Vector embeddings refer to numerical representations of data. Each data point is represented by a vector in high-dimensional space. Here embeddings and vectors are the same thing.

Vector is an array of numbers with a specific dimensionality. Embeddings refers to the technique of representing data as vectors. These capture the underlying structure or properties of data.

Vector embeddings are created through an ML process. A model is trained to convert any pieces of data into numerical vectors.

A dataset is selected. It is preprocessed. A neural network model is selected that meets our data goals. Data is fed into the model. The model learns patterns and relationships within the data. (there is adjustment of internal parameters). To illustrate, it learns words that often appear together. The model after learning generates numerical vectors. Each data point (say a word or an image) is represented by a unique vector. At this point, the model’s effectiveness can be assessed by its performance on specific tasks or asking humans to evaluate it. If the embeddings are functioning well, it can be put to work.

Word embeddings can have dimensions ranging from a few hundred to a few thousand. Humans cannot visualize such a diagram. Sentence and documents embeddings may have more dimensions.

Vector embeddings are represented as sequence of numbers. Each number in the sequence corresponds to a specific feature or dimension and contributes to the overall representation of the data point.

The actual numbers within the vector are not meaningful on their own. The values and relationships are relative.

Applications of Vector Embeddings

They are used in NLP. They are used in search engines. They are used in personalized recommendation systems. They are used for visual content. They are used for anomaly detection. They are used in graph analysis. They are used in audio and music.