TensorFlow is **an end-to-end platform for machine learning**.

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.

## Solve real-world problems with ML

Explore examples of how TensorFlow is used to advance research and build AI-powered applications.

## Explore the ecosystem

Discover production-tested tools to accelerate modeling, deployment, and other workflows.

- **TensorFlow.js**: Train and run models directly in the browser using JavaScript or Node.js.

- **LiteRT**: Deploy ML on mobile and edge devices such as Android, iOS, Raspberry Pi, and Edge TPU.

- **tf.data**: Preprocess data and create input pipelines for ML models.

- **TFX**: Create production ML pipelines and implement MLOps best practices.

- **tf.keras**: Create ML models with TensorFlow's high-level API.

- **Kaggle Models**: Find pre-trained models ready for fine-tuning and deployment.

- **TensorFlow Datasets**: Browse the collection of standard datasets for initial training and validation.

- **TensorBoard**: Visualize and track development of ML models.

## Resources

Pretrained models and ready-to-use datasets for image, text, audio, and video use cases. Packages for domain-specific applications and APIs for languages other than Python. Tools to evaluate models, optimize performance, and productionize ML workflows.

## Community and learning

New to machine learning? Begin with TensorFlow's curated curriculums or browse the resource library of books, online courses, and videos. Collaborate, find support, and share your projects by joining interest groups or attending developer events.