Neural networks generate embeddings by learning to represent complex data—like text, images, or user behavior—as compact numerical vectors. These vectors capture essential patterns and relationships in the data, enabling algorithms to process and compare information efficiently. During training, neural networks adjust their internal parameters to transform high-dimensional inputs (e.g., words or pixels) into lower-dimensional vectors. The network’s architecture and training objective determine how these embeddings encode meaningful features. For example, a network trained to predict words in context will learn embeddings where semantically similar words are closer in vector space.
A common example is Word2Vec, a neural network-based model that generates word embeddings. Word2Vec uses a shallow network to predict surrounding words (skip-gram) or a target word from its context (CBOW). As the network trains, it adjusts word vectors so that words appearing in similar contexts (like “king” and “queen”) end up with similar embeddings. Another example is convolutional neural networks (CNNs) for image embeddings. A CNN processes images through layers that detect edges, textures, and shapes, eventually producing a vector that summarizes the image’s visual features. These embeddings can then be used for tasks like similarity search or classification.
Embeddings created by neural networks are widely used in downstream applications. For instance, recommendation systems use embeddings to represent users and items (e.g., movies or products). A neural network trained on user interaction data learns embeddings that place users and items they interact with closer in vector space. Similarly, transformer-based models like BERT generate contextual embeddings for text, where the same word can have different vectors depending on its usage in a sentence. These embeddings improve performance in tasks like sentiment analysis or question answering. By compressing data into meaningful vectors, neural networks enable efficient computation of similarities (e.g., cosine distance) and facilitate transfer learning, where pre-trained embeddings are reused across multiple tasks.