A neural network (artificial neural network) is a way of organizing individual computational elements, to a certain extent simulating the structure of the brain. Neural networks are used to solve problems such as pattern recognition, clustering (grouping into clusters), making predictions, compressing information and restoring damaged or "noisy" data.

A characteristic feature of a neural network is its trainability – the ability to find dependencies between input and output data that are offered to it during training. Due to the inherent parallelism, the neural network allows processing large amounts of information, as well as performing tasks, the algorithm for solving which it works out itself.

Depending on the type of network, the signal can only be sent to the input neurons; to all neurons simultaneously, and also transmitted between neurons of different levels (feedback). Synapses (connections between neurons) have different "weights", thanks to which a hierarchy of signals entering the neuron is built.

Research on the development of neural networks has been conducted since the 1940s. In the 1980s, deep learning algorithms appeared. This improvement of neural networks (the addition of several hidden levels, or layers, of neurons) allows you to speed up the work of the neural network when performing complex tasks.

A neural network (artificial neural network) is a way of organizing individual computational elements, to a certain extent simulating the structure of the brain. Neural networks are used to solve problems such as pattern recognition, clustering (grouping into clusters), making predictions, compressing information and restoring damaged or "noisy" data.

A characteristic feature of a neural network is its trainability – the ability to find dependencies between input and output data that are offered to it during training. Due to the inherent parallelism, the neural network allows processing large amounts of information, as well as performing tasks, the algorithm for solving which it works out itself.

Depending on the type of network, the signal can only be sent to the input neurons; to all neurons simultaneously, and also transmitted between neurons of different levels (feedback). Synapses (connections between neurons) have different "weights", thanks to which a hierarchy of signals entering the neuron is built.

Research on the development of neural networks has been conducted since the 1940s. In the 1980s, deep learning algorithms appeared. This improvement of neural networks (the addition of several hidden levels, or layers, of neurons) allows you to speed up the work of the neural network when performing complex tasks.