Generalized eigenvector

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Explores PCA for predicting medals distribution and compressing face images.

Explores specific solutions and modal base in a generalized conservative oscillator.

Explores the diagonalization of matrices through eigenvectors and eigenvalues.

Explores eigenvalues, eigenvectors, diagonalization, and spectral theorem in linear algebra.

Explores the application of matrices and eigendecompositions in networks.

Explores eigenvalues and eigenvectors in matrix transformations, essential for understanding mathematical and real-world systems.

Explains cofactors, eigenvectors, eigenvalues, and studying linear applications.

Explores canonical equations, integrable systems, trajectories, and the symplectic matrix in understanding system dynamics.

Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.

Covers the diagonalization of matrices with distinct eigenvalues and the importance of this process.