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Programming Machine Learning- gradient descent calculating the derivative (page 35)

ggerico

ggerico

Programming Machine Learning- gradient descent calculating the derivative (page 35)

if someone can help me to understand how to calculate the derivative:
2/m xi ((wxi + b) - yi)

I thought it was: 2/m ((wxi + b) - yi)

tks ~

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nusco

nusco

Author of Metaprogramming Ruby & Programming Machine Learning

Aaah, OK! The xi comes out of the “chain rule” of derivation. Here is a video that explains the rule.

Because of the chain rule, the derivative of ((wxi + b) - y)2 is equal to 2((wxi + b) - y) (as you mentioned), multiplied by the derivative of ((wxi + b) - y). The only term inside this expression that depends on w is wxi, and its derivative is xi. That is where that xi is coming from.

Did that clarify it, or should I go into more detail?

Post #4

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