s technique we were able to reach a success rate of 0.88 on the 70k-element test set, close enough to our initial target.

We added serialization (`to_string`) and deserialization (constructor) functions to our library and built the resulting classifier into our product. The nice property of feed-forward neural networks is that classification is really fast once the network is loaded in memory. Adding the classifier to our infrastructure had no visible impact on the load of our aggregation servers.

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