Embeddings are a type of mathematical representation used to transform high-dimensional data, such as words or images, into a lower-dimensional vector space, where each dimension represents a certain feature or attribute.
In natural language processing, word embeddings are commonly used to represent words as vectors of real numbers. Each word is assigned a unique vector that captures its meaning and relationships with other words in the language. These vectors can be used as input to machine learning models for various natural language processing tasks, such as sentiment analysis, text classification, and language translation.
In computer vision, image embeddings are used to represent images as vectors of real numbers, where each dimension represents a certain visual feature or attribute, such as color, texture, or shape. These embeddings can be used as input to machine learning models for various computer vision tasks, such as object recognition, image retrieval, and image captioning.
Overall, embeddings are a powerful tool for representing complex data in a more manageable and meaningful way, allowing machine learning models to learn from and make predictions on this data more efficiently and accurately.