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This course includes
- 28h 9m of video
- 36 lessons
- Level: Advanced
- Spoken in English
- Your place is remembered
- Free in full, no card
Lessons
4 sections · 36 lessons · 28h 9m total length
Lessons 1 to 1010 lessons · 6h 50m
- Course Introduction of 18.065 by Professor Strang7m
- An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis, Signal Processing,...8m
- Lecture 1: The Column Space of A Contains All Vectors Ax52m
- Lecture 2: Multiplying and Factoring Matrices48m
- 3. Orthonormal Columns in Q Give Q'Q = I49m
- 4. Eigenvalues and Eigenvectors49m
- 5. Positive Definite and Semidefinite Matrices45m
- 6. Singular Value Decomposition (SVD)54m
- 7. Eckart-Young: The Closest Rank k Matrix to A47m
- Lecture 8: Norms of Vectors and Matrices49m
Lessons 11 to 2010 lessons · 8h 16m
- 9. Four Ways to Solve Least Squares Problems50m
- Lecture 10: Survey of Difficulties with Ax = b50m
- Lecture 11: Minimizing ‖x‖ Subject to Ax = b50m
- 12. Computing Eigenvalues and Singular Values49m
- Lecture 13: Randomized Matrix Multiplication52m
- 14. Low Rank Changes in A and Its Inverse51m
- 15. Matrices A(t) Depending on t, Derivative = dA/dt51m
- 16. Derivatives of Inverse and Singular Values43m
- Lecture 17: Rapidly Decreasing Singular Values51m
- Lecture 18: Counting Parameters in SVD, LU, QR, Saddle Points49m
Lessons 21 to 3010 lessons · 8h 45m
- 19. Saddle Points Continued, Maxmin Principle52m
- 20. Definitions and Inequalities55m
- Lecture 21: Minimizing a Function Step by Step54m
- 22. Gradient Descent: Downhill to a Minimum53m
- 23. Accelerating Gradient Descent (Use Momentum)49m
- 24. Linear Programming and Two-Person Games54m
- 25. Stochastic Gradient Descent53m
- 26. Structure of Neural Nets for Deep Learning53m
- 27. Backpropagation: Find Partial Derivatives53m
- Lecture 30: Completing a Rank-One Matrix, Circulants!50m
Lessons 31 to 366 lessons · 4h 18m
- 31. Eigenvectors of Circulant Matrices: Fourier Matrix53m
- Lecture 32: ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule47m
- 33. Neural Nets and the Learning Function56m
- 34. Distance Matrices, Procrustes Problem29m
- 35. Finding Clusters in Graphs35m
- Lecture 36: Alan Edelman and Julia Language38m
What you need
- Experience expected: this course is pitched at an advanced level.
Description
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
Who this course is for
- People already working with Machine learning.
Those two are our reading of the course's level. The description is the author's own.