Linear Algebra in AI

How is linear algebra used in AI?

Linear algebra includes vectors (ordered list of numbers), matrices and their linear transformations.

In AI, linear algebra is used for data representation. Data is often represented as vectors or matrices. For example, in machine learning, datasets can be represented as matrices where each row corresponds to a data point (such as an image or a sentence) and each column corresponds to a feature (such as pixel values or word embeddings).

It is also used for data transformation. Transformations such as scaling, rotation, and translation of data points can be represented as matrix operations. For example, in computer vision, images can be transformed using matrices (like for resizing or rotating).

In social network analysis and recommendation systems, data can be represented as graphs, where perform matrix operations on adjacency matrices and Laplacian matrices are performed, helping identify clusters or communities within the data.

Isn't it fascinating how Math forms the backbone of so many technological advancements!