Dropout is a regularization technique patented by Google for reducing overfitting in neural networks by preventing complex co-adaptations on training data. It is a very efficient way of performing model averaging with neural networks. The term "dropout" refers to dropping out units (both hidden and visible) in a neural network. Dropout can be seen as an extreme form of bagging in which each model is trained on a single case and each parameter of the model is very strongly regularized by sharing it with the corresponding parameter in all the other models. Key points are as follows: Large weights in a neural network are a sign of a more complex network that has overfit the training data. Probabilistically dropping out nodes in the network is a simple and effective regularization method. A large network with more training and the use of a weight constraint are suggested when using dropout.