Data type: Vectors in machine learning usually hold floating-point numbers.

Number of elements: This is the vector's length or its dimension.

For example, consider a feature vector that holds eight floating-point numbers. This feature vector has a length or dimension of eight. Note that machine learning vectors often have a huge number of dimensions.

You can represent many different kinds of information as a vector. For example:

Any position on the surface of Earth can be represented as a 2-dimensional vector, where one dimension is the latitude and the other is the longitude.

The current prices of each of 500 stocks can be represented as a 500-dimensional vector.

A probability distribution over a finite number of classes can be represented as a vector. For example, a multiclass classification system that predicts one of three output colors (red, green, or yellow) could output the vector (0.3, 0.2, 0.5) to mean P[red]=0.3, P[green]=0.2, P[yellow]=0.5.

Vectors can be concatenated; therefore, a variety of different media can be represented as a single vector. Some models operate directly on the concatenation of many one-hot encodings.

Specialized processors such as TPUs are optimized to perform mathematical operations on vectors.

A vector is a tensor of rank 1.