Neuronivõrke

Neuronivõrke, also known as artificial neural networks or ANNs, are a class of machine learning algorithms inspired by the structure and function of biological neural networks in the human brain. They are composed of interconnected nodes, or artificial neurons, organized in layers. Each connection between neurons has a weight, which determines the strength of the signal transmitted.

The process of learning in a neuronivõrk involves adjusting these weights based on training data. During training, the network is presented with input data and its corresponding output. The network's output is compared to the desired output, and the difference, known as the error, is used to update the weights through a process called backpropagation. This iterative refinement allows the network to learn complex patterns and relationships within the data.

Neuronivõrke are used in a wide range of applications, including image and speech recognition, natural language processing, medical diagnosis, and financial forecasting. Their ability to learn from data and make predictions makes them powerful tools for solving complex problems that are difficult for traditional algorithms to handle. Different architectures of neuronivõrke exist, such as feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), each suited for specific types of tasks. CNNs are particularly effective for image processing, while RNNs are well-suited for sequential data like text and time series. The development and application of neuronivõrke continue to be a rapidly advancing field in artificial intelligence.