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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. Could using lstm and cnn together be better than predicting using lstm alone? A cnn will learn to recognize patterns across space while rnn is useful for solving temporal data problems
What is your knowledge of rnns and cnns But i don't know if it is better than what i predicted using lstm Do you know what an lstm is?
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 convolutional neural network (cnn) that does not have fully connected layers is called a fully convolutional network (fcn)
See this answer for more info Pooling), upsampling (deconvolution), and copy and crop operations. 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). 0 i am working on lstm and cnn to solve the time series prediction problem
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