[강의]시즌1 딥러닝의기본 - Linear Regression의Hypothesis와 cost

Predicting exam score : regression

x(hours)    y(score)

training data set. 을 통해서 learning.

labeled data. y is range. 

After learning , put x . Then get y.

(Linear) Hypothesis 는 어떤 1차원의 선이 존재할 것이다. 라고 가정하는것.

H(x) = Wx + b

어떤 선이 우리가 찾던 선인가.?

Which hypothesis is better ? 

hypothesis 와 data point 와 비교한다 ( 거리를 측정 =cost function,loss function)

Cost function

 * How fit the line to our (training) data

H(x) - y    ->not good.

(H(x) - y)^2 is formal.

m is number of data.

Learning of linear regression is minimizing cost.

Goal : Minimize cost

minimize cost(W,b)

get the W, b.