful patterns and relationships
  • Compositionality: Vector arithmetic reveals semantic relationships (king - man + woman ≈ queen)