1. 선형대수의 기초

본 글은 주재걸교수님의 인공지능을 위한 선형대수 강의를 듣고 정리한 내용입니다.

scalar, vector, matrix

• Scalar : a single number s ∈R
• Vector : an ordered list of numbers (unordered list : Set)
• Matrix : a tow-dimensional array of numbers
  
  • Row Vector : a horizontal vector
  • Column Vector : a vertical vector

 

RowVector

Column Vector

 

• Square Matrix(#rows = #columns)
• Rectangular Matrix(#rows ≠ #columns)
• Transpose of Matrix

 

• Element-wise addition(Matrix should have the same size)
• Scalar multipe of Matrix
• Matrix-matrix multiplication
  • AB ≠ BA : Matrix multiplication is NOT commutative(교환법칙 성립 X)sssssss

Matrix-matrix multiplication

 

• A(B+C) = AB+AC : Distributive
• A(BC) = (AB)C : Associative
• (AB)T = BTAT : Property of tranpose
• (AB)-1 = B-1A-1

 

 

출처: https://www.edwith.org/ai251 

인공지능을 위한 선형대수 강좌소개 : edwith

참고 자료

https://www.mathsisfun.com/algebra/matrix-multiplying.html

https://ko.wikipedia.org/wiki/%ED%96%89%EB%A0%AC_%EA%B3%B1%EC%85%88

https://hi-datalab.tistory.com/6