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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). We use them for input sequences which are typically better handled by convolutional neural networks, such as a sequence of images.

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A cnn will learn to recognize patterns across space while rnn is useful for solving temporal data problems The task i want to do is autonomous driving using sequences of images. 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 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 Pooling), upsampling (deconvolution), and copy and crop operations. But if you have separate cnn to extract features, you can extract features for last 5 frames and then pass these features to rnn

And then you do cnn part for 6th frame and you pass the features from 2,3,4,5,6 frames to rnn which is better