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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. So, you cannot change dimensions like you mentioned. 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 The concept of cnn itself is that you want to learn features from the spatial domain of the image which is xy dimension 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 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).
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. Cisco ccna v7 exam answers full questions activities from netacad with ccna1 v7.0 (itn), ccna2 v7.0 (srwe), ccna3 v7.02 (ensa) 2024 2025 version 7.02
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