《白话机器学习》(一)机器学习算法推导过程中的数学基础知识

写在前面

本篇文章长时间更新,主要是为了整理自己在学习各种算法中遇到的数学知识。方便查阅。

一.向量、矩阵求导

1.  ∂ A x → ∂ x → = A T \frac{\partial{A\overrightarrow{x}}} {\partial \overrightarrow{x}} = A^T x Ax =AT

2.  ∂ A x → ∂ x → T = A \frac{\partial{A\overrightarrow{x}}} {\partial \overrightarrow{x}^T} = A x TAx =A

3.  ∂ x → T A ∂ x → = A \frac{\partial{\overrightarrow{x}^TA}} {\partial \overrightarrow{x}} = A x x TA=A

4.  ∂ ( X T A X ) ∂ X = 2 A X ( A T = A ) \frac{\partial {(X^T A X )} }{\partial {X}}= 2AX(A^T=A) X(XTAX)=2AXAT=A

5.  ∂ ( X T A T A X ) ∂ X = 2 A T A X   因 为 ( A T A ) T = A T A   \frac{\partial {(X^T A^TA X )} }{\partial {X}}= 2A^TAX 因为(A^TA)^T=A^TA  X(XTATAX)=2ATAX (ATA)T=ATA 

二.矩阵转置的性质

1.  ( A ± B ) T = A T ± B T (A\pm B)^T = A^T \pm B^T (A±B)T=AT±BT

2.  ( A × B ) T = B T × A T (A\times B)^T = B^T \times A^T (A×B)T=BT×AT

3.  ( A T ) T = A {(A^T)}^T = A (AT)T=A

4.  ( K A ) T = K A T {(KA)}^T = KA^T (KA)T=KAT

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