The idea of neural network algorithm came as an attempt to mimic the brain and its amazing ability to learn. Although it is a relatively old idea (emerged around the years 80-90), nowadays neural networks is considered the state of the art in many applications.
Neural network algorithms are based on the hypothesis that the brain has only one algorithm that can learn all features of the body, i.e., any area of the brain could learn to see or hear if it received the appropriate stimulus.
In the brain, each neuron receives nerve impulses through dendrites, performs some "calculation" in cell body and transmits the response via another nerve impulse using the axon. A neural network algorithm copies this system, as shown in Figures 1 and 2 below.
Figures 1 and 2 - representation of a neuron (left) and a neural network unit (right).
In this type of algorithm, several "neurons" are interconnected to form a network. This network consists of three types of layers, known as input layer, output layer and hidden layers, as shown in Figure 3 below.
Figure 3 - representation of a neural network.
The input layer receives the data and the output layers outputs the response. The hidden layers are responsible for some intermediate calculation that helps the network to find the final answer. In more complex networks, one can use multiple hidden layers between the input and the output layers. The number of neurons in each layer depends on the amount of input data and the type of problem being solved. For example, if the algorithm was designed to determine whether or not a patient has a specific disease, the input layer has as many neurons as the number of features in the input data and the output layer has only one neuron.Read more...