ideghálózatok

Ideghálózatok, also known as neural networks, are computational models inspired by the structure and function of biological nervous systems. They are a core component of artificial intelligence and machine learning, used for tasks such as pattern recognition, classification, and prediction. An ideghálózat consists of interconnected nodes, or neurons, organized in layers. The input layer receives raw data, which is then processed through one or more hidden layers. Finally, the output layer provides the result of the network's computation.

Each connection between neurons has a weight, which determines the strength of the signal passed between them. During the learning process, these weights are adjusted based on the data the network is trained on. This adjustment, often performed using algorithms like backpropagation, allows the network to learn complex relationships within the data. The activation function of each neuron determines whether and to what extent it will fire, passing its output to the next layer.

Ideghálózatok can vary in complexity, from simple perceptrons with a single layer to deep neural networks with many hidden layers. The "depth" of a network refers to the number of hidden layers. Deep learning, a subfield of machine learning, heavily relies on these deep ideghálózatok to tackle highly complex problems. Their ability to learn hierarchical representations of data has led to significant advancements in fields like computer vision, natural language processing, and speech recognition.