Model performance optimization
Since CNNs are different from the layering architecture's perspective, they have different requirements as well as tuning criteria. How do you know what combination of hyperparameters is the best for your task? Of course, you can use a grid search with cross-validation to find the right hyperparameters for linear machine learning models.
However, for CNNs, there are many hyperparameters to tune, and since training a neural network on a large dataset takes a lot of time, you will only be able to explore a tiny part of the hyperparameter space in a reasonable amount of time. Here are some insights that can be followed.
Number of hidden layers
For many problems, you can just begin with a single hidden layer and you will get reasonable results. It has actually been shown that an MLP with just one hidden layer can model even the most complex functions provided it has enough neurons. For a long time, these facts convinced researchers that there was no need to investigate...
