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Data Analysis Signal Processing

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Track :

Technology

Lessons no : 36

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Lessons | 36
Course Introduction of 18 065 by Professor Strang
00:07:04

Course Introduction of 18 065 by Professor Strang
An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis Signal Processing
00:08:07

An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis Signal Processing
Lecture 1 The Column Space of A Contains All Vectors Ax
00:52:15

Lecture 1 The Column Space of A Contains All Vectors Ax
Lecture 2 Multiplying and Factoring Matrices
00:48:26

Lecture 2 Multiplying and Factoring Matrices
3 Orthonormal Columns in Q Give Q Q I
00:49:24

3 Orthonormal Columns in Q Give Q Q I
4 Eigenvalues and Eigenvectors
00:48:56

4 Eigenvalues and Eigenvectors
5 Positive Definite and Semidefinite Matrices
00:45:28

5 Positive Definite and Semidefinite Matrices
6 Singular Value Decomposition SVD
00:53:34

6 Singular Value Decomposition SVD
7 Eckart Young The Closest Rank k Matrix to A
00:47:16

7 Eckart Young The Closest Rank k Matrix to A
Lecture 8 Norms of Vectors and Matrices
00:49:21

Lecture 8 Norms of Vectors and Matrices
9 Four Ways to Solve Least Squares Problems
00:49:51

9 Four Ways to Solve Least Squares Problems
Lecture 10 Survey of Difficulties with Ax b
00:49:36

Lecture 10 Survey of Difficulties with Ax b
Lecture 11 Minimizing ‖x‖ Subject to Ax b
00:50:22

Lecture 11 Minimizing ‖x‖ Subject to Ax b
12 Computing Eigenvalues and Singular Values
00:49:28

12 Computing Eigenvalues and Singular Values
Lecture 13 Randomized Matrix Multiplication
00:52:24

Lecture 13 Randomized Matrix Multiplication
14 Low Rank Changes in A and Its Inverse
00:50:34

14 Low Rank Changes in A and Its Inverse
15 Matrices A t Depending on t Derivative dA dt
00:50:52

15 Matrices A t Depending on t Derivative dA dt
16 Derivatives of Inverse and Singular Values
00:43:08

16 Derivatives of Inverse and Singular Values
Lecture 17 Rapidly Decreasing Singular Values
00:50:34

Lecture 17 Rapidly Decreasing Singular Values
Lecture 18 Counting Parameters in SVD LU QR Saddle Points
00:49:00

Lecture 18 Counting Parameters in SVD LU QR Saddle Points
19 Saddle Points Continued Maxmin Principle
00:52:13

19 Saddle Points Continued Maxmin Principle
20 Definitions and Inequalities
00:55:01

20 Definitions and Inequalities
Lecture 21 Minimizing a Function Step by Step
00:53:45

Lecture 21 Minimizing a Function Step by Step
22 Gradient Descent Downhill to a Minimum
00:52:44

22 Gradient Descent Downhill to a Minimum
23 Accelerating Gradient Descent Use Momentum
00:49:02

23 Accelerating Gradient Descent Use Momentum
24 Linear Programming and Two Person Games
00:53:34

24 Linear Programming and Two Person Games
25 Stochastic Gradient Descent
00:53:03

25 Stochastic Gradient Descent
26 Structure of Neural Nets for Deep Learning
00:53:17

26 Structure of Neural Nets for Deep Learning
27 Backpropagation Find Partial Derivatives
00:52:38

27 Backpropagation Find Partial Derivatives
Lecture 30 Completing a Rank One Matrix Circulants
00:49:53

Lecture 30 Completing a Rank One Matrix Circulants
31 Eigenvectors of Circulant Matrices Fourier Matrix
00:52:37

31 Eigenvectors of Circulant Matrices Fourier Matrix
Lecture 32 ImageNet is a Convolutional Neural Network CNN The Convolution Rule
00:47:19

Lecture 32 ImageNet is a Convolutional Neural Network CNN The Convolution Rule
33 Neural Nets and the Learning Function
00:56:07

33 Neural Nets and the Learning Function
34 Distance Matrices Procrustes Problem
00:29:17

34 Distance Matrices Procrustes Problem
35 Finding Clusters in Graphs
00:34:49

35 Finding Clusters in Graphs
Lecture 36 Alan Edelman and Julia Language
00:38:11

Lecture 36 Alan Edelman and Julia Language


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Signal is the real pattern, the repeatable process that we hope to capture and describe. It is the information that we care about. The signal is what lets the model generalize to new situations .