Neuronivõrk

Neuronivõrk, often referred to as a neural network in English, is a computational model inspired by the structure and function of biological neural networks found in the brain. It consists of interconnected processing units called artificial neurons, organized in layers. These neurons receive input signals, process them, and transmit output signals to other neurons. The connections between neurons have associated weights, which are adjusted during a learning process.

The primary purpose of a neuronivõrk is to learn patterns and relationships from data. This learning typically occurs through a process called training, where the network is fed a large dataset, and its weights are modified to minimize errors in its predictions or classifications. Common learning algorithms include backpropagation, which uses the error gradient to update weights iteratively.

Neuronivõrgud are a core component of machine learning and artificial intelligence. They are capable of performing a wide range of tasks, including image recognition, natural language processing, speech synthesis, and predictive analysis. Different architectures of neuronivõrgud exist, such as feedforward networks, convolutional neural networks (CNNs) for image processing, and recurrent neural networks (RNNs) for sequential data like text. The complexity and effectiveness of a neuronivõrk depend on its architecture, the number of neurons and layers, and the quality and quantity of training data.