neurovõrgustikke

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

The fundamental unit of a neurovõrgustik is the artificial neuron, or perceptron. These neurons are interconnected in layers, typically an input layer, one or more hidden layers, and an output layer. Each connection between neurons has an associated weight, which is adjusted during the training process. The input data is fed into the input layer, and as it passes through the hidden layers, transformations occur based on the weights and activation functions of the neurons. Finally, the output layer produces the network's prediction or result.

Training a neurovõrgustik involves presenting it with a large dataset of examples and their corresponding correct outputs. Through an iterative process, often using algorithms like backpropagation, the network adjusts its weights to minimize the error between its predictions and the actual outcomes. This learning process allows the network to generalize from the training data and make accurate predictions on new, unseen data.

Neurovõrgustikud have found applications in a wide range of fields, including image and speech recognition, natural language processing, medical diagnosis, financial forecasting, and autonomous systems. Their ability to learn complex patterns makes them powerful tools for solving problems that are difficult to address with traditional algorithmic approaches.