透過集合功能整理內容
你可以依據偏好儲存及分類內容。
為何該選擇 TensorFlow
無論你是新手還是專家，TensorFlow 這個端對端平台都能讓你輕鬆建構及部署機器學習模型。
輕鬆建構模型
TensorFlow 提供了多個抽象層，讓你可以選擇適合自己的抽象層。請使用高階 Keras API 來建構並訓練模型，這個 API 能讓你更容易開始使用 TensorFlow 和機器學習。
如需更多彈性，Eager Execution 可讓你立即進行疊代，偵錯也相當符合直覺。如要進行大型的機器學習訓練工作，請使用 Distribution Strategy API，以便在不同硬體配置下進行分散式訓練，而無須變更模型定義。
所有機器學習的生產環境都很健全
TensorFlow 一向都提供直達生產環境的最短途徑。不管是在伺服器、邊緣裝置或是網路上，無論使用的是哪一種語言或平台，TensorFlow 都能讓你輕鬆訓練及部署模型。
如需生產環境下完整的機器學習管道，請使用 TFX。如要在行動裝置及邊緣裝置上執行推論，請使用 TensorFlow Lite。如要在 JavaScript 環境中訓練及部署模型，則請使用 TensorFlow.js。
強大的研究性實驗
建構及訓練最先進的模型，完全不必犧牲速度或效能。TensorFlow 具備 Keras Functional API 和 Model Subclassing API 等功能，可用於建立複雜的拓撲，讓你享有彈性及主控權。如要輕鬆設計出原型並快速進行偵錯，請使用 Eager Execution。
TensorFlow 也支援由強大的外掛程式庫及模型所組成的生態系統，一舉囊括 Ragged Tensors、TensorFlow Probability、Tensor2Tensor 和 BERT，可供你盡情進行實驗。
[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)"]]
