A neural network or artificial neural network (Artificial Neural Network) is a complex computing system based on the neural structure of the human brain. They serve as the basis of machine learning, allowing computers to learn and interpret data. The process is based on receiving input data, processing it at several levels using adjustable weights (configurable at the training stage) and producing a predictable result.

Internal structure
Neural networks work on the principle of emulating the biological neurons that make up the human brain. Just as neurons transmit signals to other neurons through synapses, ANNs transmit data through a network of interconnected layers of nodes, or “artificial neurons.”

Each node applies a specific function to the input data and passes the result to the next layer. The network is trained by adjusting the weights and offsets of these nodes depending on the prediction error of the output data. This method is known as the back propagation method.

Types of neural networks

• Neural networks with direct communication (FNN). The information in FNN moves only in one direction — from the input layer through the hidden layers to the output layer. They are widely used in image recognition tasks.
• Convolutional neural networks (CNN). CNNs are designed to process grid-like data. They have convolution layers that apply filters to the input data. They are suitable for tasks such as image and video recognition.
• Recurrent neural networks (RNNs). RNNs have connections forming directed cycles. This allows them to keep a kind of “memory” of the previous input data. They are suitable for sequential data tasks such as speech recognition or time series forecasting.
• Networks with long short-term memory (LSTM). They are designed to store long-term dependencies in sequential data, which standard RNCs cannot cope with. They are often used in natural language processing tasks.
• Networks with radial basis functions (RBFN). RBFNS have one hidden layer of neurons, the activation of which is determined by the distance from the center of the neuron. They are widely used for approximating functions and solving control problems.
• Self-organizing maps (SOM). SOM uses unsupervised learning to create a low-dimensional representation of high-dimensional data, which makes them useful for visualizing complex data.
• Generative adversarial networks (GAN). GANS consist of two networks: a generator network that creates new instances of data, and a discriminator network that tries to distinguish real instances from fake ones. Such networks are used to create content.

Application of neural networks
Neural networks are actively used in various industries to solve a wide range of tasks. For example, pattern recognition in images and videos, personalization of recommendations in online services, automation of production process management, analysis of medical data for the diagnosis of diseases, development of self-driving cars, processing and translation of natural languages, as well as content creation.