23
Singular Value Decomposition: So Cool
Lecture no. 23 from the course: Mastering Linear Algebra: An Introduction with Applications
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Taught by Professor Francis Su | 32 min | Categories: Default Category
Next time you respond to a movie, music, or other online recommendation, think of the singular value decomposition (SVD), which is a matrix factorization method used to match your known preferences to similar products. Learn how SVD works, how to compute it, and how its ability to identify relevant attributes makes it an effective data compression tool for subtracting unimportant information.
24 Lectures
1
Linear Algebra: Powerful Transformations
0
of 28 min
2
Vectors: Describing Space and Motion
0
of 27 min
3
Linear Geometry: Dots and Crosses
0
of 28 min
4
Matrix Operations
0
of 31 min
5
Linear Transformations
0
of 28 min
6
Systems of Linear Equations
0
of 28 min
7
Reduced Row Echelon Form
0
of 28 min
8
Span and Linear Dependence
0
of 31 min
9
Subspaces: Special Subsets to Look For
0
of 29 min
10
Bases: Basic Building Blocks
0
of 29 min
11
Invertible Matrices: Undoing What You Did
0
of 30 min
12
The Invertible Matrix Theorem
0
of 34 min
13
Determinants: Numbers That Say a Lot
0
of 30 min
14
Eigenstuff: Revealing Hidden Structure
0
of 27 min
15
Eigenvectors and Eigenvalues: Geometry
0
of 29 min
16
Diagonalizability
0
of 32 min
17
Population Dynamics: Foxes and Rabbits
0
of 30 min
18
Differential Equations: New Applications
0
of 33 min
19
Orthogonality: Squaring Things Up
0
of 32 min
20
Markov Chains: Hopping Around
0
of 33 min
21
Multivariable Calculus: Derivative Matrix
0
of 31 min
22
Multilinear Regression: Least Squares
0
of 28 min
23
Singular Value Decomposition: So Cool
0
of 32 min
24
General Vector Spaces: More to Explore
0
of 34 min
