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Related lectures (30)
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Singular Value Decomposition: Applications and Interpretation
Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.
Linear Systems Resolution
Covers the resolution of linear systems using the Gauss method to determine unique, infinite, or no solutions.
Vectorization in Python: Efficient Computation with Numpy
Covers vectorization in Python using Numpy for efficient scientific computing, emphasizing the benefits of avoiding for loops and demonstrating practical applications.
Linear Systems: Direct Methods
Covers the formulation of linear systems, direct and iterative methods for solving them, and the cost of LU factorization.
Numerical Analysis: Linear Systems
Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.
Characteristic Polynomials and Similar Matrices
Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.
Numerical Analysis: Direct Methods for Linear Systems
Covers direct methods for solving linear systems in numerical analysis.
Matrix Properties
Explores matrix shapes, compatibility, and invertibility, including staggered shapes and system compatibility.
Linear Equations: Vectors and Matrices
Covers linear equations, vectors, and matrices, exploring their fundamental concepts and applications.
Linear Equations: Matrix Notation & Solutions
Covers matrix notation for linear equations, solutions, and geometric interpretations.