Building datasets for deep learning

Compared to other predictive models that you might have used, deep neural networks are very complicated. Consider a network with 100 inputs, two hidden layers with 30 neurons each, and a logistic output layer. That network would have 3,930 learnable parameters as well as the hyperparameters needed for optimization, and that's a very small example. A large convolutional neural network will have hundreds of millions of learnable parameters. All these parameters are what make deep neural networks so amazing at learning structures and patterns. However, this also makes overfitting possible.