Convolutional Neural Networks

Abstract

The previously discussed architecture of ANNs is called FC neural networks (FCNNs). The reason is that each neuron in a layer i is connected to all neurons in layers i-1 and i+1. Each connection between two neurons has two parameters: the weight and the bias. Adding more layers and neurons increases the number of parameters. As a result, it is very time-consuming to train such networks even on devices on multiple graphics processing units (GPUs) and multiple central processing units (CPUs). It becomes impossible to train such networks on PCs with limited processing and memory capabilities.