Machine Learning - Stanford - Coursera

main

main

Acknowledgements
Introduction
Linear Algebra Review
Types of Machine Learning
Supervised Learning
- Linear Regression
  - Linear Regression in One Variable
    Cost Function
    Gradient Descent
  - Multivariate Linear Regression
    Cost Function
    Gradient Descent
    Feature Scaling
    Mean Normalization
    Choosing the Learning Rate α
- Polynomial Regression
  - Normal Equation
  - Gradient Descent vs. Normal Equation

Powered by GitBook

On this page

Was this helpful?

Supervised Learning

Linear Regression

Multivariate Linear Regression

Gradient Descent

PreviousCost Function NextFeature Scaling

Last updated 3 years ago

Was this helpful?

The Gradient Descent can be given by:

repeat:{

$θ_j:= θ_j−α ∂/∂θ_j J(θ)$

} (simultaneously update for every j=0,1,2….)

On computing the partial differential in the above equation, we get:

repeat:{

$θ_j:=θ_j−α (1/m) ∑_{i=1}^{m}((h_θ(x^{(i)})−y^{(i)})x_{j}^{(i)})$

} (simultaneously update every j=0,1,2…,n)