Neuraalvõrkude

Neuraalvõrkude, also known as artificial neural networks, are computational models inspired by the structure and function of biological neural networks in the human brain. They are a fundamental component of machine learning and artificial intelligence, designed to recognize patterns and make predictions from data.

The basic building block of a neural network is the artificial neuron, or node. These neurons are interconnected and organized into layers. Typically, there is an input layer, one or more hidden layers, and an output layer. Information enters through the input layer, is processed through the hidden layers, and the final result is produced by the output layer. Each connection between neurons has a weight associated with it, which determines the strength of the signal passed between them.

During the training process, a neural network learns by adjusting these weights based on input data and desired outputs. This adjustment is often achieved through algorithms like backpropagation, which calculates the error at the output layer and propagates it back through the network to update the weights. Through repeated exposure to data, the network refines its ability to perform tasks such as image recognition, natural language processing, and forecasting.

Different types of neural networks exist, each suited for specific applications. For instance, convolutional neural networks (CNNs) are excellent for image analysis, while recurrent neural networks (RNNs) are well-suited for sequential data like text or time series. The complexity and architecture of a neural network can vary greatly depending on the problem it is designed to solve.