online note for data analysis study
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ml_crash_course_week4
Multi-Class Neural Networks
One vs. All : 다중 클래스 신경망: 사과, 곰, 사탕, 개 중 무엇일까?
각각의 가능한 클래스마다 하나의 로지스틱 회귀 출력 노드를 사용
이진 분류 활용 방법
소프트맥스
로지스틱 회귀(0~1사이 확률)를 다중 클래스 문제에 적용
다중 클래스 문제의 각 클래스에 소수 확률을 할당
확률의 합은 1.0
변형된 소프트맥스
- 전체 소프트맥스(Full Softmax)
- 후보 샘플링(Candidate sampling) - 최적화
양성 레이블 => 전부 확률계산
음성 레이블 => 랜덤 표본만 확률계산
비글인지 하운드인지 찾을 때 '개' 레이블이 아닌 것을 다 계산할 필요는 없다! (계산량 줄이기)
One Label vs. Many Labels : 사과랑 오렌지랑 같이 있다면?
로지스틱 회귀를 여러 개 사용 => 각 출력값이 개별로 계산
실습 MNIST Digit Classification programming exercise
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