The most common type is called the multi-layer perceptron model. This neural network model consists of neurons represented by circles, as shown in the following diagram. These neurons are organized into layers:
Every multi-layer perceptron model will have at least three layers:
- Input Layer: This layer consists of all the predictors in our data.
- Output Layer: This will consist of the outcome variable, which is also known as the dependent variable or target variable.
- Hidden Layer: This layer is where you maximize the power of a neural network. Non-linear relationships can also be created in this layer, and all the complex interactions are carried out here. You can have many such hidden layers.
You will also notice in the preceding diagram that every neuron in a layer is connected to every neuron in the next layer. This forms connections, and every connecting line will have a weight associated with it. These weights will form different equations in the model.