Coursera - Machine Learning_Andrew Ng - Week 2

2022.01.25

Coursera - Machine Learning_Andrew Ng - Week 2

 

Multiple features(variables)

 

Gradient descent for multiple variables

 

Gradient descent in practice 1: Feature Scaling

- feature scaling

: simple trick to apply → make gradients run much faster and converge in a lot fewer other iterations.

: make sure features are on a similar scale ⇒ get every features into approximately a -1<=xi<=1 range

- mean normalization

Gradient Descent in practice 2: Learning rate

- Debugging: make sure gradient descent is working correctly

        (use visualization using plot vs automatic convergence test)

If α is too small ⇒ slow convergence

If α is too big ⇒ J(θ) may not decrease on every iteration; may not converge

 

Features and Polynomial Regression

example of polynomial regression formula

Normal Equation formula

⇒ Compare with Gradient Descent

 

Gradient Descent	Normal Equation
needs to choose α	no need to choose α
needs many iterations	don't need iteratation
works well even when n is large	slow if n is very large + need to compute