Machine Learning 머신 러닝 - Linear Regression

Machine Learning 머신 러닝 - Linear Regression

관련 포스트

Machine Learning 머신러닝 - ML & Deep Learning

TensorFlow 설치 및 PyCharm 설치

TensorFlow Linear Regression

TensorFlow Minimize Cost, Gradient Discent Algorithm

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Predicting exam score : regression

- Using with Learning Training Set (학습된 데이터를 기반으로)

Regression(data)

Using Training data -> (Linear) Hypothesis  - H(x) = Wx+b

And then “Which hypothesis is better?”

Linear Regression -> Many Modeling

Cost(Loss) function

- How fit the line to our training data

- cost : (H(x)-y)2

- cost function

> cost =  (H(x1)-y1)2+(H(x2)-y2)2+(H(x3)-y3)2

> cost = 1mi=1m(H(x(i))-y(i))2

> H(x) = Wx + b  -> cost(W,b)= 1mi=1m(H(x(i))-y(i))2  ( m = num of data)

Goal : Minimize cost

minimize cost(W,b)

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Reference

Sung Kim https://www.youtube.com/channel/UCML9R2ol-l0Ab9OXoNnr7Lw

Andrew Ng’s ML class

- https://class.coursera.org/ml-003/lecture

- http://www.holehouse.org/mlclass