内容来自于机器学习课程
Model Representation
To establish notation for future use, we’ll use x^(i) to denote the input variables, also called input features. and y^(i) to denote the output or target variable that we are trying to predict.
(i) is simply an index into the training set, and has nothing to do with exponentiation.
Cost Function
We can measure the accuracy of our hypothesis function by using a cost function. This takes an average difference (actually a fancier version of an average ) of all the results of the hypothesis with inputs from x’s and the actual output y’s.
The function is otherwise called the “Squared error function”, or “Mean squared error”.
Cost Function - Intuition I
形成一个假设，可以尽可能的经过更多的数据点。希望可以获得最好的这条线，使得cost function的值最小化。
Cost Function - Intuition II
contour plots: 等高线图
当有两个值时，cost function的图是3D的，