딥 러닝 전문화 두 번째 과정에서는 딥 러닝 블랙박스를 열어 성과를 이끌어내고 체계적으로 좋은 결과를 만들어내는 과정을 이해하게 됩니다.
심층 신경망 개선: 하이퍼파라미터 튜닝, 정규화 및 최적화
심층 신경망 개선: 하이퍼파라미터 튜닝, 정규화 및 최적화
Instructor: Andrew Ng
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Sponsored by Coursera Learning Team
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
25 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace
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Assessments
3 assignments
Taught in Korean
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There are 3 modules in this course
다양한 초기화 방법을 사용하여 살펴보고 실험하고 L2 정규화 및 드롭아웃을 적용하여 모델 과적합을 방지한 다음 기울기 검사를 적용하여 사기 탐지 모델의 오류를 식별합니다.
What's included
15 videos4 readings1 assignment3 programming assignments2 app items
고급 최적화, 랜덤 미니배칭 및 학습률 감소 스케줄링을 추가하여 모델 속도를 높여 딥 러닝 도구 상자를 개발하십시오.
What's included
11 videos2 readings1 assignment1 programming assignment1 app item
신경망을 빠르고 쉽게 구축한 다음 TensorFlow 데이터세트에서 신경망을 훈련할 수 있는 딥 러닝 프레임워크인 TensorFlow를 살펴보십시오.
What's included
11 videos5 readings1 assignment1 programming assignment1 app item
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