Neuronivõrgud

Neuronivõrgud, also known as artificial neural networks (ANNs), are a class of machine learning algorithms inspired by the structure and function of biological neural networks in the human brain. They are designed to recognize patterns and relationships in data, enabling them to perform tasks such as classification, regression, and clustering.

The fundamental building block of a neuronivõrk is the artificial neuron, or node. These nodes are interconnected and process information in layers. Each connection between nodes has an associated weight, which determines the strength of the signal passed between them. During the learning process, these weights are adjusted to minimize errors and improve the network's performance.

A typical neuronivõrk consists of an input layer, one or more hidden layers, and an output layer. The input layer receives the raw data, which is then processed through the hidden layers. Each node in a hidden layer performs a weighted sum of its inputs and applies an activation function to produce an output. This output is then passed to the next layer. The output layer produces the final result of the network.

The training of a neuronivõrk involves feeding it a large dataset and adjusting the weights based on the difference between the network's predicted output and the actual desired output. This iterative process, often using algorithms like backpropagation, allows the network to learn from its mistakes and improve its accuracy over time.

Neuronivõrgud have found wide applications in various fields, including image recognition, natural language processing, speech synthesis, and financial forecasting, due to their ability to learn complex patterns from vast amounts of data.