선형회귀 (linear regression) - 피노텍 런치 스터디
TRANSCRIPT
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Machine Learning선형회귀 (Linear Regression)
이도현Finotek Inc. 화요 런치 스터디
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1. Machine Learning 의 접근방법
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1. Machine Learning 의 접근방법
10점
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1. Machine Learning 의 접근방법
10점
2 점
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1. Machine Learning 의 접근방법
10점
6 점
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1. Machine Learning 의 접근방법
10점
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2. 비용 (Cost) = Error
예측값 - 실제값
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2. 비용 (Cost) = Error
target - output
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3. 지도학습 (Supervised Learning) 의 모델Training Set
LearningAlgorithm
EngineInput Output
1/2
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3. 지도학습 (Supervised Learning) 의 모델Training Set
( 필수 )
LearningAlgorithm
EngineInput Output
2/2
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4. 선형 회귀 (Linear Regression)
2/2
공부한 시간 ( 단위 : 시간 ) 점수
1 20
2 23
8 50
4 28
12 78
19 90
22 95
공부한 시간 대비 점수표
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4. 선형 회귀 (Linear Regression)
2/2
공부한 시간 ( 단위 :시간 )
점수
1 20
2 23
8 50
4 28
12 78
19 90
22 95
공부한 시간 대비 점수표
어느 정도 공부하면
얼마 만큼의 점수가
나오는지 ?
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4. 선형 회귀 (Linear Regression)
2/2
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4. 선형 회귀 (Linear Regression)
2/2
h(x) = ax + b
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4. 선형 회귀 (Linear Regression)
2/2
f(x)
h(x) = ax + b
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4. 선형 회귀 (Linear Regression)
Training Set
가설함수 (Hypothesis Function) h(x)
x y
1 20
2 23
8 50
4 28
12 78
19 90
22 95
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5. 비용 (Cost) same as Error
Training Set가설함수 (Hypothesis Function)
h(x) = wx + bx y
1 20
2 23
8 50
4 28
12 78
19 90
22 95
h(x) – y예측값 – 실제값
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5. 비용 (Cost) same as Error
h(x) – y예측값 – 실제값
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5. 비용 (Cost) same as Error
(h(x) – y)^2예측값 – 실제값
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5. 비용 (Cost) same as Error
(h(x) – y)^2예측값 – 실제값
1. 음수를 제거
2. 비용에 대한 가중치
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6. 비용함수 ( Cost Function )
(h(x) – y)^2예측값 – 실제값
모든 x 에 대하여(= 모든 트레이닝 셋 데이터에 대한 )
의 평균
= Cost
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6. 비용함수 ( Cost Function )
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6. 비용함수 ( Cost Function )
= Cost
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6. 비용함수 ( Cost Function )
= The number of training data
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6. 비용함수 ( Cost Function )
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6. 비용함수 ( Cost Function )
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7. Linear Regression 의 목표
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7. Linear Regression 의 목표
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8. Gradient Decent Algorithm ( 점진하강법 )
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8. Gradient Decent Algorithm ( 점진하강법 )
점진하강법이란 ?어느 한 출발지점에 대해 극솟값을 찾는 알고리즘 (wikipedia)
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8. Gradient Decent Algorithm ( 점진하강법 )
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8. Gradient Decent Algorithm ( 점진하강법 )
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8. Gradient Decent Algorithm ( 점진하강법 )
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8. Gradient Decent Algorithm ( 점진하강법 )
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8. Gradient Decent Algorithm ( 점진하강법 )연습문제 1.
위 함수의 최소값을 GD 알고리즘을 이용하여 찾기
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8-2. Cost Function 에 적용하기
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8-2. Cost Function 에 적용하기
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8-2. Cost Function 에 적용하기
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8-2. Cost Function 에 적용하기
평방 피트에 따른 주택가격 데이터 셋
Squared Feet (x) Price of houses (y)1400 2451600 3121700 2791875 3081100 1991550 2192350 4052450 3241425 3191700 255
연습문제 2.
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9. 다중 변수 선형 회귀
기말고사 성적 데이터 셋(y 에 대하여 종속적인 x 변수들 )
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9-1. 다중 변수의 가설 함수
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9-2. 행렬 (Materix)
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9-2. 행렬 (Materix)
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9-2. 행렬 (Materix)
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9-2. 행렬 (Materix)
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9-3. 연습문제
기말고사 성적 데이터 셋