An Introduction to neural networks

The neural networks are a part of machine learning, inspired in the human brain functioning. One neural network is composed by a group of interconnected neurons between them through links.

The union of all these interconnected neurons constitute an artificial neural network.


Each neuron takes the inputs of the neurons of the ancestor layers as inputs, each of these inputs is multiplied by a weight, partial results are added and through an activation function the output is measured. This output is, at the same time, the input of the neuron which predicts.

These neural networks are no other thing than massively interconnected networks in parallel with simple elements and with hierarchical organisation, which try to interact with objects of the real world in the same way does the biological nervous system.

Is pointed that a neural network is a group of interconnected neurons working together, but in order to understand their functioning is very recommendable having clear all the concretes associates to each neuron.


Elements that compose a neuron



Entrance group x1,…xn

Represent the inputs of the neural network.


Synaptic Weights w1,…wn

Each input has a weight that goes automatically adjusted as the neural network learns


Add Function, Σ

Makes all the summation from all the outputs weighted by their weight.


Activation Function, F

Oversees maintain the group of outputs values in certain ranges, usually (0,1) or (-1,1)

It exists different activation functions that meet this objective, the most common one in the Sigmoide function.


Output, Y

Represent the resultant value after going thought the neural network.


Classification of neuronal networks according to their typology 

The neural networks are classified by topology, depending on the characteristics they have:








Written by: Diego Calvo, part of Idiwork’s team

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