Embedding
A numerical representation of data (text, images, etc.) in a continuous vector space where similar items are positioned close together.
Detailed Explanation
Embeddings are dense vector representations that capture the semantic meaning of data in a high-dimensional space. For text, words or sentences with similar meanings have similar embeddings (vectors that are close together). For example, 'king' and 'queen' would have similar embeddings, as would 'dog' and 'puppy.' Embeddings enable machines to understand relationships and similarities, powering applications like semantic search, recommendation systems, and similarity detection. Modern embedding models can represent not just words but entire documents, images, and even multimodal content in the same vector space.
Real-World Examples
Semantic Search
SearchCompanies use embeddings to enable search by meaning rather than keywords. Users can search 'affordable Italian restaurants' and find results containing 'budget-friendly pasta places' even without exact keyword matches.
Product Recommendations
E-commerceE-commerce platforms use embeddings to recommend similar products, increasing cross-sell revenue by 35% by understanding product relationships beyond simple category matching.
Frequently Asked Questions
Q:How are embeddings created?
Embeddings are learned by neural networks during training. The network learns to map inputs (words, images) to vectors such that similar inputs have similar vectors. Popular methods include Word2Vec, GloVe for words, and transformer-based models for sentences.
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