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I started learning machine learning and have some troubles in understanding derive rules for the gradient of cost function in particular I can't understand how sum(wjxj) transformed to -xj. I tried to google a detailed breakdown explanation but everywhere the same. I know that it pretty simple for mathematicians for those who hasn't met this for quite long time it's a problem. enter image description here

enter image description here

Could someone explain or/and give links to some resources regarding that.

Thanks in advance!

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I just wrote the first few terms on a sheet of paper and understood it. All other terms exept

w(i)j * x(i)j

are constant and thus derive to 0.

The

w(i)j * x(i)j

derives to

x(i)j

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