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18. Singular Value Decomposition (SVD) - 2
2023年9月23日 1002观看
艾伦·爱德曼和茱莉亚
大学课程 / 外语
https://ocw.mit.edu/18-065S18 MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Professor Strang describes the four topics of the course: Linear Algebra, Deep Learning, Optimization, and Statistics.
共102集
11.5万人观看
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Course Introduction of 18.065 by Professor Strang
07:03
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The Column Space of A Contains All Vectors Ax - 1
17:27
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The Column Space of A Contains All Vectors Ax - 2
17:28
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The Column Space of A Contains All Vectors Ax - 3
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Multiplying and Factoring Matrices - 1
16:11
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Multiplying and Factoring Matrices - 2
16:14
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Multiplying and Factoring Matrices - 3
16:03
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Orthonormal Columns in Q Give Q'Q = I - 1
16:31
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Orthonormal Columns in Q Give Q'Q = I - 2
16:38
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Orthonormal Columns in Q Give Q'Q = I - 3
16:28
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Eigenvalues and Eigenvectors - 1
16:21
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Eigenvalues and Eigenvectors - 2
16:22
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Eigenvalues and Eigenvectors - 3
16:21
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Positive Definite and Semidefinite Matrices - 1
15:12
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Positive Definite and Semidefinite Matrices - 2
15:19
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Positive Definite and Semidefinite Matrices - 3
15:03
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Singular Value Decomposition (SVD) - 1
17:54
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Singular Value Decomposition (SVD) - 2
17:59
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Singular Value Decomposition (SVD) - 3
17:51
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Eckart-Young - The Closest Rank k Matrix to A - 1
15:48
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Eckart-Young - The Closest Rank k Matrix to A - 2
15:49
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Eckart-Young - The Closest Rank k Matrix to A - 3
15:46
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Norms of Vectors and Matrices - 1
16:30
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Norms of Vectors and Matrices - 2
16:30
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Norms of Vectors and Matrices - 3
16:26
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Four Ways to Solve Least Squares Problems - 1
16:40
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Four Ways to Solve Least Squares Problems - 2
16:41
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Four Ways to Solve Least Squares Problems - 3
16:32
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Survey of Difficulties with Ax = b - 1
16:35
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Survey of Difficulties with Ax = b - 2
16:39
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Survey of Difficulties with Ax = b - 3
16:27
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Minimizing _x_ Subject to Ax = b - 1
16:50
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Minimizing _x_ Subject to Ax = b - 2
16:52
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Minimizing _x_ Subject to Ax = b - 3
16:46
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Computing Eigenvalues and Singular Values - 1
16:32
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Computing Eigenvalues and Singular Values - 2
16:38
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Computing Eigenvalues and Singular Values - 3
16:29
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Randomized Matrix Multiplication - 1
17:31
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Randomized Matrix Multiplication - 2
17:36
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Randomized Matrix Multiplication - 3
17:29
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Low Rank Changes in A and Its Inverse - 1
16:54
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Low Rank Changes in A and Its Inverse - 2
16:55
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Low Rank Changes in A and Its Inverse - 3
16:49
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Matrices A(t) Depending on t, Derivative = dA_dt - 1
17:00
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Matrices A(t) Depending on t, Derivative = dA_dt - 2
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Matrices A(t) Depending on t, Derivative = dA_dt - 3
16:54
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Derivatives of Inverse and Singular Values - 1
14:25
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Derivatives of Inverse and Singular Values - 2
14:32
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Derivatives of Inverse and Singular Values - 3
14:25
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Rapidly Decreasing Singular Values - 1
16:54
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Rapidly Decreasing Singular Values - 2
16:56
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Rapidly Decreasing Singular Values - 3
16:52
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Counting Parameters in SVD, LU, QR, Saddle Points - 1
16:23
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Counting Parameters in SVD, LU, QR, Saddle Points - 2
16:24
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Counting Parameters in SVD, LU, QR, Saddle Points - 3
16:16
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Saddle Points Continued, Maxmin Principle - 1
17:27
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Saddle Points Continued, Maxmin Principle - 2
17:32
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Saddle Points Continued, Maxmin Principle - 3
17:27
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Definitions and Inequalities - 1
18:23
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Definitions and Inequalities - 2
18:30
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Definitions and Inequalities - 3
18:19
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Minimizing a Function Step by Step - 1
17:57
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Minimizing a Function Step by Step - 2
18:02
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Minimizing a Function Step by Step - 3
17:50
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Gradient Descent - Downhill to a Minimum - 1
17:37
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Gradient Descent - Downhill to a Minimum - 2
17:39
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Gradient Descent - Downhill to a Minimum - 3
17:36
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Accelerating Gradient Descent (Use Momentum) - 1
16:23
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Accelerating Gradient Descent (Use Momentum) - 2
16:23
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Accelerating Gradient Descent (Use Momentum) - 3
16:23
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Linear Programming and Two-Person Games - 1
17:54
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Linear Programming and Two-Person Games - 2
18:00
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Linear Programming and Two-Person Games - 3
17:52
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Stochastic Gradient Descent - 1
17:43
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Stochastic Gradient Descent - 2
17:49
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Stochastic Gradient Descent - 3
17:37
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Structure of Neural Nets for Deep Learning - 1
17:48
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Structure of Neural Nets for Deep Learning - 2
17:54
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Structure of Neural Nets for Deep Learning - 3
17:47
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Backpropagation - Find Partial Derivatives - 1
17:35
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Backpropagation - Find Partial Derivatives - 2
17:35
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Backpropagation - Find Partial Derivatives - 3
17:36
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Completing a Rank-One Matrix, Circulants! - 1
16:40
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Completing a Rank-One Matrix, Circulants! - 2
16:44
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Completing a Rank-One Matrix, Circulants! - 3
16:34
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Eigenvectors of Circulant Matrices - Fourier Matrix - 1
17:35
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Eigenvectors of Circulant Matrices - Fourier Matrix - 2
17:36
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Eigenvectors of Circulant Matrices - Fourier Matrix - 3
17:28
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ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule - 1
15:49
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ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule - 2
15:50
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ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule - 3
15:43
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Neural Nets and the Learning Function - 1
18:45
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Neural Nets and the Learning Function - 2
18:48
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Neural Nets and the Learning Function - 3
18:44
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Distance Matrices, Procrustes Problem - 1
14:40
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Distance Matrices, Procrustes Problem - 3
14:37
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Finding Clusters in Graphs - 1
11:39
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Finding Clusters in Graphs - 2
11:40
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Finding Clusters in Graphs - 3
11:35
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Alan Edelman and Julia Language - 1
12:46
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Alan Edelman and Julia Language - 2
12:50
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Alan Edelman and Julia Language - 3
12:45
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