GitHub - alexchonglian/MIT18.065-subtitles: MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

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MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

链接: https://pan.baidu.com/s/1mqqTDSsVH9YjBkjCt4lIdQ 提取码: kh45

字幕是自动识别和翻译的，不精确。放在GitHub上了，各位朋友有兴趣的可以一起修正翻译（fork or pull request）。

Course Material: https://ocw.mit.edu/18-065S18

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Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
00-Course Introduction of 18.065 by Professor Strang.srt		00-Course Introduction of 18.065 by Professor Strang.srt
01-The Column Space of A Contains All Vectors Ax.srt		01-The Column Space of A Contains All Vectors Ax.srt
02-Multiplying and Factoring Matrices.srt		02-Multiplying and Factoring Matrices.srt
03-Orthonormal Columns in Q Give Q'Q = I.srt		03-Orthonormal Columns in Q Give Q'Q = I.srt
04-Eigenvalues and Eigenvectors.srt		04-Eigenvalues and Eigenvectors.srt
05-Positive Definite and Semidefinite Matrices.srt		05-Positive Definite and Semidefinite Matrices.srt
06-Singular Value Decomposition (SVD).srt		06-Singular Value Decomposition (SVD).srt
07-Eckart-Young - The Closest Rank k Matrix to A.srt		07-Eckart-Young - The Closest Rank k Matrix to A.srt
08-Norms of Vectors and Matrices.srt		08-Norms of Vectors and Matrices.srt
09-Four Ways to Solve Least Squares Problems.srt		09-Four Ways to Solve Least Squares Problems.srt
10-Survey of Difficulties with Ax = b.srt		10-Survey of Difficulties with Ax = b.srt
11-Minimizing _x_ Subject to Ax = b.srt		11-Minimizing _x_ Subject to Ax = b.srt
12-Computing Eigenvalues and Singular Values.srt		12-Computing Eigenvalues and Singular Values.srt
13-Randomized Matrix Multiplication.srt		13-Randomized Matrix Multiplication.srt
14-Low Rank Changes in A and Its Inverse.srt		14-Low Rank Changes in A and Its Inverse.srt
15-Matrices A(t) Depending on t, Derivative = dA_dt.srt		15-Matrices A(t) Depending on t, Derivative = dA_dt.srt
16-Derivatives of Inverse and Singular Values.srt		16-Derivatives of Inverse and Singular Values.srt
17-Rapidly Decreasing Singular Values.srt		17-Rapidly Decreasing Singular Values.srt
18-Counting Parameters in SVD, LU, QR, Saddle Points.srt		18-Counting Parameters in SVD, LU, QR, Saddle Points.srt
19-Saddle Points Continued, Maxmin Principle.srt		19-Saddle Points Continued, Maxmin Principle.srt
20-Definitions and Inequalities.srt		20-Definitions and Inequalities.srt
21-Minimizing a Function Step by Step.srt		21-Minimizing a Function Step by Step.srt
22-Gradient Descent - Downhill to a Minimum.srt		22-Gradient Descent - Downhill to a Minimum.srt
23-Accelerating Gradient Descent (Use Momentum).srt		23-Accelerating Gradient Descent (Use Momentum).srt
24-Linear Programming and Two-Person Games.srt		24-Linear Programming and Two-Person Games.srt
25-Stochastic Gradient Descent.srt		25-Stochastic Gradient Descent.srt
26-Structure of Neural Nets for Deep Learning.srt		26-Structure of Neural Nets for Deep Learning.srt
27-Backpropagation - Find Partial Derivatives.srt		27-Backpropagation - Find Partial Derivatives.srt
30-Completing a Rank-One Matrix, Circulants!.srt		30-Completing a Rank-One Matrix, Circulants!.srt
31-Eigenvectors of Circulant Matrices - Fourier Matrix.srt		31-Eigenvectors of Circulant Matrices - Fourier Matrix.srt
32-ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule.srt		32-ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule.srt
33-Neural Nets and the Learning Function.srt		33-Neural Nets and the Learning Function.srt
34-Distance Matrices, Procrustes Problem.srt		34-Distance Matrices, Procrustes Problem.srt
35-Finding Clusters in Graphs.srt		35-Finding Clusters in Graphs.srt
36-Alan Edelman and Julia Language.srt		36-Alan Edelman and Julia Language.srt
README.md		README.md

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