convolutional neural networks the second idea to consider is that learning the filters driving the convolution operation isin senserepresentation learning for instancethe learned filters might learn to detect edgesshapesetc the important point to consider here is that we are not manually describing the features to be extracted from the input dataratherwe are describing an architecture that learns to engineer the features/representations the third idea to consider is the location invariance introduced by the pooling operation the pooling operation separates the location of the feature from the fact that it is detected filter that detects straight lines might detect this feature in any portion of the imagebut the pooling operation picks the fact that the feature is detected (max poolingthe fourth idea is that of hierarchy cnn can have multiple convolutional and pooling layers stacked upfollowed by fully connected network this allows the cnn to build hierarchy of concepts wherein more abstract concepts are based on simpler concepts (refer to the final idea relates to the presence of fully connected layer at the end of series of convolutional and pooling layers the series of convolutional and pooling layers generates the featuresand standard neural network learns the final classification/regression function it is important to distinguish this aspect of the cnn from traditional machine learning in traditional machine learningan expert would hand-engineer features and feed them to neural network in cnnsthese featuresrepresentations are being learned from data