Neural network - definition

A neural network is a mathematical model inspired by the biological structure of the brain, used in the fields of artificial intelligence and machine learning for analysis, classification, prediction and data processing. Neural networks consist of interconnected artificial neurons that process input signals and pass the information to subsequent layers in the network structure. Each neuron performs computations based on the weights assigned to the signals, summing them and transforming them using an activation function, making it possible to model complex non-linear relationships.

The structure of a typical neural network includes an input layer, hidden layers and an output layer. The depth of the network, or the number of hidden layers, determines its ability to represent complex relationships in the data. In deep learning networks, multiple such layers are used, allowing automatic feature extraction and the creation of hierarchical representations of information. The learning process involves modifying the synaptic weights to minimise the error between the predicted and actual outcome, usually using a backpropagation algorithm and gradient descent optimisation.

Neural networks are used in a wide range of practical applications: from image and speech recognition and natural language translation to recommendation systems, medical diagnostics, financial data analysis and autonomous control. Their flexibility and adaptability make them an effective tool for tasks requiring pattern recognition and analysis of data with high complexity or incomplete structure.

There are many types of neural networks, varying in architecture and purpose, such as convolutional networks (CNNs) used in image processing, recurrent networks (RNNs) for sequential data, or generative networks (GANs) for creating new data. Modern neural networks run on computing platforms that support GPU/TPU acceleration, allowing them to be trained on large datasets in a reasonable amount of time.

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