Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Update week3quiz1.md #9

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Update week3quiz1.md #9

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion Stanford_Machine_Learning/Week3/week3quiz1.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ Question 2

Answer | Explanation
--- | ---
J(θ) will be a convex function, so gradient descent should converge to the global minimum. | none
J(θ) will be a convex function, so gradient descent should converge to the global minimum. | If J(θ) is a non-convex function, gradient descent will fail to converge at a global minimum because it will converge at a local optimum and get stuck there
Adding polynomial features (e.g., instead using h<sub>θ</sub>(x) = g(θ<sub>0</sub> + θ<sub>1</sub>x<sub>1</sub> + θ<sub>2</sub>x</sub>2 + θ<sub>3</sub>x<sup>2</sup> + θ<sub>4</sub>x<sup>1</sup>x<sup>2</sup> + θ<sub>5</sub>x<sup>2</sup> )) could increase how well we can fit the training data | Adding new features can only improve the fit on the training set: since setting θ<sub>3</sub> = θ<sub>4</sub> = θ<sub>5</sub> = 0 makes the hypothesis the same as the original one, gradient descent will use those features (by making the corresponding non-zero) only if doing so improves the training set fit

Question 3
Expand Down