CNNs are very similar to ordinary neural networks. As we have seen in the previous chapter, neural networks are made up of neurons that have learnable weights and biases. Each neuron still computes the weighted sum of its inputs using dot products, adds a bias term, and passes it through a nonlinear equation. The network will show just one differentiable score function that will be, from raw images at one end to the class scores at other end.
And they will also have a loss function such as the softmax, or SVM on the last layer. Moreover, all the techniques that we learned ti develop neural networks will be applicable.
But then what's different with ConvNets you may ask. So the main point to note is that the ConvNet architecture explicitly assumes that the inputs that are received are all images, this assumption actually helps us to encode other properties of the...
