Introduction to TensorFlow

TensorFlow makes it easy for beginners and experts to create machine learning models for desktop, mobile, web, and cloud. See the sections below to get started.

TensorFlow

Learn the foundation of TensorFlow with tutorials for beginners and experts to help you create your next machine learning project.

For JavaScript

Use TensorFlow.js to create new machine learning models and deploy existing models with JavaScript.

For Mobile & IoT

Run inference with TensorFlow Lite on mobile and embedded devices like Android, iOS, Edge TPU, and Raspberry Pi.

For Production

Deploy a production-ready ML pipeline for training and inference using TensorFlow Extended (TFX).

Swift for TensorFlow

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

TensorFlow ecosystem

TensorFlow provides a collection of workflows to develop and train models using Python, JavaScript, or Swift, and to easily deploy in the cloud, on-prem, in the browser, or on-device no matter what language you use.

Load & preprocess data
Build, train & reuse models
Deploy
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.
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 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.
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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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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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Product Roadmap and RFCs

Explore the priorities, focus areas, and expected functionality in the upcoming releases of TensorFlow. Review upcoming RFCs (request for comments) for technical deep dives and to participate in design decisions. Many of these areas are driven by community use cases, and we welcome further contributions.

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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