Comments (4)
hypothesis
(linear) hypothesis
H(x) = Wx +b
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cost function
- = loss function, objective function
- minimun value 찾는게 관건
- H(x) - y
cost = 1/m sigma from i=1 to i = m (H(x)-y)^2
결국 , 중요한거는 minimize cost
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gradient descent algorithm
- minimize cost function
- 경사도가 없는 부분 (기울기 =0) 인 곳까지 내려가
- 기울기니까 미분을 활용
- 최소비용에 수렴할 때 까지 반복
- 알파가 너무 크면 오버슈팅
- 알파가 너무 작으면 최소비용에 수렴하는데 시간 너무 오래 걸려
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convex vs non-convex
- 어느방향으로 얼마만큼 이동할 것인가
- linear hypothesis -> convex function ( global minimum 가져)
- non-convex function -> global minimum 보장 못해
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Related Issues (8)
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