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

# Fashion MNIST Case Study

> A case study on the Fashion MNIST dataset using TensorFlow/Keras for image classification.

The Fashion MNIST dataset is a good reference dataset for learning ML algorithms, replacing the original MNIST dataset that has near-perfect classification rates.

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

## Notebook Preview

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

**Key references**: (Zoph et al., 2017; Olston et al., 2017; Cheng et al., 2017; Abadi et al., 2016; Wang et al., 2017)

## References

* Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., et al. (2016). *TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems*.
* 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*.
* Olston, C., Fiedel, N., Gorovoy, K., Harmsen, J., Lao, L., et al. (2017). *TensorFlow-Serving: Flexible, High-Performance ML Serving*.
* Wang, J., Zhang, Z., Xie, C., Zhou, Y., Premachandran, V., et al. (2017). *Visual Concepts and Compositional Voting*.
* Zoph, B., Vasudevan, V., Shlens, J., Le, Q. (2017). *Learning Transferable Architectures for Scalable Image Recognition*.

***

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