Вектор

Very overloaded term whose meaning varies across different mathematical and scientific fields. Within machine learning, a vector has two properties: - 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 to mean . 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.

A vector refers to a numerical representation of text, images, or videos that captures relationships between inputs. Machine learning models are suited for creating embeddings by identifying patterns within large datasets. Applications can use embeddings to process and produce language, recognizing complex meanings and semantic relationships specific to the content. For more information, see Embeddings APIs overview.