In machine learning, embeddings refer to the process of converting high-dimensional, often categorical or textual, data into dense, continuous vectors in a lower-dimensional space. These vectors are designed to capture the semantic relationships between data points, such as words, images, or items in a recommendation system. By embedding data in this way, machine learning models can more easily compute similarities, clusters, or patterns.
Embeddings are widely used in various domains, including natural language processing (NLP), where words or sentences are mapped to vectors that represent their meaning. In computer vision, embeddings help represent images as vectors that capture visual features. The goal is to transform raw, unstructured data into a form that can be easily processed and interpreted by machine learning models.
Embeddings are typically learned through neural networks, which optimize the vectors to preserve meaningful relationships in the data. Once trained, these embeddings can be used in downstream tasks like classification, clustering, and recommendation, improving the model's performance by providing rich, low-dimensional representations of the data.