If we consider a simple model, here is what our network would look as follows:

This just means that a simple model learns in one big step. This may work fine for simple tasks, but for a highly complex tasks such as computer vision or image recognition, this is not enough. Complex tasks require a lot of manual engineering to achieve good precision. To do this, we add a lot of other layers of neurons that enable the network to learn step by step, instead of taking one huge leap to the output. The network should look as follows:

The first layer may learn low-level features such as horizontal lines, vertical lines, or diagonal lines, then it passes this knowledge to the second layer, which learns to detect shapes, then the third layer learns color and shapes, and detects more complex things such as faces and so on. By the fourth and the fifth layer, we may be able to detect really high-level features such as humans, cars, trees, or animals.

Another advantage of deep networks is to picture the output as the function of the input. In a simple model, we have the output that is the indirect function of the input. Here, we can see the output actually is the function of the fifth-layer weights. Then the fifth-layer weights are a function of the fourth layer, and the fourth layer is a function of the third layer, and so on. In this way, we actually learn really highly complex functions compared to a simple model.

So that's it for this section. The next section will be about organizing your data and applications, and at the same time, we will look at a highly efficient computational model for neural networks.