TensorFlow 简介

借助 TensorFlow，初学者和专家可以轻松创建适用于桌面、移动、Web 和云环境的机器学习模型。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 基础知识，并运用这些知识创建下一个机器学习项目。

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针对 Web

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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针对移动设备和边缘设备

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 TFX 部署可用于生产环境的机器学习流水线，以用于训练和推断。

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准备和加载数据以获得成功的机器学习结果

数据可能是决定机器学习工作能否成功的最重要因素。 TensorFlow 提供了多种数据工具，可以帮助您大规模整合、清理和预处理数据：

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.

在 Colab 中试用

加载并预处理图像数据集调查和可视化数据集

利用 TensorFlow 生态系统构建和微调模型

Explore an entire ecosystem built on the Core framework that streamlines model construction, training, and export. TensorFlow supports distributed training, immediate model iteration and easy debugging with Keras, and much more. Tools like Model Analysis and TensorBoard help you track development and improvement through your model’s lifecycle.

To help you get started, find collections of pre-trained models at TensorFlow Hub from Google and the community, or implementations of state-of-the art research models in the Model Garden. These libraries of high level components allow you to take powerful models, and fine-tune them on new data or customize them to perform new tasks.

在 Colab 中试用

训练神经网络对图像进行分类借助迁移学习技术重新训练图像分类器

在设备上、浏览器中、本地或云端部署模型

TensorFlow provides robust capabilities to deploy your models on any environment - servers, edge devices, browsers, mobile, microcontrollers, CPUs, GPUs, FPGAs. TensorFlow Serving can run ML models at production scale on the most advanced processors in the world, including Google's custom Tensor Processing Units (TPUs).

If you need to analyze data close to its source to reduce latency and improve data privacy, the TensorFlow Lite framework lets you run models on mobile devices, edge computing devices, and even microcontrollers, and the TensorFlow.js framework lets you run machine learning with just a web browser.

在 Colab 中试用

使用 TensorFlow Serving 将模型付诸应用

实现适用于生产型机器学习的 MLOps

The TensorFlow platform helps you implement best practices for data automation, model tracking, performance monitoring, and model retraining.

Using production-level tools to automate and track model training over the lifetime of a product, service, or business process is critical to success. TFX provides software frameworks and tooling for full MLOps deployments, detecting issues as your data and models evolve over time.