컬렉션을 사용해 정리하기
내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.
TensorFlow를 사용해야 하는 이유
전문가이든 초보자이든, TensorFlow는 ML 모델을 쉽게 빌드 및 배포할 수 있게 해주는 엔드 투 엔드 플랫폼입니다.
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.
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.
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.
[null,null,[],[],[],null,["# Why TensorFlow\n==============\n\nWhether you're an expert or a beginner, TensorFlow is an end-to-end platform that makes it easy for you to build and deploy ML models. \n\nWatch the video\n\n[Case studies](/about/case-studies) \n*close* \n\nAn entire ecosystem to help you solve challenging, real-world problems with machine learning\n--------------------------------------------------------------------------------------------\n\n### Easy model building\n\nTensorFlow offers multiple levels of abstraction so you can choose the right one for your needs. Build and train models by using the high-level Keras API, which makes getting started with TensorFlow and machine learning easy.\n\nIf you need more flexibility, eager execution allows for immediate iteration and intuitive debugging. For large ML training tasks, use the Distribution Strategy API for distributed training on different hardware configurations without changing the model definition. \n[See resources](/guide/effective_tf2) \n*close* \n\n### Robust ML production anywhere\n\nTensorFlow has always provided a direct path to production. Whether it's on servers, edge devices, or the web, TensorFlow lets you train and deploy your model easily, no matter what language or platform you use.\n\nUse TFX if you need a full production ML pipeline. For running inference on mobile and edge devices, use TensorFlow Lite. Train and deploy models in JavaScript environments using TensorFlow.js. \n[See resources](/learn) \n*close* \n\n### Powerful experimentation for research\n\nBuild and train state-of-the-art models without sacrificing speed or performance. TensorFlow gives you the flexibility and control with features like the Keras Functional API and Model Subclassing API for creation of complex topologies. For easy prototyping and fast debugging, use eager execution.\n\nTensorFlow also supports an ecosystem of powerful add-on libraries and models to experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor and BERT. \n[See resources](/guide/effective_tf2) \n*close* \n\nSee how companies are using TensorFlow\n--------------------------------------\n\nAirbnb \nCoca Cola \nDeepmind \nGE Healthcare \nGoogle \nIntel \nNERSC \nTwitter \n[See case studies](/about/case-studies) \n\nLearn how machine learning works\n--------------------------------\n\nDid you ever want to know how a neural network works? Or what the steps are to solving an ML problem? Don't worry, we've got you covered. Below is a quick overview of the fundamentals of machine learning. Or, if you're looking for a more in-depth information, head to our education page for beginner and advanced content. \n[Learn ML](/resources/learn-ml) \nIntro to ML Steps to solving an ML problem Anatomy of a neural network Training a neural network \n\n### Intro to ML\n\nMachine learning is the practice of helping software perform a task without explicit programming or rules. With traditional computer programming, a programmer specifies rules that the computer should use. ML requires a different mindset, though. Real-world ML focuses far more on data analysis than coding. Programmers provide a set of examples and the computer learns patterns from the data. You can think of machine learning as \"programming with data\". \n\n### Steps to solving an ML problem\n\nThere are multiple steps in the process of getting answers from data using ML. For a step-by-step overview, check out this [guide](https://developers.google.com/machine-learning/guides/text-classification/) that shows the complete workflow for text classification, and describes important steps like collecting a dataset, and training and evaluating a model with TensorFlow. \n\n### Anatomy of a neural network\n\nA neural network is a type of model that can be trained to recognize patterns. It is composed of layers, including input and output layers, and at least one [hidden layer](https://developers.google.com/machine-learning/glossary/#hidden_layer). Neurons in each layer learn increasingly abstract representations of the data. For example, in this visual diagram we see neurons detecting lines, shapes, and textures. These representations (or learned features) make it possible to classify the data. \n\n### Training a neural network\n\nNeural networks are trained by gradient descent. The weights in each layer begin with random values, and these are iteratively improved over time to make the network more accurate. A loss function is used to quantify how inaccurate the network is, and a procedure called backpropagation is used to determine whether each weight should be increased, or decreased, to reduce the loss. \n\nOur community\n-------------\n\nThe TensorFlow community is an active group of developers, researchers, visionaries, tinkerers and problem solvers. The door is always open to contribute, collaborate and share your ideas. \n[Learn more](/community) \n\nBuild, deploy, and experiment easily with TensorFlow\n----------------------------------------------------\n\n[Get started](/tutorials)"]]
