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Episodes


Lecture 30: Completing a Rank-One Matrix, Circulants!

26. Structure of Neural Nets for Deep Learning

27. Backpropagation: Find Partial Derivatives

25. Stochastic Gradient Descent

24. Linear Programming and Two-Person Games

23. Accelerating Gradient Descent (Use Momentum)

22. Gradient Descent: Downhill to a Minimum

Lecture 21: Minimizing a Function Step by Step

20. Definitions and Inequalities

19. Saddle Points Continued, Maxmin Principle

Lecture 18: Counting Parameters in SVD, LU, QR, Saddle Points

Lecture 17: Rapidly Decreasing Singular Values

16. Derivatives of Inverse and Singular Values

15. Matrices A(t) Depending on t, Derivative = dA/dt

14. Low Rank Changes in A and Its Inverse

Lecture 13: Randomized Matrix Multiplication

12. Computing Eigenvalues and Singular Values

Lecture 11: Minimizing ‖x‖ Subject to Ax = b

Lecture 10: Survey of Difficulties with Ax = b
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