Lectures related to Eigen and Pybind Libraries

Explores eigenvalues and eigenvectors in 3D linear algebra, covering characteristic polynomials, stability under transformations, and real roots.

Introduces eigenspaces in linear algebra through definitions and practical examples of determining eigenspaces for matrices.

Explores operations and applications of linear algebra in solving equations and transformations.

Explores linear algebra basics, emphasizing matrix representations of transformations and the importance of choosing appropriate bases.

Explores the application of Schur's Lemmas in physics, emphasizing operator properties and vector actions.

Explores the Eigen library for linear algebra, covering vectors, matrices, arrays, memory management, reshaping, and per-component operations.

Explores linear dependence and independence of vectors in geometric spaces.

Covers linear applications, matrices, transformations, and the principle of superposition.

Covers linear independence, bases, and coordinate systems with examples and theorems.

Covers the basics of linear algebra, emphasizing the identification of subspaces through key properties.

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

Explores dynamic memory allocation in C, covering malloc, calloc, and realloc functions, emphasizing proper memory management and dynamic arrays.

Covers linear equations, vectors, and matrices, exploring their fundamental concepts and applications.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Explains linear independence, bases, and dimension in vector spaces, including the importance of the order of vectors in a basis.

Covers the concept of expressing vectors in proper bases and the importance of diagonalization of matrices.

Explores linear algebra over integers, emphasizing solutions to A.x=b equations and Hermite normal form.

Explores landmarks, coordinates, vectors, coplanarity, Cartesian equations, and geometric rules in space.

Covers linear transformations using matrices, focusing on linearity, image, and kernel.

Explores orthogonality between vectors and subspaces, demonstrating practical implications in matrix operations.