Neurovõrke

Neurovõrke is a Finnish term that translates to "neural networks" in English. This refers to computational systems inspired by the biological neural networks that constitute animal brains. These networks are comprised of interconnected nodes, or artificial neurons, that process information. Each connection between neurons has a weight associated with it, which determines the strength of the signal transmitted between them.

Artificial neural networks learn by adjusting these connection weights based on the input data and the desired output. This learning process typically involves an algorithm that iteratively modifies the weights to minimize errors or achieve a specific goal. The architecture of a neural network can vary widely, with different numbers of layers, neurons per layer, and types of connections. Common architectures include feedforward networks, recurrent neural networks, and convolutional neural networks, each suited for different types of tasks.

Neurovõrke have found applications in a diverse range of fields, including pattern recognition, natural language processing, computer vision, and prediction tasks. Their ability to learn complex patterns from data makes them powerful tools for solving problems that are difficult to address with traditional algorithms. The ongoing research and development in this area continue to push the boundaries of what is possible with artificial intelligence.