超級推薦！Mathematics for Machine Learning

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課程視頻與圖解筆記視頻目錄（共三課）

第一課：Linear Algebra for Machine Learning (已完結）

只有含「圖解」的視頻是筆記視頻，其他視頻為原課程視頻

1、introduction solving data science challenges with math

2、motivation for linear algebra

1、圖解學習linear algebra目的：高效求解大量linear equations 2、圖解學習linear algebra目的：高效求解模型參數擬合大量數據 3、圖解線性代數：線代如何切入幫助解決模型優化

3、getting a handle on vectors

4、operations with vectors

5、week1 summary

6、Introduction to module2 vectors

7、modulus and inner product

4、圖解線性代數：向量的本質和加乘運算 5、圖解線性代數：vector length and dot product

8、cosine and dot product

6、圖解線性代數：vector dot product, length, cosine

9、projection

7、圖解線性代數：scalar projection, vector projection 8、圖解線性代數：vector projection 與 vector transformation

10、changing basis

9、圖解線性代數：linear combination, independence, basis vector

11、basis, vector space, linear independence

10、圖解線性代數：vector, projection, basis vector在機器學習中的應用

12、applications of changing basis

13、summary week 2

14、matrices, vectors, soliving simultaneous equations

11、圖解線性代數：matrix簡介

15、how matrices transform space

12、圖解線性代數：用映射projection來理解matrix的變形能力

16、types of matrix transformation

13、圖解線性代數：機器學習的本質與matrix transformation的各種形態

17、composition or combination of matrix transformations

14、圖解線性代數：matrix疊加變形的直觀理解

18、solving apples banana problem gaussian elimination

19、going from gaussian elimination to finding the inverse matrix

15、圖解線性代數：inverse與identity matrix的理解和求解

20、determinants and inverses

16、圖解線性代數：determinant, inverse, dependence, solution

21、summary

22、introduction Einstein summation convention and symmetry of dot product

17、圖解線性代數：einstein summation convention and dot product的對稱性

23、doing a transformation in a changed basis

24、matrices changing basis

25、orthogonal matrices

18、圖解線性代數：我的視野里的vector, basis vector在panda的視野里長得什麼樣子 19、圖解線性代數：案例熊貓與人類的視野轉換 20、圖解線性代數：如何理解orthogonal matrix

26、the Gram Smchidt process

21、圖解線性代數：gram-schmidt與轉換坐標空間來實現vector變形

27、Example reflecting in a plane

28、welcome to module 5

29、what are eigenvalues and eigenvectors

30、special eigen cases

31、calculating eigenvectors

32、changing to eigenbasis

33、eigenbasis example

22、圖解線性代數：eigenvectors and eigenvalues 23、圖解線性代數：eigenvectors and eigenvalues的求解邏輯 24、圖解線性代數：用eigenvector, eigenvalues化簡n個m

34、pagerank

35、wrap up

36、summary

Multivariate Calculus for Machine Learning

37、welcome to multivariate calculus

38、welcome to module 1

39、functions

40、rise over run

41、definition of a derivative

42、differentiation examples and special cases

43、product rule

44、chain rule

45、taming a best

46、see you next module

47、welcome to Module 2

48、variables, constants and context

49、differentiate with respect to anything

50、the jacobian

51、Jacobian applied

52、the sandpit

53、the hessian

54、reality is hard

55、see you next module

56、welcome to module 3

57、multivariate chain rule

58、more multivariate chain rule

59、simple neural networks

60、more simple neural networks

61、see you next module

62、welcome to module 4

63、building approximate functions

64、power series

65、power series derivation

66、power series details

67、examples

68、linearisation

69、multivariate taylor

70、see you next module

71、welcome to module 5

72、gradient descent

73、constrained optimization

74、see you next module

75、simple linear regression

76、general non linear least squares

77、doing least squares regression analysis in practice

78、wrap up of this course