[Machine Learning]: #5 Computing Parameters Analytically

[Machine Learning]: Computing Parameters Analytically

[Write Infront]:

We have already know the Gradient Descent Algo, Today we are going to talk another way to min the Cost Function, we call this Normal Equation!

[Normal Equation]:

The Normal Equation Formula is given below:

$\theta = {({X^T}X)^{ - 1}}{X^T}Y$

here is an example of how to use it:

If we use this method, there is no need to do Feature Scaling

[Comparison]:

Gradient Descent	Normal Equation
Need to choose $\alpha$	No need to choose $\alpha$
Needs many iterations	No need to iterate
$O(k{n^2})$	$O({n^3})$
Works well when n is large	Slow if n is very large

Comments

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