Note: The whole code for this article is freely available on GitHub

A few weeks ago I saw a talk about Skorch, a library that wraps a PyTorch neural network to use it as a Scikit-learn model.

That is amazing: I can take an existing product based on, say, a random forest, and replace only the model without refactoring anything else: the fit and predict functions have the usual interface. On the other hand, I can use the powerful tools offered by Scikit-learn, like the grid search for hyperparameters and make_pipeline to apply encoders.