Question: For our Gradient Descent algorithm, the cost function = Sigma ( Y ( mX + 1 ) ) 2 and our learning rate =

For our Gradient Descent algorithm, the cost function =\Sigma (Y(mX+1))2
and our learning rate =0.01.
We are interested in approximating a value for the parameter m using three points. Y is the true y-coordinate of each point and X is the true x-coordinate.
We initialize m with 0 and the new m is calculated as the old m -(0.083m -124)*0.01.
(a) What is the first step size?
(b) what does '0.083m-124' in the formula to compute the new m represent?
(c) Write a conditional expression based on the information provided above that will stop the looping of the algorithm, resulting in a determined value of the parameter.

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