Gram-Schmidt Algorithm

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Covers the basics of linear algebra, including linear maps, bases, and matrix operations.

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

Explores the properties and applications of orthogonal matrices in linear algebra, focusing on orthogonality and projections.

Introduces orthogonal bases, projection onto subspaces, and the Gram-Schmidt process in linear algebra.

Explores matrix operations, linear systems, solutions, and the span of vectors in linear algebra.

Covers the properties and operations of vector spaces, including addition and scalar multiplication.

Explores matrix operations, rank determination, kernel dimensions, and basis concepts in linear algebra.

Covers the reduction of a linear application and finding corresponding reduced forms and bases.

Covers strategies for matrix operations and the concept of vector spaces.

Emphasizes the importance of using orthogonal bases in linear algebra for representing linear transformations.