Neuronaalvõrkudel

Neuronaalvõrkudel, also known as artificial neural networks (ANNs), are a class of machine learning algorithms inspired by the structure and function of biological neural networks in animal brains. They consist of interconnected nodes, or artificial neurons, organized in layers. These networks learn by processing data, adjusting the strength of connections between neurons, known as weights, to identify patterns and make predictions.

The basic unit of a neural network is the artificial neuron, which receives input signals, performs a computation, and produces an output signal. Inputs are multiplied by weights, summed, and then passed through an activation function to determine the neuron's output. Data flows through the network from an input layer, through one or more hidden layers, to an output layer. During the training process, the network is fed labeled data, and an algorithm like backpropagation is used to adjust the weights to minimize the difference between the network's predictions and the actual outcomes.

Neuronaalvõrkudel have demonstrated remarkable success in various fields. They are widely used for image and speech recognition, natural language processing, recommendation systems, and even in complex tasks like playing games and diagnosing medical conditions. Their ability to learn from vast amounts of data and uncover intricate relationships makes them a powerful tool in modern artificial intelligence. However, training these networks can be computationally intensive and often requires large datasets.