Convolutional neural networks

CNNs, or ConvNets, are quite similar to regular neural networks. They are still made up of neurons with weights that can be learned from data. Each neuron receives some inputs and performs a dot product. They still have a loss function on the last fully connected layer. They can still use a nonlinearity function. All of the tips and techniques that we learned from the last chapter are still valid for CNN. As we saw in the previous chapter, a regular neural network receives input data as a single vector and passes through a series of hidden layers. Every hidden layer consists of a set of neurons, wherein every neuron is fully connected to all the other neurons in the previous layer. Within a single layer, each neuron is completely independent and they do not share any connections. The last fully connected layer, also called the output layer, contains class scores in the case of an image classification problem. Generally, there are three main layers in a simple...