The following examples are proof-of-concepts of how to apply auto-encoders to identify anomalies. Specific tuning and advanced design considerations are out of the scope for this chapter. We will take for granted some results from the literature without going into too much theoretical ground, which has already been covered in previous chapters.

We recommend the reader to carefully read Chapter 4, Unsupervised Feature Learning and the corresponding sections regarding auto-encoders.

We will use a Jupyter notebook for our examples.

Alternatively, we could have used H2O Flow (http://www.h2o.ai/product/flow/), which is a notebook-style UI for H2O pretty much like Jupyter, but we did not want to confuse the reader throughout the book.

We also ...

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