CNN Vs. RNN: How Are They Different? - TechTarget

Convolutional neural networks

Computers interpret images as sets of color values distributed over a certain width and height. Thus, what humans see as shapes and objects on a computer screen appear as arrays of numbers to the machine.

CNNs make sense of this data through mechanisms called filters: small matrices of weights tuned to detect certain features in an image, such as colors, edges or textures. In the first layers of a CNN, known as convolutional layers, a filter is slid -- or convolved -- over the input, scanning for matches between the input and the filter pattern. This results in a new matrix indicating areas where the feature of interest was detected, known as a feature map.

In the next stage of the CNN, known as the pooling layer, these feature maps are cut down using a filter that identifies the maximum or average value in various regions of the image. Reducing the dimensions of the feature maps greatly decreases the size of the data representations, making the neural network much faster.

Finally, the resulting information is fed into the CNN's fully connected layer. This layer of the network takes into account all the features extracted in the convolutional and pooling layers, enabling the model to categorize new input images into various classes.

In a CNN, the series of filters effectively builds a network that understands more and more of the image with every passing layer. The filters in the initial layers detect low-level features, such as edges. In deeper layers, the filters begin to recognize more complex patterns, such as shapes and textures. Ultimately, this results in a model capable of recognizing entire objects, regardless of their location or orientation in the image.

Bias in artificial neurons

In both artificial and biological networks, when neurons process the input they receive, they decide whether the output should be passed on to the next layer as input. The decision of whether to send information on is called bias, and it's determined by an activation function built into the system. For example, an artificial neuron can only pass an output signal on to the next layer if its inputs -- which are actually voltages -- sum to a value above some particular threshold.