Neuroniverkot

Neuroniverkot are computational models inspired by the brain’s network of neurons. They consist of layers of artificial neurons connected by weighted links and are used to learn patterns from data. Through exposure to examples, they can classify inputs, predict continuous values, or generate new content.

In Finnish, neuroniverkot is the standard term for neural networks. The concept has been widely adopted in education, research, and industry as a foundational tool in artificial intelligence.

A basic neuron computes a weighted sum of its inputs, adds a bias, and passes the result through an activation function. Networks are trained by adjusting weights to minimize a loss function, typically using gradient-based methods such as backpropagation.

Common architectures include feedforward networks, recurrent networks for sequence data, and convolutional networks for spatially structured inputs. More recent developments such as transformer-based models describe a broader class of neural networks that process data with attention mechanisms.

Applications span image and speech recognition, natural language processing, time-series analysis, and robotics. In science, neural networks serve tasks from drug discovery to climate modeling.

Limitations include data requirements, risk of overfitting, and limited interpretability. Researchers address these issues with regularization, explainable AI methods, and advances in unsupervised and self-supervised learning, as well as specialized hardware to reduce energy use.

See also neural network, deep learning, backpropagation, artificial intelligence.