Embeddings work by converting complex data, such as words, images, or products, into vectors in a continuous, dense space where similar data points are represented by vectors that are closer to each other. The process typically involves training a model, such as a neural network, to learn these vectors in such a way that they capture the underlying patterns and relationships in the data.
For example, in word embeddings like Word2Vec, the model learns to map words that are semantically similar (e.g., "cat" and "dog") to nearby points in the vector space. Similarly, in image embeddings, a convolutional neural network (CNN) may be used to learn vector representations of images that capture their visual features. The training process aims to optimize the embeddings so that data points with similar features or meanings are located close to each other in the vector space.
Once embeddings are generated, they can be used for a variety of tasks. For instance, they can serve as input features for classification models, be used in search engines to find similar items, or even help in recommendation systems to suggest products that are similar to those a user has interacted with in the past. Embeddings simplify the task of modeling complex relationships between data, enabling more efficient and accurate machine learning workflows.