Fundamentals of CNNs

In this topic, we will see how CNNs work and explain the process of convolving an image.

We know that images are made up of pixels, and if the image is in RGB, for example, it will have three channels where each letter/color (Red-Green-Blue) has its own channel with a set of pixels of the same size. Fully-connected neural networks do not represent this depth in an image in every layer. Instead, they have a single dimension to represent this depth, which is not enough. Furthermore, they connect every single neuron of one layer to every single neuron of the next layer, and so on. This in turn results in lower performance, meaning you would have to train a network for longer and would still not get good results.

CNNs are a category of neural networks that has ended up being very effective for tasks such as classification and image recognition. Although, they also work very well for sound and text data. CNNs consist of an input, hidden layers, and an output layer, just like...