In machine learning, an embedding is a mathematical representation of a set of data points in a lower-dimensional space that captures their underlying relationships and patterns. Embeddings are dense numerical representations of real-world objects and relationships, expressed as a vector. They are used to represent complex data types, such as images, text, or audio, in a way that machine learning algorithms can easily process. Here are some key points about embeddings in machine learning:
- An embedding is a relatively low-dimensional space into which you can translate high-dimensional vectors.
- Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words.
- Ideally, an embedding captures some of the semantics of the input by placing semantically similar inputs close together in the embedding space.
- Embeddings can be learned and reused across models.
- Embeddings are often used to represent categories in a transformed space, where embedding vectors that are close to each other are considered similar.
- Once learned, embeddings can be used as features for other machine learning models, such as classifiers or regressors.
- Embeddings can be used to assign a unique numerical ID to a word, an individual, a voice sound, an image, etc. .
- Embeddings can be trained with both unsupervised and supervised tasks.
- Tokens are mapped to vectors (embedded, represented), which are passed into neural networks.
- Embeddings are trained to represent data such that it makes the training task easy.
Overall, embeddings are a critical part of the data science toolkit and continue to gain in popularity. They are used in a variety of different fields including NLP, recommender systems, and computer vision.
