이 과정에서는 TensorFlow 및 Keras를 사용한 ML 모델 빌드, ML 모델의 정확성 개선, 사용 사례 확장을 위한 ML 모델 작성에 대해 다룹니다.
TensorFlow on Google Cloud - 한국어
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12 hours to complete
3 weeks at 4 hours a week
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What you'll learn
TensorFlow 입력 데이터 파이프라인을 설계하고 빌드하기
tf.data 라이브러리를 사용해 대규모 데이터 세트의 데이터 조작하기
Keras Sequential 및 Functional API를 사용해 단순 모델 및 고급 모델 만들기
Vertex AI를 사용하여 ML 모델을 규모에 맞춰 학습, 배포, 프로덕션화하기
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Assessments
4 assignments
Taught in Korean
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There are 6 modules in this course
이 모듈은 과정 및 과정 목표에 대한 개요를 제공합니다.
What's included
1 video
이 모듈에서는 TensorFlow 프레임워크를 소개하고 기본 구성요소와 전체적인 API 계층 구조를 미리 살펴봅니다.
What's included
4 videos1 reading1 assignment
데이터는 머신러닝 모델에서 중요한 구성요소입니다. 적절한 데이터를 수집하는 것만으로는 충분하지 않습니다. 필요에 따라 데이터를 정리, 분석, 변환하는 적절한 프로세스도 마련해야 모델이 데이터의 신호를 최대한 활용할 수 있습니다. 이 모듈에서는 tf.data를 사용한 대규모 데이터 세트 학습, 인메모리 파일 사용, 학습을 위해 데이터를 준비하는 방법을 다룹니다. 그런 다음 임베딩에 대해 알아보고 tf.keras 사전 처리 레이어로 데이터를 확장하는 방법을 간략히 살펴보며 마무리합니다.
What's included
10 videos1 reading1 assignment2 app items
이 모듈에서는 활성화 함수에 대해 다루며 심층신경망에서 데이터의 비선형성을 캡처하는 데 이 함수가 얼마나 중요한지 알아봅니다. 그런 다음 Keras Sequential 및 Functional API를 사용하는 심층신경망에 대해 간략히 살펴봅니다. 이어서 모델 빌드의 유연성을 높여주는 모델 서브클래스화에 대해 설명합니다. 마지막으로 정규화에 대한 수업으로 모듈을 마무리합니다.
What's included
10 videos1 reading1 assignment2 app items
이 모듈에서는 Vertex AI를 사용해 TensorFlow 모델을 규모에 따라 학습시키는 방법을 설명합니다.
What's included
3 videos1 reading1 assignment1 app item
이 모듈은 Build, Train, and Deploy ML Models with Keras on Google Cloud 과정의 요약입니다.
What's included
4 readings
Instructor
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Google Cloud
Google Cloud
Google Cloud
DeepLearning.AI
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