 This helps to exclude rare dependencies.Finally, data can be augmented via methods such as cropping and rotating such that smaller training sets can be increased in size to reduce the chances of overfitting.DNNs must consider many training parameters, such as the size (number of layers and number of units per layer), the learning rate, and initial weights.