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- TensorFlow (v2.16.1)
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- 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.

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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.

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