Neurovõrkude

Neurovõrkude, often translated as neural networks, are a class of machine learning algorithms inspired by the structure and function of the human brain. They are composed of interconnected nodes, or artificial neurons, organized in layers. These layers typically include an input layer, one or more hidden layers, and an output layer. Each connection between neurons has an associated weight, which is adjusted during the training process to enable the network to learn from data.

The fundamental principle behind neural networks is their ability to recognize complex patterns and relationships within data that might be difficult or impossible for traditional algorithms to detect. During training, the network is fed a dataset, and its output is compared to the known correct output. This error signal is then used to adjust the weights of the connections through a process called backpropagation, gradually improving the network's accuracy.

Neural networks have found widespread applications across various fields. In image recognition, they can identify objects and faces. In natural language processing, they power translation services and chatbots. They are also used in recommendation systems, fraud detection, and scientific research, demonstrating their versatility and growing importance in artificial intelligence.