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# Research and experimentation
Eager execution provides an imperative, define-by-run interface for advanced
operations. Write custom layers, forward passes, and training loops with
auto differentiation. Start with these notebooks, then read the
[eager execution guide](../../guide/eager).
1. <span>[Eager execution](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/eager_basics.ipynb){:.external}</span>
2. <span>[Automatic differentiation and gradient tape](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb){:.external}</span>
3. <span>[Custom training: basics](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/custom_training.ipynb){:.external}</span>
4. <span>[Custom layers](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb){:.external}</span>
5. [Custom training: walkthrough](/tutorials/eager/custom_training_walkthrough)
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