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本课程包含
- 28小时9分钟 视频
- 36 节课
- 难度：高阶
- 语言：English
- 记住你看到哪里
- 完全免费，无需银行卡
课时
4 个部分 · 36 节课 · 28小时9分钟 总时长
第 1 至 10 节10 节课 · 6小时50分钟
- Course Introduction of 18.065 by Professor Strang7分钟
- An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis, Signal Processing,...8分钟
- Lecture 1: The Column Space of A Contains All Vectors Ax52分钟
- Lecture 2: Multiplying and Factoring Matrices48分钟
- 3. Orthonormal Columns in Q Give Q'Q = I49分钟
- 4. Eigenvalues and Eigenvectors49分钟
- 5. Positive Definite and Semidefinite Matrices45分钟
- 6. Singular Value Decomposition (SVD)54分钟
- 7. Eckart-Young: The Closest Rank k Matrix to A47分钟
- Lecture 8: Norms of Vectors and Matrices49分钟
第 11 至 20 节10 节课 · 8小时16分钟
- 9. Four Ways to Solve Least Squares Problems50分钟
- Lecture 10: Survey of Difficulties with Ax = b50分钟
- Lecture 11: Minimizing ‖x‖ Subject to Ax = b50分钟
- 12. Computing Eigenvalues and Singular Values49分钟
- Lecture 13: Randomized Matrix Multiplication52分钟
- 14. Low Rank Changes in A and Its Inverse51分钟
- 15. Matrices A(t) Depending on t, Derivative = dA/dt51分钟
- 16. Derivatives of Inverse and Singular Values43分钟
- Lecture 17: Rapidly Decreasing Singular Values51分钟
- Lecture 18: Counting Parameters in SVD, LU, QR, Saddle Points49分钟
第 21 至 30 节10 节课 · 8小时45分钟
- 19. Saddle Points Continued, Maxmin Principle52分钟
- 20. Definitions and Inequalities55分钟
- Lecture 21: Minimizing a Function Step by Step54分钟
- 22. Gradient Descent: Downhill to a Minimum53分钟
- 23. Accelerating Gradient Descent (Use Momentum)49分钟
- 24. Linear Programming and Two-Person Games54分钟
- 25. Stochastic Gradient Descent53分钟
- 26. Structure of Neural Nets for Deep Learning53分钟
- 27. Backpropagation: Find Partial Derivatives53分钟
- Lecture 30: Completing a Rank-One Matrix, Circulants!50分钟
第 31 至 36 节6 节课 · 4小时18分钟
- 31. Eigenvectors of Circulant Matrices: Fourier Matrix53分钟
- Lecture 32: ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule47分钟
- 33. Neural Nets and the Learning Function56分钟
- 34. Distance Matrices, Procrustes Problem29分钟
- 35. Finding Clusters in Graphs35分钟
- Lecture 36: Alan Edelman and Julia Language38分钟
需要什么
- 需要经验：课程为进阶水平。
课程介绍
Instructor: Gilbert Strang View the complete course: https://ocw.mit.edu/18-065S18 Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
适合谁
- 已经在做Machine learning的人。
这两项是我们根据课程难度得出的判断，课程介绍是作者本人写的。