Närvivõrgu

Närvivõrgu, also known as a neural network, is a computational model inspired by the structure and function of biological neural networks. It consists of interconnected nodes, or neurons, organized in layers. These neurons process and transmit information, learning to identify patterns and make decisions through a process of training.

The basic unit of a neural network is the artificial neuron, which receives inputs, performs a calculation on them, and produces an output. These inputs are typically weighted, meaning that some connections have a stronger influence on the neuron's output than others. The weights are adjusted during the training phase.

Neural networks are broadly categorized into several types, including feedforward neural networks, where information flows in one direction, and recurrent neural networks (RNNs), which have feedback loops allowing them to process sequential data. Convolutional neural networks (CNNs) are particularly effective for image and video analysis.

Training a neural network involves presenting it with a large dataset and adjusting the weights of the connections to minimize errors in its predictions. This iterative process allows the network to learn complex relationships within the data. Once trained, neural networks can be used for a wide range of applications, such as image recognition, natural language processing, and forecasting. Their ability to learn from data without explicit programming makes them a powerful tool in artificial intelligence and machine learning.