Re:Mathematics for Machine Learning [pdf]
by random314 on Sunday October 20, 2019 @09:55PM
(#21308777)
I learnt eigen decomposition, Hessians, PCA, backpropagation, CNN, dropout, maxpooling etc.
The page you linked above is the derivation of PCA using linear algebra.
First part derives the encoding matrix from the decoding matrix. 2nd part derives the encoding matrix by minimizing the L2 norm.
If you find the math too heavy, you should take Andrew ngs course at Coursera (not his Stanford lectures, which follow a pattern similar to this book). Or pick up any book targeting programmers, machine learning for hackers etc.
The page you linked above is the derivation of PCA using linear algebra.
First part derives the encoding matrix from the decoding matrix. 2nd part derives the encoding matrix by minimizing the L2 norm.
If you find the math too heavy, you should take Andrew ngs course at Coursera (not his Stanford lectures, which follow a pattern similar to this book). Or pick up any book targeting programmers, machine learning for hackers etc.