person Author: Process Fellows
Neural networks are designed to imitate how the human brain processes information. They consist of many small processing units called neurons, which are connected in layers. Each neuron takes in several inputs, processes them, and produces one output. The connections between neurons have weights that indicate how important each input is.
During training, the network is given known examples and compares its output to the correct answer. The difference (error) is used to adjust the weights — strengthening the connections that lead to correct results and weakening those that cause mistakes. Over time, this process allows the network to learn how to analyze and make decisions for complex problems.