Let's go through one example that illustrates why \(ResNets \) work so well, at least in the sense of how we can make them deeper and deeper without really hurting our ability to get them to do well on the training set. Hopefully, doing well on the training set is usually a prerequisite to doing well on the test set. So, being able to at least train \(ResNet \) to do well on the training set is a good first step towards that. In the last post we saw that if we make a network deeper, it can decrease our ability to train the network well on the training set. Therefore, we sometimes avoid having too deep neural networks.