ML From Scratch, Part 1: Linear Regression
Understood everything up until this:<p><pre><code>        ∇ΘJ=∇Θ(y−XΘ)T(y−XΘ)
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expanded into<p><pre><code>        ∇ΘJ=∇ΘyTy−(XΘ)Ty−yTXΘ+ΘT(XTX)Θ

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Can someone explain why ∇Θ is only applied to the first term?
Also i have never seen gradient notation used like that before.