Deep Learning

nn.Linear(m,n)                                # fully connected layer from
                                              # m to n units
nn.ConvXd(m,n,s)                              # X dimensional conv layer from
                                              # m to n channels where X⍷{1,2,3}
                                              # and the kernel size is s
nn.MaxPoolXd(s)                               # X dimension pooling layer
                                              # (notation as above)
nn.BatchNormXd                                # batch norm layer
nn.RNN/LSTM/GRU                               # recurrent layers
nn.Dropout(p=0.5, inplace=False)              # dropout layer for any dimensional input
nn.Dropout2d(p=0.5, inplace=False)            # 2-dimensional channel-wise dropout
nn.Embedding(num\_embeddings, embedding\_dim)   # (tensor-wise) mapping from
                                              # indices to embedding vectors