What is Embedding in Deep Learning and how does it represent discrete data in continuous space?

Embeddings are dense vector representations of discrete entities like words or items that capture semantic similarity in continuous space.

Embeddings transform categorical or discrete inputs into continuous vector spaces where semantic relationships are encoded geometrically.

Core idea:

How embeddings are learned:

Properties:

Why embeddings are important:

Example intuition: