TensorFlow
모두를 위한 엔드 투 엔드 오픈소스 머신러닝 플랫폼입니다. 도구, 라이브러리, 커뮤니티 리소스로 구성된 TensorFlow의 유연한 생태계를 만나 보세요.
www.tensorflow.org
1. Tensorflow?
TensorFlow makes it easy for begineers and experts to create machine learning models for desktop, mobile, web, and cloud. See the sections below to get started
2. Where to apply
1) TensorFlow - Learn the foundations of TensorFlow with tutorials for begineers and experts to help you create your next machine learning proejct.
2) For Web - Use TensorFlow.js to create new machine learning models and deploy existing models with JavaScript
3) For Mobile & Edge - Run inference with TensorFlow Lite on mobile and embedded devices like Android, iOS, Edge TPU, and Raspberry Pi.
4) For Production - Deploy a production-ready ML pipeline for training and inference using TFX
3. An end-to-end platform for machine learning
Prepare and load data for successful ML outcomes.
- Data can be the most important factor in the success of your ML endeavors. TensorFlow offers multiple data tools to help you consolidate, clean and preprocess data at scale:
- Standard datasets for initial training and validation
- Highly scalable data pipelines for loading data
- Preprocessing layers for common input transformations
- Tools to validate and transform large datasets
Additionally, responsible AI tools help you uncover and eliminate bias in your data to produce fair, ethical outcomes from your models.
4. Why TensorFlow - An entire ecosystem to help you solve challenging, real-world problems with machine learning
1) Easy model building - TensorFlow 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 Tenforslow and machine learning easy. If 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
2) Robust ML production anywhere - TensorFlow 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. Use 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.
3) Powerful experimentation for research - Build 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. TensorFlow also supports an ecosystem of powerful add-on libraries and models to experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor and BERT.