Supervised learning is a learning method where there is a part of the training data which acts as a teacher to the algorithm to determine the model. The machine is taught what to learn from the target data. The target data, or dependent or response variables, are the outcome of the collective action of the independent variables. The network training is done with the target data and its behavior with patterns of input data. The target labels are known in advance and the data is fed to the algorithm to derive the model.

Most of neural network usage is done using supervised learning. The weights and biases are adjusted based on the output values. The output can be categorical (like true/false or 0/1/2) or continuous (like 1,2,3, and so on). The model is dependent on the type of output variables, and in the case of neural networks, the output layer is built on the type of target variable.

Supervised learning is depicted by the following diagram: