> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Hyperparameter Optimization Workshop

> Hands-on workshop for hyperparameter optimization using Keras Tuner.

In this section we will go through a hyperparameter optimization scheme based on grid-search that is seamlessly integrated in Keras.

<Note>
  In production environments where training rigs are used either in the cloud or internally, you should use a cloud native platform like [Katib](https://github.com/kubeflow/katib) or other Kubernetes-compatible solutions.
</Note>

<Card title="Run the Tutorial" icon="play" href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/keras_tuner.ipynb">
  Open the official TensorFlow Keras Tuner tutorial in Google Colab to execute the code interactively.
</Card>

## Notebook Preview

<Frame caption="Keras Tuner Tutorial - Hyperparameter Optimization (read-only preview via nbviewer)">
  <iframe src="https://nbviewer.jupyter.org/github/tensorflow/docs/blob/master/site/en/tutorials/keras/keras_tuner.ipynb" title="Keras Tuner Hyperparameter Optimization Tutorial" className="w-full rounded-lg border border-gray-200 dark:border-gray-700" style={{height: "800px"}} />
</Frame>

**Key references**: (Bengio, 2012; Golovin et al., n.d.; Cheng et al., 2017; Or et al., 2025; Bottou et al., 2016)

## References

* Bengio, Y. (2012). *Practical recommendations for gradient-based training of deep architectures*.
* Bottou, L., Curtis, F., Nocedal, J. (2016). *Optimization Methods for Large-Scale Machine Learning*.
* Cheng, H., Haque, Z., Hong, L., Ispir, M., Mewald, C., et al. (2017). *TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks*.
* Golovin, D., Solnik, B., Moitra, S., Kochanski, G., Karro, J., et al. (n.d.). *Google Vizier: A Service for Black-Box Optimization*.
* Or, A., Jain, A., Vega-Myhre, D., Cai, J., Hernandez, C., et al. (2025). *TorchAO: PyTorch-native training-to-serving model optimization*.

***

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