neuronavõrke

Neuronavõrke, often translated as neural networks, are a fundamental concept in the field of artificial intelligence and machine learning. They are computational models inspired by the structure and function of biological neural networks, such as the human brain. These networks are composed of interconnected nodes, or "neurons," that process and transmit information. Each connection between neurons has an associated weight, which determines the strength of the signal transmitted.

The learning process in a neuronavõrk involves adjusting these weights based on input data. When presented with a set of examples, the network attempts to produce an output. If the output is incorrect, the weights are modified through an algorithm, typically backpropagation, to minimize the error. This iterative process allows the neuronavõrk to learn patterns, relationships, and features within the data.

Neuronavõrke are capable of performing a wide range of tasks, including image recognition, natural language processing, prediction, and decision-making. Different architectures of neuronavõrke exist, such as feedforward networks, convolutional neural networks (CNNs) for image analysis, and recurrent neural networks (RNNs) for sequential data. Their ability to learn complex, non-linear relationships has made them a cornerstone of modern AI advancements.