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Course Introduction of 18.065 by Professor Strang
7:04
Course Introduction of 18.065 by Professor Strang
591.5K viewsMay 16, 2019
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Lecture 2: Multiplying and Factoring Matrices
48:26
Lecture 2: Multiplying and Factoring Matrices
172.9K viewsMay 16, 2019
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An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis, Signal Processing,...
8:07
An Interview with Gilbert Strang on Teaching Matrix Methods in Data …
89.5K viewsAug 19, 2019
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Syllabus | Matrix Methods in Data Analysis, Signal Processing, and Machine Learning | Mathematics | MIT OpenCourseWare
Syllabus | Matrix Methods in Data Analysis, Signal Processing, and …
Mar 31, 2022
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Lecture 21: Minimizing a Function Step by Step
53:45
Lecture 21: Minimizing a Function Step by Step
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5. Positive Definite and Semidefinite Matrices
45:27
5. Positive Definite and Semidefinite Matrices
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Lecture 30: Completing a Rank-One Matrix, Circulants!
49:53
Lecture 30: Completing a Rank-One Matrix, Circulants!
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49:21
Lecture 8: Norms of Vectors and Matrices
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49:28
12. Computing Eigenvalues and Singular Values
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49:00
Lecture 18: Counting Parameters in SVD, LU, QR, Saddle Points
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38:11
Lecture 36: Alan Edelman and Julia Language
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52:24
Lecture 13: Randomized Matrix Multiplication
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52:15
Lecture 1: The Column Space of A Contains All Vectors Ax
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49:02
23. Accelerating Gradient Descent (Use Momentum)
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48:56
4. Eigenvalues and Eigenvectors
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52:37
31. Eigenvectors of Circulant Matrices: Fourier Matrix
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49:36
Lecture 10: Survey of Difficulties with Ax = b
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49:24
3. Orthonormal Columns in Q Give Q'Q = I
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53:34
6. Singular Value Decomposition (SVD)
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53:17
26. Structure of Neural Nets for Deep Learning
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43:08
16. Derivatives of Inverse and Singular Values
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34:49
35. Finding Clusters in Graphs
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47:16
7. Eckart-Young: The Closest Rank k Matrix to A
96.7K viewsJul 18, 2019
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50:22
Lecture 11: Minimizing ‖x‖ Subject to Ax = b
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49:51
9. Four Ways to Solve Least Squares Problems
135.1K viewsMay 16, 2019
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53:34
24. Linear Programming and Two-Person Games
80K viewsMay 16, 2019
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53:03
25. Stochastic Gradient Descent
96.8K viewsMay 16, 2019
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47:19
Lecture 32: ImageNet is a Convolutional Neural Network (CN…
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55:01
20. Definitions and Inequalities
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50:52
15. Matrices A(t) Depending on t, Derivative = dA/dt
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