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TensorFlow 简介

借助 TensorFlow，初学者和专家可以在桌面、移动、网络和云端环境下创建机器学习模型。请参阅以下几部分，了解如何开始使用。

TensorFlow

通过面向初学者和专家的教程学习 TensorFlow 的基础知识，运用这些知识创建下一个机器学习项目。

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针对 JavaScript

通过 TensorFlow.js 使用 JavaScript 创建新的机器学习模型和部署现有模型。

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针对移动设备和 IoT 设备

使用 TensorFlow Lite 在 Android、iOS、Edge TPU 和 Raspberry Pi 等移动设备和嵌入式设备上进行推断。

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针对生产

使用 TensorFlow Extended (TFX) 部署可正式投入使用的机器学习流水线，以进行训练和推断。

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Swift for TensorFlow

Integrate directly with Swift for TensorFlow, the next generation platform for deep learning and differentiable programming.

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TensorFlow 生态系统

TensorFlow 提供了一系列工作流程，以供您使用 Python、JavaScript 或 Swift 开发和训练模型，并在云端、本地、浏览器中或设备上轻松地部署模型，无论您采用什么语言，都能提供支持。

Load & preprocess data
Build, train & reuse models
部署
Python 开发
CPU GPU TPU
TensorFlow
Build TensorFlow Input Pipelines
The tf.data API enables you to build complex input pipelines from simple, reusable pieces.
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TensorFlow
Build and train models using Keras
tf.keras is a high-level API to build and train models. It supports TensorFlow-specific functionality, such as eager execution, tf.data pipelines, and estimators.
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TensorFlow
Deploy using Python
Deploy on a mobile or edge device, in browser, or at scale using TensorFlow Serving.
JavaScript 开发
CPU GPU TPU
TensorFlow.js
Import a Python model, or write one in JavaScript
Learn to convert pretrained models from Python to TensorFlow.js, as well as how to build and train models directly in JavaScript.
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TensorFlow.js
Deploy in browser or Node.js
Learn how to deploy TensorFlow.js models in the browser, on node.js, or on the Google Cloud platform.
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Swift 开发
CPU GPU TPU
Swift for TensorFlow (in Beta)
Develop models natively in Swift (beta)
Using Swift differentiable programming allows for first-class support in a general-purpose programming language. Take derivatives of functions, and make custom data structures differentiable in an instant. Learn how Swift APIs give you transparent access to all low-level TensorFlow operators.
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边缘设备
CPU GPU RPi
TensorFlow Lite
Deploy on mobile or embedded devices, like Android, iOS, and Raspberry Pi
Read the developer guide and pick a new model or retrain an existing one, convert it to a compressed file, load it on an edge device, and then optimize it.
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端到端生产
CPU GPU TPU
TFX
Validate input data with TF Data Validation
See how to use TFX components to analyze and transform your data before you even train a model.
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TFX
Feature engineering with TF Transform
Learn how to define a preprocessing function that transforms raw data into the data used to train a machine learning model, and see how the Apache Beam implementation is used to transform data by converting the preprocessing function into a Beam pipeline.
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TFX
Modeling and training
Learn how to train your models in a TFX pipeline as a managed process.
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TFX
Understanding model performance with TF model analysis
See how TensorFlow Model Analysis allows you to perform model evaluations in the TFX pipeline and visualize the results in a Jupyter notebook.
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TFX
Serve models with a REST API with TF Serving
Learn how TensorFlow Serving allows you to deploy new algorithms and experiments while keeping the same server architecture and APIs.
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工具
TensorBoard
TensorBoard is a tool to visualize training and results
With TensorBoard you can track experiment metrics like loss and accuracy, visualize the model graph, project embeddings to a lower dimensional space, and more.
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TensorFlow Hub
TensorFlow Hub is an extensive library of existing models
TensorFlow Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models called modules.
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产品路线图和 RFC

探索即将发布的 TensorFlow 版本中的优先目标、侧重点和预期功能。查看即将发布的评论征集 (RFC) 通知，以进行技术方面的深入探讨并参与设计决策。其中许多方面都是由社区用例推动的，我们欢迎各方人员进一步做出贡献。

阅读路线图   参与 RFC  

Looking to expand your ML knowledge?

TensorFlow is easier to use with a basic understanding of machine learning principles and core concepts. Learn and apply fundamental machine learning practices to develop your skills.

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开始使用 TensorFlow

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