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A convolutional neural network (cnn) is a neural network where one or more of the layers employs a convolution as the function applied to the output of the previous layer. 12 you can use cnn on any data, but it's recommended to use cnn only on data that have spatial features (it might still work on data that doesn't have spatial features, see duttaa's comment below). What is your knowledge of rnns and cnns
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Do you know what an lstm is? Pooling), upsampling (deconvolution), and copy and crop operations. What will a host on an ethernet network do if it receives a frame with a unicast destination mac address that does not match its own mac address
It will discard the frame
It will forward the frame to the next host It will remove the frame from the media A cnn will learn to recognize patterns across space while rnn is useful for solving temporal data problems The concept of cnn itself is that you want to learn features from the spatial domain of the image which is xy dimension
So, you cannot change dimensions like you mentioned. A convolutional neural network (cnn) that does not have fully connected layers is called a fully convolutional network (fcn) See this answer for more info
