17 Questions
What is the purpose of the gradient descent algorithm in the context of linear regression?
To optimize the parameters of the linear regression model
What does the cost function $J(\theta_0, \theta_1)$ represent in the context of linear regression?
The sum of the squared differences between the predicted and actual outputs
What is the purpose of the update step in the gradient descent algorithm for linear regression?
To update the parameters of the linear regression model
What is the relationship between the cost function $J(\theta_0, \theta_1)$ and the parameters $\theta_0$ and $\theta_1$ in the context of linear regression?
The cost function is a quadratic function of the parameters
What is the role of the learning rate in the gradient descent algorithm for linear regression?
To determine the step size in the update step of the algorithm
What is the main difference between the cost function $J(\theta_0, \theta_1)$ and the parameters $\theta_0$ and $\theta_1$ in the context of linear regression?
The cost function is a function of the parameters, while the parameters are constants
What happens if the learning rate α is too large in gradient descent?
Gradient descent may overshoot the minimum and fail to converge
What is the effect of a small learning rate α on the convergence of gradient descent?
Gradient descent will converge slowly
How can you choose a good value for the learning rate α in gradient descent?
Try values like 0.001, 0.01, 0.1, 1
What happens to the step size of gradient descent as it approaches a local minimum?
The step size decreases
What is the main advantage of not decreasing the learning rate α over time in gradient descent?
It avoids overshooting the minimum
What is the purpose of the Cost Function in linear regression?
To minimize the difference between predicted and actual values
What does the parameter θ1 represent in linear regression?
Slope of the regression line
What is the main goal of Gradient Descent in machine learning?
To minimize a function by iteratively moving towards its minimum
What does 'α' represent in the Gradient Descent algorithm?
Learning rate or step size
How are parameters updated in Gradient Descent?
Updating all parameters simultaneously at each iteration
What happens if the learning rate 'α' in Gradient Descent is too small?
Gradient Descent may converge slowly
This quiz covers topics from the second lecture on regression, including simple regression, cost function, and gradient descent. It includes model representation, training set size, and linear regression with one variable.
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