Convolutional neural networks (CNNs)

As a result, you may send a photo to a deep neural network that has been trained to recognise dogs and cats and get an output that tells you whether the photo contains a dog or a cat. The network outputs the chance of the photo containing a dog or a cat (the two classes you trained it to identify) and the output sums to 100 per cent if the last network layer is a softmax layer. You get scores that you can interpret as probabilities of content belonging to each class, independently, when the last layer is a sigmoid-activated layer. The scores will not always add up to 100 per cent. Because its architecture outputs the entire image as being of a given class, a simple CNN can't duplicate the instances below.