TensorFlow
Skip to main content
Install
Learn
Introduction
New to TensorFlow?
Tutorials
Learn how to use TensorFlow with end-to-end examples
Guide
Learn framework concepts and components
Learn ML
Educational resources to master your path with TensorFlow
API
TensorFlow (v2.16.1)
Versions…
TensorFlow.js
TensorFlow Lite
TFX
Ecosystem
LIBRARIES
TensorFlow.js
Develop web ML applications in JavaScript
TensorFlow Lite
Deploy ML on mobile, microcontrollers and other edge devices
TFX
Build production ML pipelines
All libraries
Create advanced models and extend TensorFlow
RESOURCES
Models & datasets
Pre-trained models and datasets built by Google and the community
Tools
Tools to support and accelerate TensorFlow workflows
Responsible AI
Resources for every stage of the ML workflow
Recommendation systems
Build recommendation systems with open source tools
Community
Groups
User groups, interest groups and mailing lists
Contribute
Guide for contributing to code and documentation
Blog
Stay up to date with all things TensorFlow
Forum
Discussion platform for the TensorFlow community
Why TensorFlow
About
Case studies
/
English
Español – América Latina
Français
Português – Brasil
中文 – 简体
中文 – 繁體
日本語
한국어
GitHub
Sign in
Install
Learn
More
API
More
Ecosystem
More
Community
More
Why TensorFlow
More
GitHub
Introduction
Tutorials
Guide
Learn ML
TensorFlow (v2.16.1)
Versions…
TensorFlow.js
TensorFlow Lite
TFX
LIBRARIES
TensorFlow.js
TensorFlow Lite
TFX
All libraries
RESOURCES
Models & datasets
Tools
Responsible AI
Recommendation systems
Groups
Contribute
Blog
Forum
About
Case studies
TensorFlow
Stay organized with collections
Save and categorize content based on your preferences.
An end-to-end platform for machine learning
Install TensorFlow
Get started with TensorFlow
TensorFlow makes it easy to create ML models that can run in any environment.
Learn how to use the intuitive APIs through interactive code samples.
View tutorials
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
Run quickstart
Solve real-world problems with ML
Explore examples of how TensorFlow is used to advance research and build AI-powered applications.
TENSORFLOW.JS
Catch up on the latest from the Web AI Summit
Explore the latest advancements in running models client-side with speakers from Chrome, MediaPipe, Intel, Hugging Face, Microsoft, LangChain, and more.
Watch now
close
TensorFlow GNN
Analyze relational data using graph neural networks
GNNs can process complex relationships between objects, making them a powerful technique for traffic forecasting, medical discovery, and more.
Learn about TF GNN
TensorFlow Agents
Build recommendation systems with reinforcement learning
Learn how Spotify uses the TensorFlow ecosystem to design an extendable offline simulator and train RL Agents to generate playlists.
Read the blog
What's new in TensorFlow
Read the latest announcements from the TensorFlow team and community.
Explore the ecosystem
Discover production-tested tools to accelerate modeling, deployment, and other workflows.
Library
TensorFlow.js
Train and run models directly in the browser using JavaScript or Node.js.
Library
LiteRT
Deploy ML on mobile and edge devices such as Android, iOS, Raspberry Pi, and Edge TPU.
API
tf.data
Preprocess data and create input pipelines for ML models.
Library
TFX
Create production ML pipelines and implement MLOps best practices.
API
tf.keras
Create ML models with TensorFlow's high-level API.
Resource
Kaggle Models
Find pre-trained models ready for fine-tuning and deployment.
Resource
TensorFlow Datasets
Browse the collection of standard datasets for initial training and validation.
Tool
TensorBoard
Visualize and track development of ML models.
ML models & datasets
Pretrained models and ready-to-use datasets for image, text, audio, and video use cases.
Libraries & extensions
Packages for domain-specific applications and APIs for languages other than Python.
Developer tools
Tools to evaluate models, optimize performance, and productionize ML workflows.
Join the community
Collaborate, find support, and share your projects by joining interest groups or attending developer events.
Get involved
Learn ML
New to machine learning? Begin with TensorFlow's curated curriculums or browse the resource library of books, online courses, and videos.
Explore resources
Stay connected
Learn the latest in machine learning and TensorFlow by following our channels or signing up for the newsletter. View past newsletters in the archive.
Forum
X
YouTube
Linkedin
Forum
X
YouTube
Linkedin
Start building with TensorFlow
Install TensorFlow
Explore tutorials
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],[],[],[]]
Stay connected
Blog
Forum
GitHub
Twitter
YouTube
Support
Issue tracker
Release notes
Stack Overflow
Brand guidelines
Cite TensorFlow
Terms
Privacy
Manage cookies
Sign up for the TensorFlow newsletter
Subscribe
English
Español – América Latina
Français
Português – Brasil
中文 – 简体
中文 – 繁體
日本語
한국어