Linear Algebra for Machine Learning

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6:32:20

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01-Topics.mp4

00:33

02-Topics.mp4

00:37

03-3.1 Tensor Transposition.mp4

03:37

04-3.5 Exercises.mp4

05:38

05-3.2 Basic Tensor Arithmetic.mp4

05:56

06-Topics.mp4

00:24

07-5.4 Exercises.mp4

08:31

08-6.4 Orthogonal Matrices.mp4

04:23

09-6.2 Matrix Inversion.mp4

16:15

10-Topics.mp4

00:26

11-Topics.mp4

00:43

12-1.1 Defining Linear Algebra.mp4

06:35

13-1.5 Exercise.mp4

08:49

14-4.1 The Substitution Strategy.mp4

03:40

15-4.2 Substitution Exercises.mp4

08:41

16-4.4 Elimination Exercises.mp4

09:49

17-Topics.mp4

00:40

18-5.3 Symmetric and Identity Matrices.mp4

05:13

19-7.4 High-Dimensional Eigenvectors.mp4

03:54

20-1.2 Solving a System of Equations Algebraically.mp4

06:42

21-Linear Algebra for Machine Learning (Machine Learning Foundations) - Introduction.mp4

02:54

22-1.3 Linear Algebra in Machine Learning and Deep Learning.mp4

10:51

23-2.1 Tensors.mp4

04:35

24-2.7 Generic Tensor Notation.mp4

04:46

25-2.3 Vectors and Vector Transposition.mp4

11:15

26-Topics.mp4

00:21

27-3.3 Reduction.mp4

04:39

28-2.4 Norms and Unit Vectors.mp4

15:35

29-2.5 Basis, Orthogonal, and Orthonormal Vectors.mp4

04:47

30-4.3 The Elimination Strategy.mp4

04:10

31-5.2 Matrix-by-Matrix Multiplication.mp4

09:44

32-5.1 Matrix-by-Vector Multiplication.mp4

12:01

33-3.4 The Dot Product.mp4

06:04

34-5.5 Machine Learning and Deep Learning Applications.mp4

11:45

35-6.1 The Frobenius Norm.mp4

03:39

36-7.1 The Eigenconcept.mp4

09:00

37-6.3 Diagonal Matrices.mp4

03:41

38-7.2 Exercises.mp4

09:29

39-8.1 The Determinant of a 2 x 2 Matrix.mp4

06:02

40-Topics.mp4

00:38

41-8.3 Exercises.mp4

03:59

42-8.2 The Determinants of Larger Matrices.mp4

07:18

43-8.4 Determinants and Eigenvalues.mp4

08:43

44-9.4 Regression via Pseudoinversion.mp4

12:47

45-9.2 Media File Compression.mp4

06:57

46-9.1 Singular Value Decomposition.mp4

08:09

47-9.3 The Moore-Penrose Pseudoinverse.mp4

08:44

48-9.6 Resources for Further Study of Linear Algebra.mp4

03:54

49-1.4 Historical and Contemporary Applications.mp4

07:36

50-2.2 Scalars.mp4

23:03

51-2.6 Matrices.mp4

07:39

52-2.8 Exercises.mp4

02:11

53-Topics.mp4

00:24

54-6.5 The Trace Operator.mp4

03:45

55-7.3 Eigenvectors in Python.mp4

28:00

56-8.5 Eigendecomposition.mp4

14:01

57-Linear Algebra for Machine Learning (Machine Learning Foundations) - Summary.mp4

01:11

58-9.5 Principal Component Analysis.mp4

06:57

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