Why multiply by 2 when calculating partial derivatives during backpropagation? I'm wondering why we multiple by 2 when calculating partial derivatives.

I'm referencing the 2's that I've circled below, from here.



We also see this in the python implementation,
m_deriv += -2*X[i] * (Y[i] - (m*X[i] + b)), there is a 2. 

How can I understand the intuition here? 

My math is not strong so apologies in advance if this is a silly question.
You're using the Mean Square Error $\Sigma\frac{1}{N}(y-(Wx+b))^2$as the loss function, if you take the derivative, you will have the $2$. In some materials, we will use $\frac{1}{2}\Sigma\frac{1}{N}(y-(Wx+b))^2$ as the loss function to cancel out the $2$. In fact, this doesn't matter at all and it has no impacts on params optimization.