neuralvõrgu

Neural võrk, also known as artificial neural network (ANN), is a computational model inspired by the structure and function of biological neural networks found in the brain. It consists of interconnected nodes, or neurons, organized in layers. These neurons process and transmit information, learning to perform tasks by adjusting the strength of connections between them.

The basic unit of a neural võrk is the artificial neuron, which receives inputs, performs a calculation, and produces an output. Inputs are typically numerical values. Each input is multiplied by a weight, which represents the importance of that input. These weighted inputs are then summed, and an activation function is applied to the sum. The activation function determines whether the neuron should be activated and what its output should be.

Neural võrgud are typically organized into an input layer, one or more hidden layers, and an output layer. The input layer receives the raw data. The hidden layers perform intermediate computations, extracting features and patterns from the data. The output layer produces the final result, which could be a classification, a prediction, or another form of output depending on the task.

Training a neural võrk involves adjusting the weights and biases of the neurons to minimize the difference between the network's predictions and the actual target values. This is usually done using an algorithm called backpropagation, which propagates error signals backward through the network to update the weights. Neural võrgud are widely used in various applications, including image recognition, natural language processing, and machine learning.