Neuronhálózati

Neuronhálózati, also known as neural network, is a computational model inspired by the structure and function of biological neural networks in the human brain. It is a subset of machine learning and is used for various tasks such as pattern recognition, classification, and prediction. Neural networks consist of interconnected nodes, or neurons, organized in layers. The first layer is the input layer, which receives data. The last layer is the output layer, which produces the final result. Between these, there can be one or more hidden layers, which process the input data through a series of mathematical operations.

The basic unit of a neural network is the neuron, which receives inputs, processes them, and produces an output. Each input is associated with a weight, which determines the influence of that input on the neuron's output. The neuron also has a bias, which allows it to shift the activation function to the left or right. The weighted sum of the inputs, plus the bias, is passed through an activation function, which introduces non-linearity into the model.

Neural networks learn by adjusting the weights and biases of the neurons based on the error between the predicted output and the actual output. This process is known as backpropagation. During training, the network is presented with a set of input-output pairs, and the weights and biases are updated iteratively to minimize the error.

Neural networks have been successfully applied to a wide range of problems, including image and speech recognition, natural language processing, and game playing. They have also been used in various fields such as healthcare, finance, and transportation. However, neural networks also have limitations, such as the need for large amounts of data and computational resources, and the lack of interpretability.