a cost function \(J\) tells us how good our training is.
additional information
least-squares error
\begin{equation} J\qty(\theta) = \sum_{i=1}^{n}\qty(h_{\theta }\qty(x^{(i)}) - y^{(i)})^{2} \end{equation}
a cost function \(J\) tells us how good our training is.
\begin{equation} J\qty(\theta) = \sum_{i=1}^{n}\qty(h_{\theta }\qty(x^{(i)}) - y^{(i)})^{2} \end{equation}