Summary

It should be noted, as it may have become clear, that there is no general architecture for a convolutional neural network. However, there are general guidelines. Normally, pooling layers follow convolutional layers, and often it is customary to stack two or more successive convolutional layers to detect more complex features, as it is done in the VGG-16 neural net example shown earlier. Convolutional networks are very powerful. However, they can be quite resource-heavy (the VGG-16 example above, for example, is relatively complex), and usually require a long training time, which is why the use of GPU can help speed up performance. Their strength comes from the fact that they do not focus on the entire image, rather they focus on smaller sub-regions to find interesting features that make up the image in order to be able to find discriminating elements between different inputs. Since convolutional layers are very resource-heavy, we have introduced pooling layers that help reduce the...