使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
选择 TensorFlow 的原因
TensorFlow 是一个端到端平台，无论您是专家还是初学者，它都可以让您轻松地构建和部署机器学习模型。
轻松地构建模型
TensorFlow 提供多个抽象级别，因此您可以根据自己的需求选择合适的级别。您可以使用高阶 Keras API 构建和训练模型，该 API 让您能够轻松地开始使用 TensorFlow 和机器学习。
如果您需要更高的灵活性，则可以借助即刻执行环境进行快速迭代和直观的调试。对于大型机器学习训练任务，您可以使用 Distribution Strategy API 在不同的硬件配置上进行分布式训练，而无需更改模型定义。
随时随地进行可靠的机器学习生产
TensorFlow 始终提供直接的生产途径。不管是在服务器、边缘设备还是网络上，TensorFlow 都可以助您轻松地训练和部署模型，无论您使用何种语言或平台。
如果您需要完整的生产环境机器学习流水线，请使用 TFX。如需在移动设备和边缘设备上进行推断，请使用 TensorFlow Lite。如需在 JavaScript 环境中训练和部署模型，请使用 TensorFlow.js。
强大的研究实验
构建和训练先进的模型，并且不会降低速度或性能。借助 Keras Functional API 和 Model Subclassing API 等功能，TensorFlow 可以助您灵活地创建复杂拓扑并实现相关控制。为了轻松地设计原型并快速进行调试，请使用即刻执行环境。
TensorFlow 还支持强大的附加库和模型生态系统以供您开展实验，包括 Ragged Tensors、TensorFlow Probability、Tensor2Tensor 和 BERT。
[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)"]]
