Embeddings are used to represent complex data in a lower-dimensional vector space, making it easier for models to process and analyze the data. They are widely used in a variety of machine learning tasks such as classification, clustering, recommendation, and search. For instance, in natural language processing (NLP), word embeddings like Word2Vec or GloVe are used to represent words as vectors, enabling models to understand semantic relationships between words.
In computer vision, embeddings are used to represent images, enabling tasks like image retrieval, object detection, and facial recognition. For example, an embedding might represent an image of a dog, and the model can use this representation to find similar images in a database. Similarly, embeddings are used in recommendation systems to represent both users and items, allowing the system to match users with products or content that align with their preferences.
Embeddings are also used in generative models, where they help create new data points that resemble the input data. They allow the model to manipulate and combine different elements of the input data to generate novel outputs. Overall, embeddings serve as a compact and efficient way to represent complex data, making them a critical component in many machine learning applications across different domains.