Embeddings are numerical representations of data that capture semantic relationships, enabling machines to process complex information like text or images efficiently. They convert high-dimensional, unstructured data (such as words or pixels) into dense, lower-dimensional vectors of real numbers. These vectors encode meaningful patterns, so similar items—like related words or visually alike images—are positioned closer together in the vector space. For example, in natural language processing (NLP), the words “cat” and “dog” might have embedding vectors that are mathematically closer than the vectors for “cat” and “car,” reflecting their semantic similarity.
Embeddings are created using machine learning models trained to identify relationships in data. In NLP, models like Word2Vec or BERT learn by analyzing large text corpora. For instance, Word2Vec trains a neural network to predict surrounding words (skip-gram) or a target word from its context (CBOW). During training, the model adjusts word vectors so words appearing in similar contexts (e.g., “king” and “queen”) end up with similar embeddings. Similarly, image embeddings are generated using convolutional neural networks (CNNs) that learn hierarchical features from pixels—edges, textures, shapes—and encode them into vectors. These models are often pre-trained on massive datasets (like ImageNet for images) and fine-tuned for specific tasks.
Developers use embeddings to solve tasks like semantic search, recommendation systems, or clustering. For example, in a search engine, converting user queries and documents into embeddings allows ranking results by vector similarity (using cosine distance). Embeddings also reduce computational complexity: representing a word as a 300-dimensional vector is more efficient than one-hot encoding over a 50,000-word vocabulary. Key considerations include choosing the right embedding dimension (e.g., 768 dimensions for BERT-base) and whether to use pre-trained embeddings or train custom ones. While pre-trained models (e.g., GPT-4’s token embeddings) save time, domain-specific tasks (like medical text analysis) may require fine-tuning to capture specialized terminology.