Tuning hyperparameters and advanced FFNNs
The flexibility of neural networks is also one of their main drawbacks: there are many hyperparameters to tweak. Even in a simple MLP, you can change the number of layers, the number of neurons per layer, and the type of activation function to use in each layer. You can also change the weight initialization logic, the drop out keep probability, and so on.
Additionally, some common problems in FFNNs, such as the gradient vanishing problem, and selecting the most suitable activation function, learning rate, and optimizer, are of prime importance.
Tuning FFNN hyperparameters
Hyperparameters are parameters that are not directly learned within estimators. It is possible and recommended that you search the hyperparameter space for the best cross-validation (http://scikit-learn.org/stable/modules/cross_validation.html#cross-validation) score. Any parameter provided when constructing an estimator may be optimized in this manner. Now, the question is: how you...
