Neural network

Neural networks or Artificial Neural Networks are complex computing systems 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 taking input data, processing it at multiple levels using adjustable weights (customizable during the training phase) and producing a predicted result.

Internal design
. 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 learns by adjusting the weights and biases 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

• Feed-forward neural networks (FNNs). Information in FNNs moves in only one direction – from the input layer through hidden layers to the output layer. They are widely used in pattern recognition tasks.
• Convolutional neural networks (CNNs). CNNs are designed to process grid-like data. They have convolutional 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 that form directed loops. This allows them to retain a kind of “memory” of previous input data. They are suitable for tasks with sequential data, such as speech recognition or time series prediction.
• Long Short-Term Memory (LSTM) networks. They are designed to memorize long-term dependencies in sequential data, which standard RNNs cannot cope with. They are often used in natural language processing tasks.
• Radial basis function networks (RBFNs). RBFNs have a single hidden layer of neurons whose activation is determined by the distance from the center of the neuron. They are widely used to approximate functions and solve control problems.
• Self-Organizing Maps (SOM). SOMs use unsupervised learning to create a low-dimensional representation of high-dimensional data, making them useful for visualizing complex data.
• Generative Adversarial Networks (GANs). GANs consist of two networks: a generator network, which creates new data instances, and a discriminator network, which tries to distinguish real instances from fake ones. Such networks are used for content creation.

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