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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. Here are a few more specific questions Fully convolution networks a fully convolution network (fcn) is a neural network that only performs convolution (and subsampling or upsampling) operations

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Equivalently, an fcn is a cnn without fully connected layers How do i handle such large image sizes without downsampling Convolution neural networks the typical convolution neural network (cnn) is not fully convolutional because it often contains fully connected layers too (which do not perform the.

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 Do you know what an lstm is? 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

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. Suppose that i have 10k images of sizes $2400 \\times 2400$ to train a cnn