Hermoverkostojen

Hermoverkostojen, often translated as neural networks, are a fundamental concept in artificial intelligence and machine learning. They are computational models inspired by the structure and function of biological neural networks found in animal brains. These networks consist of interconnected nodes, or artificial neurons, organized in layers.

The basic unit of a neural network is the artificial neuron, which receives input signals, processes them through a mathematical function, and transmits an output signal. These inputs are typically weighted, meaning that some connections have a stronger influence on the neuron's output than others. The weights are adjusted during a learning process to enable the network to perform specific tasks.

Neural networks typically have an input layer, one or more hidden layers, and an output layer. The input layer receives the raw data, such as pixels in an image or words in a sentence. The hidden layers perform complex computations and feature extraction from the input data. The output layer produces the final result, such as a classification label or a predicted value.

The learning process in hermoverkostojen is achieved through algorithms like backpropagation, where errors in the output are used to adjust the weights of the connections. This iterative process allows the network to gradually improve its performance on a given task by minimizing prediction errors. Hermoverkostojen are widely used in applications such as image recognition, natural language processing, and forecasting.