Neuronivõrgu

Neuronivõrgu, also known as a neural network, is a computational model inspired by the structure and function of biological neural networks. It consists of interconnected nodes, called artificial neurons, that process and transmit information. These neurons are organized in layers, typically an input layer, one or more hidden layers, and an output layer.

Each artificial neuron receives input signals, processes them using an activation function, and produces an output signal. The connections between neurons have associated weights, which are adjusted during a training process. This training involves feeding the network data and adjusting the weights to minimize the difference between the network's output and the desired output. Through this learning process, the neural network can identify patterns, make predictions, and perform complex tasks.

Neural networks are widely used in various fields, including image recognition, natural language processing, and medical diagnosis. Their ability to learn from data and adapt to new information makes them powerful tools for solving a broad range of problems. Different types of neural networks exist, such as feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), each designed for specific types of data and tasks. The complexity and capabilities of neural networks continue to evolve with advancements in algorithms and computational power.