Week 1 Quiz

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Question 1

Gradient descent is an algorithm for finding values of parameters w and b that minimize the cost function J:

When $\frac{\partial J(w,b)}{\partial w}$ is a negative number (less than zero), what happens to w after one update step?

• w increases.
• w decreases
• It is not possible to tell if w will increase or decrease.
• w stays the same

Answer

w increases

The learning rate is always a positive number, so if you take W minus a negative number, you end up with a new value for W that is larger (more positive).

Question 2

For linear regression, what is the update step for parameter b?

$b=b-\alpha \frac{1}{m}{\sum }_{i=1}^m\left(f_{w,b}(x^{(i)})-y^{(i)}\right)x^{(i)}$

$b=b-\alpha \frac{1}{m}{\sum }_{i=1}^m\left(f_{w,b}(x^{(i)})-y^{(i)}\right)$

Answer

$b=b-\alpha \frac{1}{m}{\sum }_{i=1}^m\left(f_{w,b}(x^{(i)})-y^{(i)}\right)$

the first option is the update step for parameter w, and should be `w = w - alpha…’

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