ideghálók

Ideghálók, also known as neural networks, are a class of machine learning algorithms inspired by the structure and function of the human brain. They consist of interconnected nodes, or artificial neurons, organized in layers. The input layer receives raw data, which is then processed by one or more hidden layers before reaching the output layer, which produces the final result. Each connection between neurons has a weight, and during the training process, these weights are adjusted to minimize errors in the network's predictions.

The fundamental principle behind ideghálók is learning from data. They are trained on large datasets, identifying patterns and relationships that might be too complex for traditional algorithms to discern. This makes them particularly effective for tasks such as image recognition, natural language processing, and predictive modeling. Different types of ideghálók exist, including feedforward networks, convolutional neural networks (CNNs) for image processing, and recurrent neural networks (RNNs) for sequential data like text.

The development and application of ideghálók have led to significant advancements in artificial intelligence, enabling machines to perform tasks that were once considered exclusively human capabilities. Their ability to adapt and learn continuously makes them a powerful tool for solving complex problems across various scientific and industrial domains.