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

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

In a deep learning model, the learning rate is set to 0.001. If the gradient of the loss function is 0.2, what is the update value for the weights?

Assume that your hypothesis function for linear regression is of the form f(x) = w0 + w1x and that the current values of w0 and w1 are 1 and 2 respectively. Further assume that you are using a learning rate (alpha) of 0.001What is the new w0 value associated with the point (1, 12), after one gradient update?

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?