what is embedding in machine learning

what is embedding in machine learning

1 year ago 95
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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.

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