Embedding

Embeddings are representations of data in vector form, capturing the semantic meaning of the data in a continuous vector space.

They are used when there is a need to represent complex data, such as words, images, or vertices in a way that preserves their semantic relationships. Embeddings are commonly applied in scenarios such as natural language processing, image recognition, and recommendation systems. The technique works by mapping data points to vectors in a high-dimensional space, where similar data points are located close to each other. Popular algorithms using embeddings are Autoencoders and various models following the Encoder-Decoder architecture, where data is transformed into a latent space representation.

For example, in natural language processing, word embeddings represent words as vectors, capturing their meanings and relationships. In image recognition, embeddings can represent images in a way that similar images have similar vector representations.

Embeddings play a crucial role in multimodal models, which integrate data from multiple modalities, such as text, images, and audio. By converting different types of data into a common embedding space, multimodal models can learn to understand and relate information across different modalities.

Embeddings are important because they provide a way to represent complex data in a continuous vector space, enabling models to capture the semantic meaning of data and leverage it for various tasks.