neurovõrgustikku

Neurovõrgustikku, often translated as neural network, is a computational model inspired by the structure and function of biological neural networks in the brain. These networks consist of interconnected processing units called artificial neurons, which receive input, process it, and transmit output to other neurons. The connections between neurons have associated weights that are adjusted during a learning process.

The primary function of a neuro võrgustikku is to learn from data. This learning occurs through an iterative process where the network is presented with examples, and its weights are modified to minimize the difference between its predicted output and the actual desired output. Common learning algorithms include backpropagation, where errors are propagated backward through the network to update weights.

Neuro võrgustikku have found widespread applications in various fields. They are fundamental to machine learning and artificial intelligence, powering tasks such as image recognition, natural language processing, speech synthesis, and predictive modeling. Their ability to identify complex patterns and relationships in data makes them powerful tools for solving problems that are difficult for traditional algorithms. Different architectures, such as convolutional neural networks (CNNs) for image data and recurrent neural networks (RNNs) for sequential data, have been developed to optimize performance for specific types of tasks. The complexity and capabilities of neuro võrgustikku continue to evolve with ongoing research and advancements in computational power.