How does an ANN learn?

Based on the concept of biological neurons, the term and the idea of ANs arose. Similarly to biological neurons, the artificial neuron consists of the following:

• One or more incoming connections that aggregate signals from neurons
• One or more output connections for carrying the signal to the other neurons
• An activation function, which determines the numerical value of the output signal

The learning process of a neural network is configured as an iterative process of optimization of the weights (see more in the next section). The weights are updated in each epoch. Once the training starts, the aim is to generate predictions by minimizing the loss function. The performance of the network is then evaluated on the test set.

Now we know the simple concept of an artificial neuron. However, generating only some artificial signals is not enough to learn a complex task. Albeit, a commonly used supervised learning algorithm is the backpropagation algorithm, which is very commonly used to train a complex ANN.