Exercise $1$

Complete the compute$\_$cost below to:

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

The prediction of the model for that example

The cost for that example

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

$cost{t}^{(i)}=(f_{wb}-y^{(i)}{)}^2$

Return the total cost over all examples

$J(w,b)=\frac{1}{2m}{\displaystyle \underset{i=0}{\overset{m-1}{}}}cos{t}^{(i)}$

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.