Understanding Embeddings

What are Embeddings?

Embeddings are dense vector representations of data that capture semantic meaning. They transform complex data like text, images, or audio into fixed-length arrays of numbers that machines can process.

Key Characteristics

1. Dense Representations: Every dimension contains meaningful information
2. Fixed Length: Same size regardless of input length
3. Semantic Capture: Similar meanings = similar vectors
4. Learned Features: Neural networks learn the best representation

Why Embeddings Matter

Traditional representations like one-hot encoding are:

• Sparse and high-dimensional
• Don't capture relationships
• Can't handle new vocabulary

Embeddings solve these problems by learning dense, meaningful representations!

Types of Embeddings

Type	Use Case	Example Models
Word	Single words	Word2Vec, GloVe
Sentence	Full sentences	Sentence-BERT
Document	Paragraphs/docs	Doc2Vec
Multi-modal	Text + Images	CLIP