Please complete the compute$\_$gradient function to:

Iterate over the training examples, and for each example, compute:

The prediction of the model for that example

$f_{wb}(x^{(i)})=w{x}^{(i)}+b$

The gradient for the parameters $w,b$ from that example

$\frac{\text{d}\text{e}\text{l}\text{J}(w,b)}{(delb{)}^{(i)}}=(f_{w,b}(x^{(i)})-y^{(i)})$

$\frac{\text{d}\text{e}\text{l}\text{J}(w,b)}{(delw{)}^{(i)}}=(f_{w,b}(x^{(i)})-y^{(i)})x^{(i)}$

Return the total gradient update from all the examples

$\frac{\text{d}\text{e}\text{l}\text{J}(w,b)}{\text{d}\text{e}\text{l}\text{b}}=\frac{1}{m}{\displaystyle \underset{i=0}{\overset{m-1}{}}}\frac{\text{d}\text{e}\text{l}\text{J}(w,b{)}^{(i)}}{\text{d}\text{e}\text{l}\text{b}}$

$\frac{\text{d}\text{e}\text{l}\text{J}(w,b)}{\text{d}\text{e}\text{l}\text{w}}=\frac{1}{m}{\displaystyle \underset{i=0}{\overset{m-1}{}}}\frac{\text{d}\text{e}\text{l}\text{J}(w,b{)}^{(i)}}{\text{d}\text{e}\text{l}\text{w}}$

Here, $m$ is the number of training examples and ${\displaystyle \underset{?}{\overset{?}{}}}$ is the summation operator

If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.