Convolutional Layers vs Fully Connected Layers

The design of a Neural Network is quite a difficult thing to get your head around at first. Designing a neural network involves choosing many design features like the input and output sizes of each layer, where and when to apply batch normalization layers, dropout layers, what activation functions to use, etc. In this article, I want to discuss what is really going on behind fully connected layers and convolutions, and how the output size of convolutional layers can be calculated. Deep learning is a field of research that has skyrocketed in the past few years with the increase in computational power and advances in the architecture of models. Two kinds of networks you'll often hear when reading about deep learning are fully connected neural nets (FCNN), and convolutional neural nets (CNNs).