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

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.