Neural Network – what is it?

Neural Network is a class of mathematical models built on the principle of interaction between artificial neurons that process input data through a sequence of layers and parameters (weights) adjusted during training. An artificial neural network performs nonlinear transformations of data using activation functions and is optimized by minimizing a loss function using gradient descent methods and backpropagation. Architectures may include fully connected networks, convolutional (CNN), recurrent (RNN), and transformer-based models, depending on the type of task and the structure of the data.

In applied scenarios, an artificial neural network is used for computer vision, natural language processing, speech recognition, time series forecasting, and generative tasks. Practical implementation involves data preparation, architecture selection, hyperparameter tuning, and the use of hardware acceleration (GPU/TPU) for training and inference. Model performance depends on network depth, the volume of training data, and the ability to generalize results without overfitting in production environments.