[ 机器学习 - 吴恩达 ] Linear regression with one variable | 2-3 Cost function intuition ||

Hypothesis: \(h_\theta(x) = \theta_0 + \theta_1x\)
Parameters: \(\theta_0, \theta_1\)
Cost Function: Contour (等高线) plots \(J(\theta_0,\theta_1) = 1/2m\sum_{i=1}^m(h_\theta(x^{(i)}) - y^{(i)})^2\)

Goal:\(\begin{matrix} minimize J(\theta_0,\theta_1)\\ \theta_0,\theta_1 \end{matrix}\)