Embeddings are a machine learning concept that translates objects like words, sentences, or users into a vector of numbers. They are a type of representation learning, allowing models to understand and use complex inputs. Text embeddings, for instance, let models comprehend language by mapping words or phrases into numerical vectors, capturing semantic relationships.
Embeddings are created from raw data using unsupervised learning algorithms. The process begins with random vectors, which are then adjusted based on the data, resulting in embeddings that reflect the underlying patterns.
The cosine similarity between two vectors is often used to measure the similarity of the corresponding objects. This metric ranges from -1 to 1, where 1 indicates identical vectors, 0 suggests no relationship, and -1 means the vectors are diametrically opposed.
Embeddings are used in recommendation systems, natural language processing, and many other machine learning applications. They allow models to make predictions about new data by understanding the relationships between different pieces of information.
It’s important to note that embeddings can unintentionally capture and perpetuate biases present in the training data. Therefore, it’s crucial to use them responsibly and consider potential ethical implications.
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