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TensorFlow를 사용해야 하는 이유

전문가이든 초보자이든, TensorFlow는 ML 모델을 쉽게 빌드 및 배포할 수 있게 해주는 엔드 투 엔드 플랫폼입니다.

동영상 보기 우수사례

손쉬운 모델 빌드

TensorFlow는 다양한 수준의 추상화를 제공하므로 사용자는 자신의 요구에 맞는 수준을 선택할 수 있습니다. 상위 수준의 Keras API를 사용하여 모델을 빌드하고 학습시키세요. 그러면 TensorFlow 및 머신러닝을 쉽게 시작할 수 있습니다.

유연성이 더 필요한 경우 즉시 실행 기능을 사용하면 즉각적인 반복 및 직관적인 디버깅이 가능합니다. 대규모 ML 학습 작업이 필요한 경우, 모델 정의를 변경하지 않고 서로 다른 하드웨어 구성에서 분산 학습을 진행하려면 Distribution Strategy API를 사용하세요.

리소스 보기

어디서든 강력한 ML 제작

TensorFlow는 항상 프로덕션에 바로 배포할 방법을 제공해왔습니다. 서버, 에지 기기 또는 웹 등 어디서나 TensorFlow를 사용하면 언어나 플랫폼에 관계없이 모델을 쉽게 학습시키고 배포할 수 있습니다.

전체 프로덕션 ML 파이프라인이 필요한 경우 TFX를 사용하세요. 모바일 및 에지 기기에서 추론을 실행하려면 TensorFlow Lite를 사용하세요. TensorFlow.js를 사용하면 자바스크립트 환경에서 모델을 학습시키고 배포할 수 있습니다.

리소스 보기

연구를 위한 강력한 실험

속도나 성능 저하 없이 최첨단 모델을 빌드하고 학습시키세요. TensorFlow에서는 Keras Functional API 및 Model Subclassing API와 같은 기능을 사용하여 유연하고 긴밀하게 복잡한 토폴로지 생성을 제어할 수 있습니다. 손쉬운 프로토타입 제작과 빠른 디버깅을 구현하려면 즉시 실행 기능을 사용하세요.

TensorFlow는 Ragged Tensors, TensorFlow Probability, Tensor2Tensor, BERT를 비롯한 강력한 부가기능 라이브러리 및 모델을 실험해 볼 수 있는 생태계를 지원합니다.

리소스 보기

Airbnb

Coca Cola

Deepmind

GE Healthcare

Google

Intel

NERSC

트위터

머신러닝 작동 방법 알아보기

신경망의 작동 방식이나 ML 문제 해결 단계가 궁금하셨던 적이 있나요? 지금부터 함께 알아보겠습니다. 다음은 머신러닝의 기초에 관한 간단한 개요입니다. 더 깊이 있는 정보를 원하신다면 교육 페이지에서 초보자용 콘텐츠와 고급 콘텐츠를 찾아보세요.

ML 알아보기

ML 소개 ML 문제를 해결하는 단계 신경망의 해부학적 구조 신경망 학습시키기

ML 소개

머신러닝은 소프트웨어가 명시적인 프로그래밍이나 규칙 없이 작업을 실행하도록 돕는 방식입니다. 기존의 컴퓨터 프로그래밍에서는 프로그래머가 컴퓨터가 사용해야 할 규칙을 지정하지만, ML에서는 다른 사고방식이 필요합니다. 실제 ML은 코딩보다 데이터 분석에 훨씬 더 중점을 둡니다. 프로그래머는 일련의 예제를 제공하고 컴퓨터는 데이터로부터 패턴을 학습합니다. 머신러닝은 '데이터를 이용한 프로그래밍'이라고 생각할 수 있습니다.

ML 문제를 해결하는 단계

ML을 사용하여 데이터에서 해답을 얻는 과정에는 여러 단계가 있습니다. 텍스트 분류의 전체 워크플로를 보여 주고 데이터 세트 수집, TensorFlow를 이용한 모델 학습 및 평가와 같은 중요한 단계를 설명하는 이 가이드에서 단계별 개요를 확인하세요.

신경망의 해부학적 구조

신경망은 패턴을 인식하도록 학습시킬 수 있는 모델 유형으로, 입력 및 출력 레이어, 하나 이상의 히든 레이어 등 다양한 레이어로 구성됩니다. 각 레이어의 뉴런은 추상화 수준이 점점 높아지는 데이터 표현을 학습합니다. 예를 들어, 이 시각적 다이어그램에는 선, 모양, 텍스처를 감지하는 뉴런이 있습니다. 이러한 표현(또는 학습된 특성)을 사용하여 데이터를 분류할 수 있습니다.

신경망 학습시키기

신경망은 경사하강법을 통해 학습합니다. 각 레이어의 가중치는 임의의 값으로 시작하며, 시간이 지남에 따라 반복적으로 개선되어 신경망의 정확도를 높입니다. 손실 함수는 신경망의 부정확성을 정량화하는 데 사용되며, 손실을 줄이기 위해 각 가중치를 늘리거나 줄여야 하는지를 결정하기 위해 역전파라는 절차가 사용됩니다.

TensorFlow를 사용해야 하는 이유

머신러닝을 통해 까다로운 실생활 문제를 해결하도록 지원하는 전체 생태계

손쉬운 모델 빌드

어디서든 강력한 ML 제작

연구를 위한 강력한 실험

기업에서 TensorFlow를 사용하는 방법 보기

머신러닝 작동 방법 알아보기

ML 소개

ML 문제를 해결하는 단계

신경망의 해부학적 구조

신경망 학습시키기

커뮤니티

TensorFlow를 사용하여 손쉽게 빌드, 배포, 실험