For our Gradient Descent algorithm, the cost function = Σ(Y−(mX+1))2Σ(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?

Consider a function f(x)=x3−4x2+7𝑓(𝑥)=𝑥3−4𝑥2+7. What is the updated value of x𝑥 after 2nd iteration of the gradient descent update, if the learning rate is 0.10.1 and the initial value of x𝑥 is 5?

Suppose we have a function f(x1,x2)=x21+3x2+25𝑓(𝑥1,𝑥2)=𝑥12+3𝑥2+25 which we want to minimize the given function using the gradient descent algorithm. We initialize (x1,x2)=(0,0)(𝑥1,𝑥2)=(0,0). What will be the value of x1𝑥1 after ten updates in the gradient descent process?(Let η𝜂 be 1) 0 -3 −4.5 −3

Consider the function y = (x + 4)^2 and assume the learning rate is 0.01. What is local minima of the function when x is initialized to 3? What is x after the first iteration using gradient descent?1 point0, 3.020, 4.08-4, 2.864, 3.8

Given a learning rate of 0.01 and a gradient of 0.05, what is the update step for the weights?

What role does the learning rate play in the Steepest Descent method? a. It represents the error function b. It determines the size of the incremental steps in updating parameters c. It is the output of the input layer d. It represents the difference between true output and observed output