Discover everything about neural networks

In order to understand this subject more easily, we believe it is important to start with its definition and what it is all about:

What are neural networks?

Artificial neural networks are a model that was born inspired by the functioning of the human brain, which aims to emulate certain cognitive functions of living beings.

It is formed by a set of nodes, called artificial neurons, which are connected and transmit signals to each other, from the input to the generation of an output.

What is searched through neural networks?

The objective pursued through this model is learning, which automatically modifies itself so that complex tasks can be performed that could not be carried out through classical programming. That is why it is said that functions can be automated that at first could only be performed by people.

How do they work?

Networks receive a series of input values ​​and each of them reaches a node, called a neuron. The neurons of the network are grouped in layers that thus form the neural network, these have a numerical value with which it modifies the input that is received and the new values ​​obtained leave the neurons and continue on their way through the network.

Once they reach the end of the network, an output is obtained which will be the prediction calculated by the network. The more layers the network has and the more complex it is, the functions that can perform will also be.

How are neural networks trained?

To make it possible for a neural network to perform desired functions, it is necessary to undergo training, this is done by modifying the weights of its neurons so that it can extract the results that are sought.


Mainly training data must be entered into the network, depending on the result obtained, the weights of the neurons will be modified according to the error achieved and depending on how much each neuron has contributed to said result.

With backpropagation, it is possible for the network to learn through a model capable of obtaining quite successful results even with data very different from what has been used during its training.

Did you know that neural networks have been around since 1950? However, the low power of the equipment of that decade and the lack of algorithms that allowed networks to learn, led to their discontinuation. It was thanks to the creation of the Backpropagation algorithm that neural networks were able to resurface and to them, the appearance of Deep learning, the use of deep neural networks for complex tasks.

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