The TensorFlow tutorials are written as Jupyter notebooks and run directly in Google Colab—a hosted notebook environment that requires no setup. At the top of each tutorial, you'll see a Run in Google Colab button. Click the button to open the notebook and run the code yourself.

The best place to start is with the user-friendly Keras sequential API. Build models by plugging together building blocks. After these tutorials, read the Keras guide.
This "Hello, World!" notebook shows the Keras Sequential API and
This notebook collection demonstrates basic machine learning tasks using Keras.
These tutorials use to load various data formats and build input pipelines.
The Keras functional and subclassing APIs provide a define-by-run interface for customization and advanced research. Build your model, then write the forward and backward pass. Create custom layers, activations, and training loops.
This "Hello, World!" notebook uses the Keras subclassing API and a custom training loop.
This notebook collection shows how to build custom layers and training loops in TensorFlow.
Distribute your model training across multiple GPUs, multiple machines or TPUs.
The Advanced section has many instructive notebooks examples, including Neural machine translation, Transformers, and CycleGAN.
Check out these videos for an introduction to machine learning with TensorFlow:
Explore libraries to build advanced models or methods using TensorFlow, and access domain-specific application packages that extend TensorFlow. This is a sample of the tutorials available for these projects.
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