MACHINE LEARNING

Detailed explanation-1: -Dropout is a regularization method approximating concurrent training of many neural networks with various designs. During training, some layer outputs are ignored or dropped at random. This makes the layer appear and is regarded as having a different number of nodes and connectedness to the preceding layer.

Detailed explanation-2: -Dropout is a regularization technique 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.

Detailed explanation-3: -There are three main regularization techniques, namely: Ridge Regression (L2 Norm) Lasso (L1 Norm) Dropout.