$[$Select all that apply$]$ Regarding Convolutional Neural Networks, the following is correct $($See image below for reference$).$The LeNet CNN architecture is made up of several layers that implement feature extraction and then clustering. The image is divided into receptive fields that feed into a convolutional layer, which then extracts features from the input image. The next step is pooling, which reduces the dimensionality of the extracted features $($through down$-$sampling$)$ while retaining the most important information $($typically$,$ through max pooling$).$ Another convolution and pooling step is then performed that feeds into a fully connected multilayer perception. The final output layer of this network is a set of nodes that identify features of the image $($in this case, a node per identified number$).$ Y You train the network by using back$-$propagation.