Play
1
2
3
4
5
6

4. Eigenvalues and Eigenvectors

7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

19. Saddle Points Continued, Maxmin Principle

22

20. Definitions and Inequalities

23

Lecture 21: Minimizing a Function Step by Step

24

22. Gradient Descent: Downhill to a Minimum

25

23. Accelerating Gradient Descent (Use Momentum)

26

24. Linear Programming and Two-Person Games

27

25. Stochastic Gradient Descent

28

26. Structure of Neural Nets for Deep Learning

29

27. Backpropagation: Find Partial Derivatives

30

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

31

31. Eigenvectors of Circulant Matrices: Fourier Matrix

32
33

33. Neural Nets and the Learning Function

34

34. Distance Matrices, Procrustes Problem

35

35. Finding Clusters in Graphs

36

Lecture 36: Alan Edelman and Julia Language