Deep Learning, Regularisation, Optimization Algorithms, Deep Neural Networks, Gradient Descent.
0.8 So, 20% of the total units (i.e. 10) will be randomly shut off. Different sets of hidden layers are dropped randomly in each training iteration. Note that dropout is only done at the time of training the model (not during the test phase). The reason for doing this is because: We don’t want our