## Neural networks and hyperparameter optimization

As the parameter space of neural networks and deep learning models is so wide, optimization is a hard task and computationally very expensive. A wrong neural network architecture can be a recipe for failure. These models can only be accurate if we apply the right parameters and choose the right architecture for our problem. Unfortunately, there are only a few applications that provide tuning methods. We found that the best parameter tuning method at the moment is **randomized search**, an algorithm that iterates over the parameter space at random sparing computational resources. The sknn library is really the only library that has this option. Let's walk through the parameter tuning methods with the following example based on the wine-quality dataset.

In this example, we first load the wine dataset. Than we apply transformation to the data, from where we tune our model based on chosen parameters. Note that this dataset has 13 features; we specify...