gs gs126 Multivariate Linear Regression - Gradient Descent for Multiple Variables - Quiz No.1
gs gs126 Neural Networks Quiz
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Question 1: The cost function is minimized by __________
Linear regression
Polynomial regression
PAC learning
Gradient descent
Question 3: What happens when the learning rate is low?
It always reaches the minima quickly
It reaches the minima very slowly
It overshoots the minima
Nothing happens
Question 4: When was gradient descent invented?
1847
1947
1857
1957
Question 5: Gradient descent tries to _____________
maximize the cost function
minimize the cost function
minimize the learning rate
maximize the learning rate.
Question 7: x1’s range is 0 to 300. x2’s range is 0 to 1000. What are the suitable ranges of x1 and x2 after mean normalization?
x1 = (x1 – 150)/300, x2 = (x2-500)/1000
x1 = x2 – 700
x1 = x1 – 300, x2 = x2 – 1000
x1 = x1/300, x2 = x2/1000
Question 8: x1’s range is 0 to 300. x2’s range is 0 to 1000. What are the suitable ranges of x1 and x2 after feature scaling?
x1 = x1 – 300, x2 = x2 – 1000
x1 = x2 – 700
x1 = x1/1000, x2 = x2/ 300
x1 = x1/300, x2 = x2/1000
Question 9: On which factor is the updating of each parameter dependent on?
The number of training examples
Target variable
The learning rate and the target variable
The learning rate
Question 10: What is updated by gradient descent after each iteration?
The learning rate
Independent variables
Target variable
The number of training examples
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