10 Questions
What is the purpose of the Gradient Descent algorithm in linear regression?
To minimize the cost function
In the context of linear regression, what does J(θ₀,θ₁) represent?
The cost function
How does the learning rate affect the Gradient Descent algorithm?
Higher learning rate leads to faster convergence
What is the relationship between the parameters θ₀ and θ₁ in linear regression?
θ₀ represents the intercept and θ₁ represents the slope
Which function does the Gradient Descent algorithm aim to optimize in linear regression?
Cost function
What is the significance of iterations in the Gradient Descent algorithm for linear regression?
Iterations are needed to update the parameters towards convergence
Which concept relates to finding the 'elbow' in linear regression?
'Bowel-shaped' function
How does adjusting the model parameters impact linear regression?
It improves the cost function
What is a key characteristic of a good learning rate in Gradient Descent for linear regression?
High learning rate for stable convergence
In linear regression, what is primarily updated during each iteration of the Gradient Descent algorithm?
The model parameters like slope and intercept
Test your knowledge on linear regression with one variable, covering topics like simple regression, cost function, and gradient descent. Explore model representation and training set sizes in feet squared to predict prices in 1000's of dollars.
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