  Deep learning is a machine learning methodology using multi-layer neural
network. A multi-layer neural network can be regarded as a chain of complete
bipartite graphs. The nodes of the first partita is the input layer and the
last is the output layer. The edges of a bipartite graph function as weights
which are represented as a matrix. The values of i-th partita are computed by
multiplication of the weight matrix and values of (i-1)-th partita. Using mass
training and teacher data, the weight parameters are estimated little by
little. Overfitting (or Overlearning) refers to a model that models the
`training data` too well. It then becomes difficult for the model to generalize
to new data which were not in the training set. The most popular method to
avoid overfitting is called dropout. Dropout deletes a random sample of
activations (nodes) to zero during the training process. A random sample of
nodes causes more irregular frequency of dropout edges. There is a similar
sampling concept in the area of design of experiments. We propose a
combinatorial design on dropout nodes from each partita which balances the
frequency of edges. We analyze and construct such designs in this paper.
