Summary

Building a small neural network from scratch provides a practical view of the elementary properties of a neuron. We saw that a neuron requires an input that can contain many variables. Then, weights are applied to the values with biases. An activation function then transforms the result and produces an output.

Neuronal networks, even one- or two-layer networks, can provide real-life solutions in a corporate environment. The real-life business case was implemented using complex theory broken down into small functions. Then, these components were assembled to be as minimal and profitable as possible.

Customers expect quick-win solutions. Artificial intelligence provides a large variety of tools that satisfy that goal. When solving a problem for a customer, do not look for the best theory, but the simplest and fastest way to implement a profitable solution no matter how unconventional it seems.

In this case, an enhanced perceptron solved a complex business problem. In the next chapter, an FNN will be introduced using TensorFlow.