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

Run the Tutorial

Open the official TensorFlow tutorial and click “Run in Google Colab” to execute the code interactively.

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Notebook Preview

TensorFlow Keras Classification Tutorial - Fashion MNIST (read-only preview via nbviewer)

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

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References

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