better but it's going to be forced to have multiple channels through the network and build a more robust representation of its prediction now we just repeat this on every iteration so on the first iteration we dropped out one 50% of the nodes on the next iteration we can drop out a different randomly sampled 50% which may include some of the previously sampled nodes as well and this will allow the network to generalize better to new test data the second regularization technique that we'll talk about is the notion of early