Liner Regression(좀더 공부하고 수정)

Superviesd learning : training set으로 학습

ex) 사진을 통해 개인지 고양이인지 판단

Most commom problem type in ML

- Image labeling: learning from tagged images

- Email spam filter : learning from labeled (spam or ham) email

- predicting exam score : learning from previous exam score and time spent

 

Types of supervised learning

Predicting final exam score based on time spent

- regression

Pass/non-pass based on time spent

- binary classification

Letter grade (A, B, C, E and F) based on time spent

- multi-label classification

 

Unsupervised learning: 데이터를 보고 스스로 학습

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Gradient descent(경사 하강 알고리즘) : 최소한의 코스트를 찾는 법

HOW IT WORKS?

- start with initial guesses

-> Start at 0,0 (or any other value)

-> Keeping changing W and b a little bit to try and reduce cost(W, b)

- Each time you change the parameters, you select the gradient which reduces cost(W, b) the most possible

- Repeat

- Do so until you converge to a local minimum

- Has an interesting property

-> Where you start can determine which minimum you end up

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