There are several common types of embeddings used across different domains in machine learning. Some of the most widely known types include:
- Word Embeddings: These are representations of words in a continuous vector space, where similar words are close together. Popular examples include Word2Vec, GloVe, and FastText. These embeddings are primarily used in natural language processing (NLP) tasks.
- Document Embeddings: Similar to word embeddings, but they represent entire documents, paragraphs, or sentences. Examples include Doc2Vec and Sentence-BERT.
- Image Embeddings: Used in computer vision tasks, these embeddings represent images or parts of images as vectors. Techniques such as ResNet and VGG are commonly used to generate image embeddings.
- Graph Embeddings: These represent nodes or entire graphs as vectors in a way that captures the structure and relationships in the graph. Examples include node2vec and GraphSAGE.
Each type of embedding is designed to capture the inherent structure and relationships of the data within a specific domain.