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TensorFlow の概要

TensorFlow を利用すると、パソコン、モバイル、ウェブ、およびクラウドで使える機械学習モデルを、エキスパートはもちろん初心者でも簡単に作成できます。まずは以下の各セクションをご覧ください。

TensorFlow

初心者向けおよびエキスパート向けのチュートリアルで TensorFlow の基礎を学び、新たな機械学習プロジェクトの構築に役立てましょう。

JavaScript 向け

TensorFlow.js を使用して新しい機械学習モデルを作成し、JavaScript で既存のモデルをデプロイします。

モバイルおよび IoT 向け

Android、iOS、Edge TPU、Raspberry Pi などのモバイル デバイスや組み込みデバイスで、TensorFlow Lite を使用して推論を実行します。

本番環境向け

TensorFlow Extended(TFX)を使用してトレーニングと推論を行う、本番環境に対応した ML パイプラインをデプロイします。

Swift for TensorFlow

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

TensorFlow エコシステム

TensorFlow には、Python、JavaScript、Swift を使ってモデルの開発およびトレーニングができるさまざまなワークフローが用意されています。使用する言語を問わず、クラウドで、オンプレミスで、ブラウザで、またはデバイス上で、モデルを簡単にデプロイできます。

Load & preprocess data
Build, train & reuse models
デプロイ
TensorFlow
Build TensorFlow Input Pipelines
The tf.data API enables you to build complex input pipelines from simple, reusable pieces.
Explore
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.
Explore
TensorFlow
Deploy using Python
Deploy on a mobile or edge device, in browser, or at scale using TensorFlow Serving.
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.
Explore
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.
Explore
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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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.
Explore
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.
Explore
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.
Explore
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.
Explore
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.
Explore
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.
Explore
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.
Explore

サービスのロードマップと RFC

TensorFlow の次期リリースにおける優先項目、重点分野、提供予定の機能などを公開しています。技術的な詳細の確認や設計上の決定への参加については、今後の RFC(Request for Comments)をご覧ください。これらの大部分はコミュニティのユースケースによって決まります。さらなるご参加をお待ちしています。

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