Linear Algebra, Data Science, Applied Machine Learning.
leading to λ₁e₁. The same is true for the other eigenvector e₂, although its corresponding eigenvalue λ₂ is 1, hence after transformation we get e₂ itself. Each eigenvector has a norm of 1, and is orthogonal to all other eigenvectors (ie. dot product between them leads to 0). Each eigenvector can