딥 러닝 전문화 두 번째 과정에서는 딥 러닝 블랙박스를 열어 성과를 이끌어내고 체계적으로 좋은 결과를 만들어내는 과정을 이해하게 됩니다.
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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
Instructor
Offered by
Recommended if you're interested in Machine Learning
Coursera Project Network
University of Minnesota
DeepLearning.AI
The University of Melbourne
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